2024

A computer vision approach to continuously monitor fatigue during resistance training. Albert, Justin Amadeus; Arnrich, Bert in Biomedical Signal Processing and Control (2024). 89 105701.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Monitoring fatigue during resistance training is essential to avoid injuries caused by overtraining. Fatigue can be comprehensively quantified by the external and internal load, where the external load is the work done by the athlete, and the internal load is the psychological and physiological response to the external load. This paper proposes a computer vision method to continuously monitor fatigue during resistance training by predicting external and internal parameters, namely the generated power and the rating of perceived exertion. We utilize the human pose estimation from two Microsoft Azure Kinect cameras to capture the movement of athletes while performing stationary exercises. Our method processes the obtained kinematic data, computes skeleton features to train traditional machine learning algorithms, and constructs feature maps to train convolutional neural network-based models to predict the load parameters. For evaluation, we recorded a dataset of 16 subjects who performed squat exercises on a Flywheel and rated their perceived exertion after each set. A measuring unit integrated into the Flywheel provided power readings for each repetition. The results show that our method achieves good results in predicting both parameters. Gradient Boosting Regression Trees best predicted perceived exertion with a mean absolute percentage error of 8.08% and a Spearman’s ρ=0.74. Multi-layer Perceptron performed best in predicting power with a mean absolute error of 23.13 Watts and ρ=0.79. Our findings show that our approach delivers promising external and internal load quantifications for fatigue, with great potential to provide external feedback to coaches or athletes.
@article{ALBERT2024105701, abstract = {Monitoring fatigue during resistance training is essential to avoid injuries caused by overtraining. Fatigue can be comprehensively quantified by the external and internal load, where the external load is the work done by the athlete, and the internal load is the psychological and physiological response to the external load. This paper proposes a computer vision method to continuously monitor fatigue during resistance training by predicting external and internal parameters, namely the generated power and the rating of perceived exertion. We utilize the human pose estimation from two Microsoft Azure Kinect cameras to capture the movement of athletes while performing stationary exercises. Our method processes the obtained kinematic data, computes skeleton features to train traditional machine learning algorithms, and constructs feature maps to train convolutional neural network-based models to predict the load parameters. For evaluation, we recorded a dataset of 16 subjects who performed squat exercises on a Flywheel and rated their perceived exertion after each set. A measuring unit integrated into the Flywheel provided power readings for each repetition. The results show that our method achieves good results in predicting both parameters. Gradient Boosting Regression Trees best predicted perceived exertion with a mean absolute percentage error of 8.08% and a Spearman’s ρ=0.74. Multi-layer Perceptron performed best in predicting power with a mean absolute error of 23.13 Watts and ρ=0.79. Our findings show that our approach delivers promising external and internal load quantifications for fatigue, with great potential to provide external feedback to coaches or athletes.}, author = {Albert, Justin Amadeus and Arnrich, Bert}, journal = {Biomedical Signal Processing and Control}, keywords = {justinalbert bertarnrich}, pages = 105701, title = {A computer vision approach to continuously monitor fatigue during resistance training}, volume = 89, year = 2024 }

Analyzing population-level trials as N-of-1 trials: An application to gait. Zhou, Lin; Schneider, Juliana; Arnrich, Bert; Konigorski, Stefan in Contemporary Clinical Trials Communications (2024). 101282.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Studying individual causal effects of health interventions is important whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. As an alternative method, we propose to re-analyze existing population-level studies as N-of-1 trials, and use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. As a reference to the N-of-1 trials approach, we first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian repeated-measures models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses revealed differences between participants. Baseline values of the gait parameters varied largely among all participants, and the effects of fatigue and cognitive task were also heterogeneous, with some individuals showing effects in opposite directions. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.
@article{zhou2024analyzing, abstract = {Studying individual causal effects of health interventions is important whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. As an alternative method, we propose to re-analyze existing population-level studies as N-of-1 trials, and use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. As a reference to the N-of-1 trials approach, we first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian repeated-measures models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses revealed differences between participants. Baseline values of the gait parameters varied largely among all participants, and the effects of fatigue and cognitive task were also heterogeneous, with some individuals showing effects in opposite directions. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.}, author = {Zhou, Lin and Schneider, Juliana and Arnrich, Bert and Konigorski, Stefan}, journal = {Contemporary Clinical Trials Communications}, keywords = {linzhou bertarnrich stefankonigorski julianaschneider}, pages = 101282, publisher = {Elsevier}, title = {Analyzing population-level trials as N-of-1 trials: An application to gait}, year = 2024 }

