2022

Have Your Cake and Log it Too: A Pilot Study Leveraging IMU Sensors for Real-time Food Journaling Notifications Kappattanavar, Arpita; Kremser, Marten; Arnrich, Bert (2022). (Vol. 5) 532–541.
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@proceedings{kappattanavarhave, author = {Kappattanavar, Arpita and Kremser, Marten and Arnrich, Bert}, keywords = {arpitakappattanavar bertarnrich}, pages = {532-541}, title = {Have Your Cake and Log it Too: A Pilot Study Leveraging IMU Sensors for Real-time Food Journaling Notifications}, volume = 5, year = 2022 }
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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)
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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 = {arpitakappattanavar bertarnrich jonaschromik kristinakirsten}, number = 1, title = {SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies}, volume = 22, year = 2022 }
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URNhttp://dx.doi.org/10.3390/s22010408
AbstractObservational 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.

2021

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review. Kappattanavar, Arpita Mallikarjuna; Steckhan, Nico; Sachs, Jan Philipp; Freitas da Cruz, Harry; Boettinger, Erwin; Arnrich, Bert in JMIR Biomed Eng (2021). 6(1) e21105.
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Background: A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain. Objective: This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements, using sensors and wearables such as inertial measurement units, pressure or piezoresistive sensors, accelerometers or gyroscopes, combined with machine learning approaches. Methods: A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases in the shortlisted papers. Results: A total of 14 papers were shortlisted from 952 papers obtained after a systematic search. The majority of the studies used pressure sensors to measure sitting postures, whereas neural networks were the most frequently used approaches for classification tasks in this context. Only 2 studies were performed in a free-living environment. Most studies presented ethical and methodological shortcomings. Moreover, the findings indicate that the strategic placement of sensors can lead to better performance and lower costs. Conclusions: The included studies differed in various aspects of design and analysis. The majority of studies were rated as medium quality according to our assessment. Our study suggests that future work for posture classification can benefit from using inertial measurement unit sensors, since they make it possible to differentiate among spine movements and similar postures, considering transitional movements between postures, and using three-dimensional cameras to annotate the data for ground truth. Finally, comparing such studies is challenging, as there are no standard definitions of sitting postures that could be used for classification. In addition, this study identifies five basic sitting postures along with different combinations of limb and spine movements to help guide future research efforts.
@article{info:doi/10.2196/21105, abstract = {Background: A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain. Objective: This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements, using sensors and wearables such as inertial measurement units, pressure or piezoresistive sensors, accelerometers or gyroscopes, combined with machine learning approaches. Methods: A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases in the shortlisted papers. Results: A total of 14 papers were shortlisted from 952 papers obtained after a systematic search. The majority of the studies used pressure sensors to measure sitting postures, whereas neural networks were the most frequently used approaches for classification tasks in this context. Only 2 studies were performed in a free-living environment. Most studies presented ethical and methodological shortcomings. Moreover, the findings indicate that the strategic placement of sensors can lead to better performance and lower costs. Conclusions: The included studies differed in various aspects of design and analysis. The majority of studies were rated as medium quality according to our assessment. Our study suggests that future work for posture classification can benefit from using inertial measurement unit sensors, since they make it possible to differentiate among spine movements and similar postures, considering transitional movements between postures, and using three-dimensional cameras to annotate the data for ground truth. Finally, comparing such studies is challenging, as there are no standard definitions of sitting postures that could be used for classification. In addition, this study identifies five basic sitting postures along with different combinations of limb and spine movements to help guide future research efforts.}, author = {Kappattanavar, Arpita Mallikarjuna and Steckhan, Nico and Sachs, Jan Philipp and Freitas da Cruz, Harry and Boettinger, Erwin and Arnrich, Bert}, journal = {JMIR Biomed Eng}, keywords = {arpitakappattanavar bertarnrich}, month = {mar}, number = 1, pages = {e21105}, title = {Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review}, volume = 6, year = 2021 }
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URNhttp://dx.doi.org/10.2196/21105
AbstractBackground: A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain. Objective: This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements, using sensors and wearables such as inertial measurement units, pressure or piezoresistive sensors, accelerometers or gyroscopes, combined with machine learning approaches. Methods: A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases in the shortlisted papers. Results: A total of 14 papers were shortlisted from 952 papers obtained after a systematic search. The majority of the studies used pressure sensors to measure sitting postures, whereas neural networks were the most frequently used approaches for classification tasks in this context. Only 2 studies were performed in a free-living environment. Most studies presented ethical and methodological shortcomings. Moreover, the findings indicate that the strategic placement of sensors can lead to better performance and lower costs. Conclusions: The included studies differed in various aspects of design and analysis. The majority of studies were rated as medium quality according to our assessment. Our study suggests that future work for posture classification can benefit from using inertial measurement unit sensors, since they make it possible to differentiate among spine movements and similar postures, considering transitional movements between postures, and using three-dimensional cameras to annotate the data for ground truth. Finally, comparing such studies is challenging, as there are no standard definitions of sitting postures that could be used for classification. In addition, this study identifies five basic sitting postures along with different combinations of limb and spine movements to help guide future research efforts.

