A syntactic two-component encoding model for the trajectories of human actions.

Human actions have been widely studied for their potential application in various areas such as sports, pervasive patient monitoring, and rehabilitation. However, challenges still persist pertaining to determining the most useful ways to describe human actions at the sensor, then limb and complete action levels of representation and deriving important relations between these levels each involving their own atomic components. In this paper, we report on a motion encoder developed for the sensor level based on the need to distinguish between the shape of the sensor's trajectory and its temporal characteristics during execution. This distinction is critical as it provides a different encoding scheme than the usual velocity and acceleration measures which confound these two attributes of any motion. At the same time, we eliminate noise from sensors by comparing temporal and spatial indexing schemes and a number of optimal filtering models for robust encoding. Results demonstrate the benefits of spatial indexing and separating the shape and dynamics of a motion, as well as its ability to decompose complex motions into several atomic ones. Finally, we discuss how this specific type of sensor encoder bears on the derivation of limb and complete action descriptions.

Multi-kinect skeleton fusion for physical rehabilitation monitoring.

Kinect has been increasingly applied in rehabilitation as a motion capture device. However, the inherent limitations significantly hinder its further development in this important area. Although a number of Kinect fusion approaches have been proposed, only a few of them was actually considered for rehabilitation. In this paper, we propose to fuse information from multiple Kinects to achieve this. Given the specific scenario of users suffering from limited range of movements, we propose to calibrate depth cameras in multiple Kinects with 3D positions of joints on a human body rather than in a checkerboard pattern, so that patients are able to calibrate Kinects without extra support. Kalman filter is applied for skeleton-wise Kinect fusion since skeleton data (3D positions of joints) and its derivatives are preferred by physiotherapists to evaluate the exercise performance of patients. Various preliminary experiments were conducted to illustrate the accuracy of proposed calibration and fusion approach by comparing with a commercial Vicon system®, confirming the practical use of the system in rehabilitation exercise monitoring.

Further applications of Doppler radar for non-contact respiratory assessment.

This paper further investigates the use of Doppler radar for detecting and identifying certain human respiratory characteristics from observed frequency and phase modulations. Specifically, we show how breathing frequencies can be determined from the demodulated signal leading to identifying abnormalities of breathing patterns using signal derivatives, optimal filtering and standard statistical measures. Specifically, we report results on a robust method for distinguishing cessation of the normal breathing cycle. The proposed approach can have potential application in the management of sudden infant death syndrome(SIDS) and sleep apnea.