2023

Federated Learning for Activity Recognition: A System Level Perspective. Kalabakov, Stefan; Jovanovski, Borche; Denkovski, Daniel; Rakovic, Valentin; Pfitzner, Bjarne; Konak, Orhan; Arnrich, Bert; Gjoreski, Hristijan in IEEE Access (2023). 11 64442–64457.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
The past decade has seen substantial growth in the prevalence and capabilities of wearable devices. For instance, recent human activity recognition (HAR) research has explored using wearable devices in applications such as remote monitoring of patients, detection of gait abnormalities, and cognitive disease identification. However, data collection poses a major challenge in developing HAR systems, especially because of the need to store data at a central location. This raises privacy concerns and makes continuous data collection difficult and expensive due to the high cost of transferring data from a user’s wearable device to a central repository. Considering this, we explore the adoption of federated learning (FL) as a potential solution to address the privacy and cost issues associated with data collection in HAR. More specifically, we investigate the performance and behavioral differences between FL and deep learning (DL) HAR models, under various conditions relevant to real-world deployments. Namely, we explore the differences between the two types of models when (i) using data from different sensor placements, (ii) having access to users with data from heterogeneous sensor placements, (iii) considering bandwidth efficiency, and (iv) dealing with data with incorrect labels. Our results show that FL models suffer from a consistent performance deficit in comparison to their DL counterparts, but achieve these results with much better bandwidth efficiency. Furthermore, we observe that FL models exhibit very similar responses to those of DL models when exposed to data from heterogeneous sensor placements. Finally, we show that the FL models are more robust to data with incorrect labels than their centralized DL counterparts.
@article{kalabakov2023federated, abstract = {The past decade has seen substantial growth in the prevalence and capabilities of wearable devices. For instance, recent human activity recognition (HAR) research has explored using wearable devices in applications such as remote monitoring of patients, detection of gait abnormalities, and cognitive disease identification. However, data collection poses a major challenge in developing HAR systems, especially because of the need to store data at a central location. This raises privacy concerns and makes continuous data collection difficult and expensive due to the high cost of transferring data from a user’s wearable device to a central repository. Considering this, we explore the adoption of federated learning (FL) as a potential solution to address the privacy and cost issues associated with data collection in HAR. More specifically, we investigate the performance and behavioral differences between FL and deep learning (DL) HAR models, under various conditions relevant to real-world deployments. Namely, we explore the differences between the two types of models when (i) using data from different sensor placements, (ii) having access to users with data from heterogeneous sensor placements, (iii) considering bandwidth efficiency, and (iv) dealing with data with incorrect labels. Our results show that FL models suffer from a consistent performance deficit in comparison to their DL counterparts, but achieve these results with much better bandwidth efficiency. Furthermore, we observe that FL models exhibit very similar responses to those of DL models when exposed to data from heterogeneous sensor placements. Finally, we show that the FL models are more robust to data with incorrect labels than their centralized DL counterparts.}, author = {Kalabakov, Stefan and Jovanovski, Borche and Denkovski, Daniel and Rakovic, Valentin and Pfitzner, Bjarne and Konak, Orhan and Arnrich, Bert and Gjoreski, Hristijan}, journal = {IEEE Access}, keywords = {bertarnrich bjarnepfitzner orhankonak widehealth stefankalabakov}, pages = {64442-64457}, title = {Federated Learning for Activity Recognition: A System Level Perspective}, volume = 11, year = 2023 }

HARE: Unifying the Human Activity Recognition Engineering Workflow. Konak, Orhan; van de Water, Robin; Döring, Valentin; Fiedler, Tobias; Liebe, Lucas; Masopust, Leander; Postnov, Kirill; Sauerwald, Franz; Treykorn, Felix; Wischmann, Alexander; Gjoreski, Hristijan; Luštrek, Mitja; Arnrich, Bert in Sensors (2023). 23(23)
[ BibTeX ] [ URL ] [ DOI ]
 
@article{s23239571, author = {Konak, Orhan and van de Water, Robin and Döring, Valentin and Fiedler, Tobias and Liebe, Lucas and Masopust, Leander and Postnov, Kirill and Sauerwald, Franz and Treykorn, Felix and Wischmann, Alexander and Gjoreski, Hristijan and Luštrek, Mitja and Arnrich, Bert}, journal = {Sensors}, keywords = {bertarnrich orhankonak robinvandewater widehealth}, number = 23, title = {HARE: Unifying the Human Activity Recognition Engineering Workflow}, volume = 23, year = 2023 }

Overcoming Data Scarcity in Human Activity Recognition. Konak, Orhan; Liebe, Lucas; Postnov, Kirill; Sauerwald, Franz; Gjoreski, Hristijan; Luštrek, Mitja; Arnrich, Bert in IEEE EMBC 2023 (2023).
[ BibTeX ]
 
@article{konak2023overcoming, author = {Konak, Orhan and Liebe, Lucas and Postnov, Kirill and Sauerwald, Franz and Gjoreski, Hristijan and Luštrek, Mitja and Arnrich, Bert}, booktitle = {IEEE EMBC 2023}, keywords = {bertarnrich orhankonak widehealth}, organization = {IEEE EMBC}, title = {Overcoming Data Scarcity in Human Activity Recognition}, year = 2023 }

A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition. Konak, Orhan; Wischmann, Alexander; van De Water, Robin; Arnrich, Bert (2023). 1–6.
[ BibTeX ]
 
@inproceedings{konak2023real, author = {Konak, Orhan and Wischmann, Alexander and van De Water, Robin and Arnrich, Bert}, booktitle = {Proceedings of the 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence}, keywords = {bertarnrich orhankonak robinvandewater}, pages = {1–6}, title = {A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition}, year = 2023 }

SONAR, a nursing activity dataset with inertial sensors. Konak, Orhan; Döring, Valentin; Fiedler, Tobias; Liebe, Lucas; Masopust, Leander; Postnov, Kirill; Sauerwald, Franz; Treykorn, Felix; Wischmann, Alexander; Kalabakov, Stefan; others in Scientific Data (2023). 10(1) 727.
[ BibTeX ]
 
@article{konak2023sonar, author = {Konak, Orhan and D{ö}ring, Valentin and Fiedler, Tobias and Liebe, Lucas and Masopust, Leander and Postnov, Kirill and Sauerwald, Franz and Treykorn, Felix and Wischmann, Alexander and Kalabakov, Stefan and others}, journal = {Scientific Data}, keywords = {bertarnrich orhankonak widehealth stefankalabakov}, number = 1, pages = 727, publisher = {Nature Publishing Group UK London}, title = {SONAR, a nursing activity dataset with inertial sensors}, volume = 10, year = 2023 }

DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units. Zhou, Lin; Fischer, Eric; Brahms, Clemens Markus; Granacher, Urs; Arnrich, Bert (2023).
[ BibTeX ]
 
@article{noauthororeditor2023duogait, author = {Zhou, Lin and Fischer, Eric and Brahms, Clemens Markus and Granacher, Urs and Arnrich, Bert}, keywords = {linzhou sys:relevantfor:dhc bertarnrich}, title = {DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units}, year = 2023 }

