Abstract
Movements of a person can be recorded with a mobile camera and visualized as sequences of stick figures for assessments in health and elderly care, physio-therapy, and sports. However, since the visualizations flicker due to noisy input data, the visualizations themselves and even whole assessment applications are not trusted in general. The present paper evaluates different filters for smoothing the movement visualizations but keeping their validity for a visual physio-therapeutic assessment. It evaluates variants of moving average, high-pass, and Kalman filters with different parameters. Moreover, it presents a framework for the quantitative evaluation of smoothness and validity. As these two criteria are contradicting, the framework also allows to weight them differently and to automatically find the correspondingly best-fitting filter and its parameters. Different filters can be recommended for different weightings of smoothness and validity. The evaluation framework is applicable in more general contexts and with more filters than the three filters assessed. However, as a practical result of this work, a suitable filter for stick figure visualizations in a mobile application for assessing movement quality could be selected and used in a mobile app. The application is now more trustworthy and used by medical and sports experts, and end customers alike.
Highlights
Automated sensor-based assessment of human movements has many applications ranging from the diagnosis and therapy of musculo-skeletal insufficiency, to elderly care, and to high performance sport
We suggest an approach based on Principle Component Analysis (PCA) to check whether the main movement signal is maintained while the noise is filtered
As we do not have any ground truth validity, any validity score can only be an approximation of the actual property. We suggest such a validity score based on principle component analysis (PCA)
Summary
Automated sensor-based assessment of human movements has many applications ranging from the diagnosis and therapy of musculo-skeletal insufficiency, to elderly care, and to high performance sport. We contribute to camera-based human movement assessment. Kumar et al [21] recorded gait data simultaneously using motion sensors and visible-light cameras and fused the data to accurately classify different types of walks. Guzov et al [14] used IMUs attached at the body limbs and a head mounted camera looking outwards, to fuse camera based self-localization with IMU-based human body tracking. These multi-modal approaches can reduce measurement errors and noise coming from each individual channel
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