Human Motion Analysis Using 3D Cameras

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 = {bertarnrich justinalbert orhankonak}, organization = {INSTICC}, pages = {304-312}, publisher = {SciTePress}, title = {Unsupervised Activity Recognition Using Trajectory Heatmaps from Inertial Measurement Unit Data}, year = 2022 }

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 = {arneherdick bertarnrich justinalbert}, 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 }

Data Augmentation of Kinematic Time-Series From Rehabilitation Exercises Using GANs. Albert, Justin; Glöckner, Pawel; Pfitzner, Bjarne; Arnrich, Bert (2021). 1–6.
[ BibTeX ] [ URL ] [ DOI ]
 
@inproceedings{9524284, 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 bertarnrich bjarnepfitzner justinalbert}, pages = {1-6}, title = {Data Augmentation of Kinematic Time-Series From Rehabilitation Exercises Using GANs}, year = 2021 }

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 = {bertarnrich justinalbert linzhou}, 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 = {bertarnrich justinalbert}, 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 }

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 }