Protocol for a Randomized Crossover Trial to Evaluate the Effect of Soft Brace and Rigid Orthosis on Performance and Readiness to Return to Sport Six Months Post-ACL-Reconstruction. Jahnke, Sonja; Cruysen, Caren; Prill, Robert; Kittmann, Fabian; Pflug, Nicola; Albert, Justin Amadeus; de Camargo, Tibor; Arnrich, Bert; Królikowska, Aleksandra; Kołcz, Anna; Reichert, Paweł; Oleksy, Łukasz; Michel, Sven; Kopf, Sebastian; Wagner, Michael; Scheffler, Sven; Becker, Roland in Healthcare (2023). 11(4)
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
A randomized crossover trial was designed to investigate the influence of muscle activation and strength on functional stability/control of the knee joint, to determine whether bilateral imbalances still occur six months after successful anterior cruciate ligament reconstruction (ACLR), and to analyze whether the use of orthotic devices changes the activity onset of these muscles. Furthermore, conclusions on the feedforward and feedback mechanisms are highlighted. Therefore, twenty-eight patients will take part in a modified Back in Action (BIA) test battery at an average of six months after a primary unilateral ACLR, which used an autologous ipsilateral semitendinosus tendon graft. This includes double-leg and single-leg stability tests, double-leg and single-leg countermovement jumps, double-leg and single-leg drop jumps, a speedy jump test, and a quick feet test. During the tests, gluteus medius and semitendinosus muscle activity are analyzed using surface electromyography (sEMG). Motion analysis is conducted using Microsoft Azure DK and 3D force plates. The tests are performed while wearing knee rigid orthosis, soft brace, and with no aid, in random order. Additionally, the range of hip and knee motion and hip abductor muscle strength under isometric conditions are measured. Furthermore, patient-rated outcomes will be assessed.
@article{healthcare11040513, abstract = {A randomized crossover trial was designed to investigate the influence of muscle activation and strength on functional stability/control of the knee joint, to determine whether bilateral imbalances still occur six months after successful anterior cruciate ligament reconstruction (ACLR), and to analyze whether the use of orthotic devices changes the activity onset of these muscles. Furthermore, conclusions on the feedforward and feedback mechanisms are highlighted. Therefore, twenty-eight patients will take part in a modified Back in Action (BIA) test battery at an average of six months after a primary unilateral ACLR, which used an autologous ipsilateral semitendinosus tendon graft. This includes double-leg and single-leg stability tests, double-leg and single-leg countermovement jumps, double-leg and single-leg drop jumps, a speedy jump test, and a quick feet test. During the tests, gluteus medius and semitendinosus muscle activity are analyzed using surface electromyography (sEMG). Motion analysis is conducted using Microsoft Azure DK and 3D force plates. The tests are performed while wearing knee rigid orthosis, soft brace, and with no aid, in random order. Additionally, the range of hip and knee motion and hip abductor muscle strength under isometric conditions are measured. Furthermore, patient-rated outcomes will be assessed.}, author = {Jahnke, Sonja and Cruysen, Caren and Prill, Robert and Kittmann, Fabian and Pflug, Nicola and Albert, Justin Amadeus and de Camargo, Tibor and Arnrich, Bert and Królikowska, Aleksandra and Kołcz, Anna and Reichert, Paweł and Oleksy, Łukasz and Michel, Sven and Kopf, Sebastian and Wagner, Michael and Scheffler, Sven and Becker, Roland}, journal = {Healthcare}, keywords = {sys:relevantfor:dhc justinalbert bertarnrich}, number = 4, title = {Protocol for a Randomized Crossover Trial to Evaluate the Effect of Soft Brace and Rigid Orthosis on Performance and Readiness to Return to Sport Six Months Post-ACL-Reconstruction}, volume = 11, year = 2023 }

2022

Unsupervised Activity Recognition Using Trajectory Heatmaps from Inertial Measurement Unit Data. Konak., Orhan; Wegner., Pit; Albert., Justin; Arnrich., Bert (2022). 304–312.
[ BibTeX ] [ URL ] [ DOI ]
 
@conference{icaart22, author = {Konak., Orhan and Wegner., Pit and Albert., Justin and Arnrich., Bert}, booktitle = {Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,}, keywords = {justinalbert bertarnrich orhankonak}, organization = {INSTICC}, pages = {304-312}, publisher = {SciTePress}, title = {Unsupervised Activity Recognition Using Trajectory Heatmaps from Inertial Measurement Unit Data}, year = 2022 }

SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies. Chromik, Jonas; Kirsten, Kristina; Herdick, Arne; Kappattanavar, Arpita Mallikarjuna; Arnrich, Bert in Sensors (2022). 22(1)
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.
@article{s22010408, abstract = {Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.}, author = {Chromik, Jonas and Kirsten, Kristina and Herdick, Arne and Kappattanavar, Arpita Mallikarjuna and Arnrich, Bert}, journal = {Sensors}, keywords = {bertarnrich jonaschromik kristinakirsten arpitakappattanavar}, number = 1, title = {SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies}, volume = 22, year = 2022 }

