Recognizing specific errors in human physical exercise performance with Microsoft Kinect

Abstract

Recognizing specific errors in human physical exercise performance with Microsoft Kinect Ryan StaabThe automatic assessment of human physical activity performance is useful for a number of beneficial systems including in-home rehabilitation monitoring systems and Reactive Virtual Trainers (RVTs). RVTs have the potential to replace expensive personal trainers to promote healthy activity and help teach correct form to prevent injury.Additionally, unobtrusive sensor technologies for human tracking, especially those that incorporate depth sensing such as Microsoft Kinect, have become effective, affordable, and commonplace.The work of this thesis contributes towards the development of RVT systems by using RGB-D and tracked skeletal data collected with Microsoft Kinect to assess human performance of physical exercises. I collected data from eight volunteers performing three exercises: jumping jacks, arm circles, and arm curls. I labeled each exercise repetition as either correct or one or more of a select number of predefined erroneous forms. I trained a statistical model using the labeled samples and developed a system that recognizes specific structural and temporal errors in a test set of unlabeled samples.

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“…Then, they classify the motion using hierarchical SVM. Staab also implements SVM and also SVM with sigmoid Kernel to train some motion exercises since each exercise has a unique distribution in feature space [33]. Thus, concludes that a model that performs well for one exercise might not be suitable for another.…”

“…In brief, skeleton joints can provide reliable joint coordinates to the users with its real-time skeleton estimation algorithm. Its also has drawn a great attention [33][34][35][36][37][38][39][40][41][42][43], as it brings a great robustness to illumination, clustered background, and camera motion. II.…”

HMM and DTW for evaluation of therapeutical gestures using kinect

“…Different approaches have been proposed to deal with the evaluation problem. Staab [3] defines a limited set of errors for each of the movements considered, and trains Support Vector Machines to detect these errors using data coming from a Kinect sensor. This approach however is hardly scalable, due to the difficulty in obtaining data for all the possible mistakes that can arise while performing the movements.…”