Efficient and Robust Skeleton-Based Quality Assessment and Abnormality Detection in Human Action Performance

Elderly people can be provided with safer and more independent living by the early detection of abnormalities in their performing actions and the frequent assessment of the quality of their motion. Low-cost depth sensing is one of the emerging technologies that can be used for unobtrusive and inexpensive motion abnormality detection and quality assessment. In this study, we develop and evaluate vision-based methods to detect and assess neuromusculoskeletal disorders manifested in common daily activities using three-dimensional skeletal data provided by the SDK of a depth camera (e.g., MS Kinect and Asus Xtion PRO). The proposed methods are based on extracting medically-justified features to compose a simple descriptor. Thereafter, a probabilistic normalcy model is trained on normal motion patterns. For abnormality detection, a test sequence is classified as either normal or abnormal based on its likelihood, which is calculated from the trained normalcy model. For motion quality assessment, a linear regression model is built using the proposed descriptor in order to quantitatively assess the motion quality. The proposed methods were evaluated on four common daily actions-sit to stand, stand to sit, flat walk, and gait on stairs-from two datasets, a publicly released dataset and our dataset that was collected in a clinic from 32 patients suffering from different neuromusculoskeletal disorders and 11 healthy individuals. Experimental results demonstrate promising results, which is a step toward having convenient in-home automatic health care services.