Hand Gesture Recognition in Daily Life as an Additional Tool for Unobtrusive Data Labeling in Medical Studies. Joch, Julia; Kirsten, Kristina; Arnrich, Bert (2022).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
For many use cases, such as supervised machine learning, labeled data is needed. However, to collect information for labels in real-life contexts, scientists are confronted with the challenge of gathering labeled data over an extended period. Labeling this data can become problematic, as constant supervision, similar to a laboratory setting, is neither feasible nor desired. Therefore, participants of such studies have to label their data themselves via appropriate apps on a smartphone. Nevertheless, this process can become very obtrusive in daily life and might even influence the results, especially studies regarding emotions. For example, in studies where participants need to indicate their stress levels frequently, labels get missed in situations where it would be inappropriate to take the phone. Consequently, missing these labels presents a significant problem. This paper aims to provide an unobtrusive solution to labeling data in real-world studies. We recorded a dataset consisting of five gestures and data from daily life. Thereby, we provide a set of predefined gestures that can be distinguished from other everyday life activities by using accelerometer and gyroscope sensors of wearable devices on the wrist. The use of predefined hand gestures for labeling data can therefore serve as an additional tool for the labeling process. Two machine learning approaches were compared and achieved promising results with Matthews Correlation Coefficients of up to 0.789 for a Random Forest and up to 0.835 for a Convolutional Neural Network.
@inproceedings{joch_kirsten_2022handgesture, abstract = {For many use cases, such as supervised machine learning, labeled data is needed. However, to collect information for labels in real-life contexts, scientists are confronted with the challenge of gathering labeled data over an extended period. Labeling this data can become problematic, as constant supervision, similar to a laboratory setting, is neither feasible nor desired. Therefore, participants of such studies have to label their data themselves via appropriate apps on a smartphone. Nevertheless, this process can become very obtrusive in daily life and might even influence the results, especially studies regarding emotions. For example, in studies where participants need to indicate their stress levels frequently, labels get missed in situations where it would be inappropriate to take the phone. Consequently, missing these labels presents a significant problem. This paper aims to provide an unobtrusive solution to labeling data in real-world studies. We recorded a dataset consisting of five gestures and data from daily life. Thereby, we provide a set of predefined gestures that can be distinguished from other everyday life activities by using accelerometer and gyroscope sensors of wearable devices on the wrist. The use of predefined hand gestures for labeling data can therefore serve as an additional tool for the labeling process. Two machine learning approaches were compared and achieved promising results with Matthews Correlation Coefficients of up to 0.789 for a Random Forest and up to 0.835 for a Convolutional Neural Network.}, author = {Joch, Julia and Kirsten, Kristina and Arnrich, Bert}, booktitle = {Proceedings of the 7th international Workshop on Sensor-based Activity Recognition and Artifical Intelligence (iWOAR '22)}, keywords = {bertarnrich kristinakirsten}, publisher = {Association for Computing Machinery}, title = {Hand Gesture Recognition in Daily Life as an Additional Tool for Unobtrusive Data Labeling in Medical Studies}, year = 2022 }

Elements of a System for Automatic Monitoring of Specific Mental Health Characteristics at Home. Kirsten, Kristina; Arnrich, Bert (2022).
[ Abstract ] [ BibTeX ] [ URL ]
 
Addressing one’s mental health has never been more important. The incidences of mental diseases, such as depression or anxiety disorders, have drastically increased in recent years. The longer an adequate treatment is delayed, the greater the impact on the severity of the illness which often results in long absences from work. With the development of smart devices and wearables, it is already possible to measure many physiological parameters in everyday life. In addition, monitoring people in their natural environment offers many advantages, e.g. it is not based on retrospective feelings and memories but can measure and reflect the momentary state. This conceptual paper presents an overview of possible elements of a system for automated monitoring of mental health characteristics in the home. We describe examples of typical parameters for various mental disorders and present different systems and methods to measure them. Furthermore, we show how the individual components of a system can be connected to get a holistic view of specific mental health characteristics. Finally, we also discuss challenges and limitations.
@conference{kirsten_arnrich_2022, abstract = {Addressing one’s mental health has never been more important. The incidences of mental diseases, such as depression or anxiety disorders, have drastically increased in recent years. The longer an adequate treatment is delayed, the greater the impact on the severity of the illness which often results in long absences from work. With the development of smart devices and wearables, it is already possible to measure many physiological parameters in everyday life. In addition, monitoring people in their natural environment offers many advantages, e.g. it is not based on retrospective feelings and memories but can measure and reflect the momentary state. This conceptual paper presents an overview of possible elements of a system for automated monitoring of mental health characteristics in the home. We describe examples of typical parameters for various mental disorders and present different systems and methods to measure them. Furthermore, we show how the individual components of a system can be connected to get a holistic view of specific mental health characteristics. Finally, we also discuss challenges and limitations.}, author = {Kirsten, Kristina and Arnrich, Bert}, booktitle = {Proceedings of the 25th International Multiconference Information Society 2022, IS 2022, Ljubljana, Slovenia}, keywords = {sys:relevantfor:dhc bertarnrich kristinakirsten}, title = {Elements of a System for Automatic Monitoring of Specific Mental Health Characteristics at Home}, year = 2022 }

