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)
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.
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AbstractThe 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.
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).
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).
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.
Weitere Informationen
AbstractGait 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.