2020

Position Matters: Sensor Placement for SittingPosture Classification. Kappattanavar, A. M.; da Cruz, H. F.; Arnrich, B.; Böttinger, E. (2020).
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Prolonged sitting behavior and postures that cause strain on the spine and muscles have been reported to increase the probability of low back pain. To address this issue, many commercially available sensors already provide feedback about whether a person is 'slouching' or 'not slouching'. However, they do not provide information on a person's posture, which would give insights into the strain caused by a specific posture. Hence, in this pilot study, we attempt to find the optimum number of inertial measurement unit sensors required and the best locations to place them using six mock postures. Data is collected from these sensors and features are extracted. The number of features are reduced and the best features are selected using the Recursive Feature Elimination method with Cross-Validation. The reduced number of features is then trained and tested on Logistic Regression, Support Vector Machine and Hierarchical Model. Among the three models, the Support Vector Machine algorithm had the highest accuracy of 93.68%, obtained for the thoracic, hip and sacral region sensor combinations. While these findings will be validated in a larger study in an uncontrolled environment, this pilot study quantitatively highlights the importance of sensor placement in shaping discriminative performance in sitting posture classification tasks.
@inproceedings{kappattanavar2020position, abstract = {Prolonged sitting behavior and postures that cause strain on the spine and muscles have been reported to increase the probability of low back pain. To address this issue, many commercially available sensors already provide feedback about whether a person is 'slouching' or 'not slouching'. However, they do not provide information on a person's posture, which would give insights into the strain caused by a specific posture. Hence, in this pilot study, we attempt to find the optimum number of inertial measurement unit sensors required and the best locations to place them using six mock postures. Data is collected from these sensors and features are extracted. The number of features are reduced and the best features are selected using the Recursive Feature Elimination method with Cross-Validation. The reduced number of features is then trained and tested on Logistic Regression, Support Vector Machine and Hierarchical Model. Among the three models, the Support Vector Machine algorithm had the highest accuracy of 93.68%, obtained for the thoracic, hip and sacral region sensor combinations. While these findings will be validated in a larger study in an uncontrolled environment, this pilot study quantitatively highlights the importance of sensor placement in shaping discriminative performance in sitting posture classification tasks.}, author = {Kappattanavar, A. M. and da Cruz, H. F. and Arnrich, B. and Böttinger, E.}, booktitle = {ICHI '20: Proceedings of the 2020 IEEE International Conference on Healthcare Informatics}, keywords = {arpitakappattanavar bertarnrich fb-boettinger}, publisher = {IEEE}, title = {Position Matters: Sensor Placement for SittingPosture Classification}, year = 2020 }
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URNhttp://dx.doi.org/10.1109/ICHI48887.2020.9374328
AbstractProlonged sitting behavior and postures that cause strain on the spine and muscles have been reported to increase the probability of low back pain. To address this issue, many commercially available sensors already provide feedback about whether a person is 'slouching' or 'not slouching'. However, they do not provide information on a person's posture, which would give insights into the strain caused by a specific posture. Hence, in this pilot study, we attempt to find the optimum number of inertial measurement unit sensors required and the best locations to place them using six mock postures. Data is collected from these sensors and features are extracted. The number of features are reduced and the best features are selected using the Recursive Feature Elimination method with Cross-Validation. The reduced number of features is then trained and tested on Logistic Regression, Support Vector Machine and Hierarchical Model. Among the three models, the Support Vector Machine algorithm had the highest accuracy of 93.68%, obtained for the thoracic, hip and sacral region sensor combinations. While these findings will be validated in a larger study in an uncontrolled environment, this pilot study quantitatively highlights the importance of sensor placement in shaping discriminative performance in sitting posture classification tasks.

Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study. Moontaha, Sidratul; Steckhan, Nico; Kappattanavar, Arpita; Surges, Rainer; Arnrich, Bert (2020). (Vol. 14)
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Drug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.
@inproceedings{moontaha2020selfprediction, abstract = {Drug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.}, author = {Moontaha, Sidratul and Steckhan, Nico and Kappattanavar, Arpita and Surges, Rainer and Arnrich, Bert}, booktitle = {14th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters}, journal = {Pervasive Health}, keywords = {arpitakappattanavar bertarnrich nicosteckhan sidratulmoontaha}, month = {oct}, title = {Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study}, volume = 14, year = 2020 }
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AbstractDrug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.

2018

HPI-DHC at TREC 2018 Precision Medicine Track. Oleynik, M; Faessler, E; Sasso, A.M; Kappattanavar, A.; Bergner, B.; da Cruz, H.F.; Sachs, J.-P.; Datta, S.; Boettinger, E (2018).
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@inproceedings{oleynik2018hpidhc, author = {Oleynik, M and Faessler, E and Sasso, A.M and Kappattanavar, A. and Bergner, B. and da Cruz, H.F. and Sachs, J.-P. and Datta, S. and Boettinger, E}, keywords = {arpitakappattanavar}, title = {HPI-DHC at TREC 2018 Precision Medicine Track}, year = 2018 }
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