PERSIST: A Multimodal Dataset for the Prediction of Perceived Exertion during Resistance Training. Albert, Justin Amadeus; Herdick, Arne; Brahms, Clemens Markus; Granacher, Urs; Arnrich, Bert in Data (2022). 8(1)
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Measuring and adjusting the training load is essential in resistance training, as training overload can increase the risk of injuries. At the same time, too little load does not deliver the desired training effects. Usually, external load is quantified using objective measurements, such as lifted weight distributed across sets and repetitions per exercise. Internal training load is usually assessed using questionnaires or ratings of perceived exertion (RPE). A standard RPE scale is the Borg scale, which ranges from 6 (no exertion) to 20 (the highest exertion ever experienced). Researchers have investigated predicting RPE for different sports using sensor modalities and machine learning methods, such as Support Vector Regression or Random Forests. This paper presents PERSIST, a novel dataset for predicting PERceived exertion during reSIStance Training. We recorded multiple sensor modalities simultaneously, including inertial measurement units (IMU), electrocardiography (ECG), and motion capture (MoCap). The MoCap data has been synchronized to the IMU and ECG data. We also provide heart rate variability (HRV) parameters obtained from the ECG signal. Our dataset contains data from twelve young and healthy male participants with at least one year of resistance training experience. Subjects performed twelve sets of squats on a Flywheel platform with twelve repetitions per set. After each set, subjects reported their current RPE. We chose the squat exercise as it involves the largest muscle group. This paper demonstrates how to access the dataset. We further present an exploratory data analysis and show how researchers can use IMU and ECG data to predict perceived exertion.
@article{data8010009, abstract = {Measuring and adjusting the training load is essential in resistance training, as training overload can increase the risk of injuries. At the same time, too little load does not deliver the desired training effects. Usually, external load is quantified using objective measurements, such as lifted weight distributed across sets and repetitions per exercise. Internal training load is usually assessed using questionnaires or ratings of perceived exertion (RPE). A standard RPE scale is the Borg scale, which ranges from 6 (no exertion) to 20 (the highest exertion ever experienced). Researchers have investigated predicting RPE for different sports using sensor modalities and machine learning methods, such as Support Vector Regression or Random Forests. This paper presents PERSIST, a novel dataset for predicting PERceived exertion during reSIStance Training. We recorded multiple sensor modalities simultaneously, including inertial measurement units (IMU), electrocardiography (ECG), and motion capture (MoCap). The MoCap data has been synchronized to the IMU and ECG data. We also provide heart rate variability (HRV) parameters obtained from the ECG signal. Our dataset contains data from twelve young and healthy male participants with at least one year of resistance training experience. Subjects performed twelve sets of squats on a Flywheel platform with twelve repetitions per set. After each set, subjects reported their current RPE. We chose the squat exercise as it involves the largest muscle group. This paper demonstrates how to access the dataset. We further present an exploratory data analysis and show how researchers can use IMU and ECG data to predict perceived exertion.}, author = {Albert, Justin Amadeus and Herdick, Arne and Brahms, Clemens Markus and Granacher, Urs and Arnrich, Bert}, journal = {Data}, keywords = {justinalbert bertarnrich arneherdick}, number = 1, title = {PERSIST: A Multimodal Dataset for the Prediction of Perceived Exertion during Resistance Training}, volume = 8, year = 2022 }

2021

Optimal Sensor Placement for Human Activity Recognition with a Minimal Smartphone–IMU Setup. Rahn, Vincent Xeno; Zhou, Lin; Klieme, Eric; Arnrich, Bert (2021). (Vol. 10) 37–48.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Human Activity Recognition (HAR) of everyday activities using smartphones has been intensively researched over the past years. Despite the high detection performance, smartphones can not continuously provide reli- able information about the currently conducted activity as their placement at the subject’s body is uncertain. In this study, a system is developed that enables real-time collection of data from various Bluetooth inertial mea- surement units (IMUs) in addition to the smartphone. The contribution of this work is an extensive overview of related work in this field and the identification of unobtrusive, minimal combinations of IMUs with the smartphone that achieve high recognition performance. Eighteen young subjects with unrestricted mobility were recorded conducting seven daily-life activities with a smartphone in the pocket and five IMUs at different body positions. With a Convolutional Neural Network (CNN) for activity recognition, activity classification accuracy increased by up to 23% with one IMU additional to the smartphone. An overall prediction rate of 97% was reached with a smartphone in the pocket and an IMU at the ankle. This study demonstrated the potential that an additional IMU can improve the accuracy of smartphone-based HAR on daily-life activities.
@inproceedings{rahn2021optimal, abstract = {Human Activity Recognition (HAR) of everyday activities using smartphones has been intensively researched over the past years. Despite the high detection performance, smartphones can not continuously provide reli- able information about the currently conducted activity as their placement at the subject’s body is uncertain. In this study, a system is developed that enables real-time collection of data from various Bluetooth inertial mea- surement units (IMUs) in addition to the smartphone. The contribution of this work is an extensive overview of related work in this field and the identification of unobtrusive, minimal combinations of IMUs with the smartphone that achieve high recognition performance. Eighteen young subjects with unrestricted mobility were recorded conducting seven daily-life activities with a smartphone in the pocket and five IMUs at different body positions. With a Convolutional Neural Network (CNN) for activity recognition, activity classification accuracy increased by up to 23% with one IMU additional to the smartphone. An overall prediction rate of 97% was reached with a smartphone in the pocket and an IMU at the ankle. This study demonstrated the potential that an additional IMU can improve the accuracy of smartphone-based HAR on daily-life activities.}, author = {Rahn, Vincent Xeno and Zhou, Lin and Klieme, Eric and Arnrich, Bert}, booktitle = {Proceedings of the 10th International Conference on Sensor Networks - Volume 1: SENSORNETS,}, keywords = {linzhou sys:relevantfor:dhc bertarnrich}, month = {February}, organization = {INSTICC}, pages = {37-48}, publisher = {SciTePress}, title = {Optimal Sensor Placement for Human Activity Recognition with a Minimal Smartphone–IMU Setup}, volume = 10, year = 2021 }

Sensor-Based Obsessive-Compulsive Disorder Detection With Personalised Federated Learning. Kirsten, Kristina; Pfitzner, Bjarne; Löper, Lando; Arnrich, Bert (2021). 333–339.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
The mental illness Obsessive-Compulsive Disorder (OCD) is characterised by obsessive thoughts and compulsive actions. The latter can occur as repetitive activities to ensure that severe fears do not come true. A diagnosis of the disease is usually very late due to a lack of knowledge and shame of the patient. Nevertheless, early detection can significantly increase the success of therapy. With the development of new wearable sensors, it is possible to recognise human activities. Accordingly, wearables can also be used to identify recurring activities that indicate an OCD. Through this form of an automatic detection system, a diagnosis can be made earlier and thus therapy can be started sooner. Since compulsive behaviour is very individual and varies from patient to patient, this paper deals with personalised federated machine learning models. We first adapt the publicly available OPPORTUNITY dataset to simulate OCD behaviour. Secondly, we evaluate two existing personalised federated learning algorithms against baseline approaches. Finally, we propose a hybrid approach that merges the two evaluated algorithms and reaches a mean area under the precision-recall curve (AUPRC) of 0.954 across clients.
@inproceedings{kirsten2021sensorbased, abstract = {The mental illness Obsessive-Compulsive Disorder (OCD) is characterised by obsessive thoughts and compulsive actions. The latter can occur as repetitive activities to ensure that severe fears do not come true. A diagnosis of the disease is usually very late due to a lack of knowledge and shame of the patient. Nevertheless, early detection can significantly increase the success of therapy. With the development of new wearable sensors, it is possible to recognise human activities. Accordingly, wearables can also be used to identify recurring activities that indicate an OCD. Through this form of an automatic detection system, a diagnosis can be made earlier and thus therapy can be started sooner. Since compulsive behaviour is very individual and varies from patient to patient, this paper deals with personalised federated machine learning models. We first adapt the publicly available OPPORTUNITY dataset to simulate OCD behaviour. Secondly, we evaluate two existing personalised federated learning algorithms against baseline approaches. Finally, we propose a hybrid approach that merges the two evaluated algorithms and reaches a mean area under the precision-recall curve (AUPRC) of 0.954 across clients.}, author = {Kirsten, Kristina and Pfitzner, Bjarne and Löper, Lando and Arnrich, Bert}, booktitle = {2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}, keywords = {bertarnrich bjarnepfitzner kristinakirsten}, pages = {333-339}, title = {Sensor-Based Obsessive-Compulsive Disorder Detection With Personalised Federated Learning}, year = 2021 }

Using Machine Learning to Predict Perceived Exertion During Resistance Training With Wearable Heart Rate and Movement Sensors. Albert, Justin; Herdick, Arne; Brahms, Clemens Markus; Granacher, Urs; Arnrich, Bert (2021).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
The quantification of subjective exertion during training is an important measurement as it has the potential to avoid injuries resulting from overtraining. In this paper, we present a method to predict the subjective exertion during resistance training using Inertial Measurement Units (IMU) and electrocardiographical data. The participants’ subjective exertion was assessed using a Rating of Perceived Exertion (RPE) scale. We obtained data from 16 participants performing squats on a flywheel training machine while being equipped with six IMU sensors and an electrocardiography (ECG) sensor. Data was analyzed using multiple regressors, such as Support Vector Regression, Random Forests, and Gradient Boosting Regression Trees, to predict the personal exertion level on the processed IMU and ECG data. The best learning model achieved a mean absolute percentage error of 7.71% with a Pearson correlation coefficient of 0.85 and a R 2 of 0.48. Additionally, we investigated the impact of supplementing the IMU data features with ECG-derived heart rate variability (HRV) parameters in the training stage. Our results indicate that the HRV parameters derived from ECG significantly improve prediction results, with the training impulse (TRIMP) parameter acting as the most informative feature for predicting perceived exertion.
@inproceedings{albert2021using, abstract = {The quantification of subjective exertion during training is an important measurement as it has the potential to avoid injuries resulting from overtraining. In this paper, we present a method to predict the subjective exertion during resistance training using Inertial Measurement Units (IMU) and electrocardiographical data. The participants’ subjective exertion was assessed using a Rating of Perceived Exertion (RPE) scale. We obtained data from 16 participants performing squats on a flywheel training machine while being equipped with six IMU sensors and an electrocardiography (ECG) sensor. Data was analyzed using multiple regressors, such as Support Vector Regression, Random Forests, and Gradient Boosting Regression Trees, to predict the personal exertion level on the processed IMU and ECG data. The best learning model achieved a mean absolute percentage error of 7.71% with a Pearson correlation coefficient of 0.85 and a R 2 of 0.48. Additionally, we investigated the impact of supplementing the IMU data features with ECG-derived heart rate variability (HRV) parameters in the training stage. Our results indicate that the HRV parameters derived from ECG significantly improve prediction results, with the training impulse (TRIMP) parameter acting as the most informative feature for predicting perceived exertion.}, address = {Los Alamitos, CA, USA}, author = {Albert, Justin and Herdick, Arne and Brahms, Clemens Markus and Granacher, Urs and Arnrich, Bert}, booktitle = {2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, keywords = {justinalbert bertarnrich arneherdick}, month = {dec}, publisher = {IEEE Computer Society}, title = {Using Machine Learning to Predict Perceived Exertion During Resistance Training With Wearable Heart Rate and Movement Sensors}, year = 2021 }

TRIPOD—A Treadmill Walking Dataset with IMU, Pressure-Distribution and Photoelectric Data for Gait Analysis. Trautmann, Justin; Zhou, Lin; Brahms, Clemens Markus; Tunca, Can; Ersoy, Cem; Granacher, Urs; Arnrich, Bert in Data (2021). 6(9) 95.
[ BibTeX ] [ URL ] [ DOI ]
 
@article{trautmann2021tripod, author = {Trautmann, Justin and Zhou, Lin and Brahms, Clemens Markus and Tunca, Can and Ersoy, Cem and Granacher, Urs and Arnrich, Bert}, journal = {Data}, keywords = {linzhou bertarnrich}, number = 9, pages = 95, publisher = {Multidisciplinary Digital Publishing Institute}, title = {TRIPOD—A Treadmill Walking Dataset with IMU, Pressure-Distribution and Photoelectric Data for Gait Analysis}, volume = 6, year = 2021 }

Data Augmentation of Kinematic Time-Series From Rehabilitation Exercises Using GANs. Albert, Justin; Glöckner, Pawel; Pfitzner, Bjarne; Arnrich, Bert (2021). 1–6.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Machine learning, especially deep learning, offers great potential for medical applications. However, deep learning algorithms need a vast amount of training data. Especially in the medical domain, it is challenging to collect larger datasets. Access to patients can be limited, and data recording is mainly bound to laboratory settings requiring expertise from medical professionals. When involving a healthy control group, datasets are often unbalanced, with most data belonging to the control group. This paper proposes a data augmentation method to generate pose data of repetitive rehabilitation exercises trained on a specific population, e.g., a specific neurological disease. Our method is based on a generative adversarial network (GAN) that uses convolutional and long short-term memory (LSTM) layers. We evaluated our method using a dataset that contains rehabilitation exercises from stroke and Parkinson’s disease patients and a healthy control group. We demonstrated that a classifier trained using our augmentation method could distinguish between healthy, stroke, and Parkinson’s disease patients with an accuracy of 81%. In contrast, the same classifier achieved only 75% when using a standard resampling technique.
@inproceedings{9524284, abstract = {Machine learning, especially deep learning, offers great potential for medical applications. However, deep learning algorithms need a vast amount of training data. Especially in the medical domain, it is challenging to collect larger datasets. Access to patients can be limited, and data recording is mainly bound to laboratory settings requiring expertise from medical professionals. When involving a healthy control group, datasets are often unbalanced, with most data belonging to the control group. This paper proposes a data augmentation method to generate pose data of repetitive rehabilitation exercises trained on a specific population, e.g., a specific neurological disease. Our method is based on a generative adversarial network (GAN) that uses convolutional and long short-term memory (LSTM) layers. We evaluated our method using a dataset that contains rehabilitation exercises from stroke and Parkinson’s disease patients and a healthy control group. We demonstrated that a classifier trained using our augmentation method could distinguish between healthy, stroke, and Parkinson’s disease patients with an accuracy of 81%. In contrast, the same classifier achieved only 75% when using a standard resampling technique.}, author = {Albert, Justin and Glöckner, Pawel and Pfitzner, Bjarne and Arnrich, Bert}, booktitle = {2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS)}, keywords = {sys:relevantfor:dhc justinalbert bertarnrich bjarnepfitzner}, pages = {1-6}, title = {Data Augmentation of Kinematic Time-Series From Rehabilitation Exercises Using GANs}, year = 2021 }

2020

Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations. Zhou, Lin; Tunca, Can; Fischer, Eric; Brahms, Clemens Markus; Ersoy, Cem; Granacher, Urs; Arnrich, Bert (2020).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person’s cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life,individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.
@inproceedings{zhou2020validation, abstract = {Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person’s cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life,individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.}, author = {Zhou, Lin and Tunca, Can and Fischer, Eric and Brahms, Clemens Markus and Ersoy, Cem and Granacher, Urs and Arnrich, Bert}, booktitle = {2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)}, keywords = {linzhou bertarnrich}, month = {july}, publisher = {IEEE}, title = {Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations}, year = 2020 }

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications. Zhou, Lin; Fischer, Eric; Tunca, Can; Brahms, Clemens Markus; Ersoy, Cem; Granacher, Urs; Arnrich, Bert in Sensors (2020).
[ BibTeX ] [ URL ] [ DOI ]
 
@article{zhou2020found, author = {Zhou, Lin and Fischer, Eric and Tunca, Can and Brahms, Clemens Markus and Ersoy, Cem and Granacher, Urs and Arnrich, Bert}, journal = {Sensors}, keywords = {linzhou bertarnrich}, title = {How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications}, year = 2020 }

Will You Be My Quarantine: A Computer Vision and Inertial Sensor Based Home Exercise System. Albert, Justin; Zhou, Lin; Gloeckner, Pawel; Trautmann, Justin; Ihde, Lisa; Eilers, Justus; Kamal, Mohammed; Arnrich, Bert (2020). (Vol. 14)
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
The quarantine situation inflicted by the COVID-19 pandemic has left many people around the world isolated at home. Despite the large variety of mobile device-based self exercise tools for training plans, activity recognition or repetition counts, it remains challenging for an inexperienced person to perform fitness workouts or learn a new sport with the correct movements at home. As a proof of concept, a home exercise system has been developed in this contribution. The system takes computer vision and inertial sensor data recorded for the same type of exercise as two independent inputs, and processes the data from both sources into the same representations on the levels of raw inertial measurement unit (IMU) data and 3D movement trajectories. Moreover, a Key Performance Indicator (KPI) dashboard was developed for data import and visualization. The usability of the system was investigated with an example use case where the learner equipped with IMUs performed a kick movement and was able to compare it to that from a coach in the video.
@inproceedings{albert2020quarantine, abstract = {The quarantine situation inflicted by the COVID-19 pandemic has left many people around the world isolated at home. Despite the large variety of mobile device-based self exercise tools for training plans, activity recognition or repetition counts, it remains challenging for an inexperienced person to perform fitness workouts or learn a new sport with the correct movements at home. As a proof of concept, a home exercise system has been developed in this contribution. The system takes computer vision and inertial sensor data recorded for the same type of exercise as two independent inputs, and processes the data from both sources into the same representations on the levels of raw inertial measurement unit (IMU) data and 3D movement trajectories. Moreover, a Key Performance Indicator (KPI) dashboard was developed for data import and visualization. The usability of the system was investigated with an example use case where the learner equipped with IMUs performed a kick movement and was able to compare it to that from a coach in the video.}, author = {Albert, Justin and Zhou, Lin and Gloeckner, Pawel and Trautmann, Justin and Ihde, Lisa and Eilers, Justus and Kamal, Mohammed and Arnrich, Bert}, booktitle = {14th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters}, journal = {Pervasive Health}, keywords = {linzhou justinalbert bertarnrich}, month = 8, publisher = {EAI}, title = {Will You Be My Quarantine: A Computer Vision and Inertial Sensor Based Home Exercise System}, volume = 14, year = 2020 }

Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study. Albert, Justin; Owolabi, Victor; Gebel, Arnd; Brahms, Markus Clemens; Granacher, Urs; Arnrich, Bert in MDPI Sensors (2020). 20(18)
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking usinggold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.
@article{albert2020evaluation, abstract = {Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking usinggold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.}, author = {Albert, Justin and Owolabi, Victor and Gebel, Arnd and Brahms, Markus Clemens and Granacher, Urs and Arnrich, Bert}, journal = {MDPI Sensors}, keywords = {justinalbert bertarnrich}, title = {Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study}, volume = {20(18)}, year = 2020 }

IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition. Konak, Orhan; Wegner, Pit; Arnrich, Bert in Sensors (Switzerland) (2020). 20(24) 1–15.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.
@article{Konak2020, abstract = {Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.}, author = {Konak, Orhan and Wegner, Pit and Arnrich, Bert}, journal = {Sensors (Switzerland)}, keywords = {bertarnrich orhankonak}, number = 24, pages = {1–15}, title = {IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition}, volume = 20, year = 2020 }

2019

Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions. Morassi Sasso, Ariane; Datta, Suparno; Pfitzner, Bjarne; Zhou, Lin; Steckhan, Nico; Boettinger, Erwin; Arnrich, Bert (2019).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
Hypertension is one of the most prevalent chronic diseases worldwide. Early diagnosis of this condition can prevent the incidence of stroke and also, cardiovascular diseases (CVDs) such as myocardial infarction and heart failure. Lifestyle interventions, such as intermittent fasting (IF), aim to lower blood pressure (BP) levels and increase the health of patients with cardiometabolic conditions. However, for monitoring BP, we still rely on a cuff that slows the �ow of blood, which is both uncomfortable and makes continuous monitoring implausible. Recent research has shown that BP can be estimated using comfortable sensors such as the photoplethysmography (PPG) and the electrocardiography (ECG). Features that can be used for the estimation of BP are systolic upstroke time (SUT) and diastolic time (DT) extracted from the PPG signal, and pulse arrival and transit time (PAT/PTT) derived from the combination of ECG and PPG signals. In this paper we present: (1) a study design to collect continuous physiological signals, before and after a 7-days intermittent fasting (IF) intervention from both cardiometabolic and non-hypertensive patients using wearable devices and (2) initial results for predicting continuous blood pressure from the PPG and ECG signals using statistical and machine learning methods.
@inproceedings{MorassiSasso2019, abstract = {Hypertension is one of the most prevalent chronic diseases worldwide. Early diagnosis of this condition can prevent the incidence of stroke and also, cardiovascular diseases (CVDs) such as myocardial infarction and heart failure. Lifestyle interventions, such as intermittent fasting (IF), aim to lower blood pressure (BP) levels and increase the health of patients with cardiometabolic conditions. However, for monitoring BP, we still rely on a cuff that slows the �ow of blood, which is both uncomfortable and makes continuous monitoring implausible. Recent research has shown that BP can be estimated using comfortable sensors such as the photoplethysmography (PPG) and the electrocardiography (ECG). Features that can be used for the estimation of BP are systolic upstroke time (SUT) and diastolic time (DT) extracted from the PPG signal, and pulse arrival and transit time (PAT/PTT) derived from the combination of ECG and PPG signals. In this paper we present: (1) a study design to collect continuous physiological signals, before and after a 7-days intermittent fasting (IF) intervention from both cardiometabolic and non-hypertensive patients using wearable devices and (2) initial results for predicting continuous blood pressure from the PPG and ECG signals using statistical and machine learning methods.}, address = {Trento}, author = {Morassi Sasso, Ariane and Datta, Suparno and Pfitzner, Bjarne and Zhou, Lin and Steckhan, Nico and Boettinger, Erwin and Arnrich, Bert}, booktitle = {Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters}, keywords = {linzhou nicosteckhan bertarnrich bjarnepfitzner}, publisher = {EAI}, title = {Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions}, year = 2019 }

MORPHER – A Platform to Support Modeling of Outcome and Risk Prediction in Health Research. Freitas da Cruz, Harry; Bergner, Benjamin; Konak, Orhan; Schneider, Frederic; Bode, Philipp; Lempert, Conrad; Schapranow, Matthieu-P. (2019).
[ BibTeX ]
 
@conference{freitasdacruz2019morpher, author = {Freitas da Cruz, Harry and Bergner, Benjamin and Konak, Orhan and Schneider, Frederic and Bode, Philipp and Lempert, Conrad and Schapranow, Matthieu-P.}, booktitle = {Proceedings of the 19th IEEE International Conference on Bioinformatics and Biomedicine}, keywords = {orhankonak}, title = {MORPHER – A Platform to Support Modeling of Outcome and Risk Prediction in Health Research}, year = 2019 }

An Information and Communication Platform Supporting Analytics for Elderly Care. Konak, Orhan; Freitas Da Cruz, Harry; Thiele, Marvin; Golla, David; Schapranow, Matthieu-P. (2019).
[ BibTeX ]
 
@inproceedings{konak2019information, author = {Konak, Orhan and Freitas Da Cruz, Harry and Thiele, Marvin and Golla, David and Schapranow, Matthieu-P.}, booktitle = {5th International Conference on Information for Ageing Well, Communication Technologies e Health}, keywords = {orhankonak}, title = {An Information and Communication Platform Supporting Analytics for Elderly Care}, year = 2019 }

2017

Geometric Algebra Computing for Heterogeneous Systems. Hildenbrand, D.; Albert, Justin; Charrier, P.; Steinmetz, C. in Advances in Applied Clifford Algebras (2017). 27 599–620.
[ BibTeX ] [ URL ] [ DOI ]
 
@article{Hildenbrand2017GeometricAC, author = {Hildenbrand, D. and Albert, Justin and Charrier, P. and Steinmetz, C.}, journal = {Advances in Applied Clifford Algebras}, keywords = {justinalbert prior-hpi}, pages = {599-620}, title = {Geometric Algebra Computing for Heterogeneous Systems}, volume = 27, year = 2017 }