Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions.

Wearable motion sensors are assumed to be correctly positioned and oriented in most of the existing studies. However, generic wireless sensor units, patient health and state monitoring sensors, and smart phones and watches that contain sensors can be differently oriented on the body. The vast majority of the existing algorithms are not robust against placing the sensor units at variable orientations.

Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors.

Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data.

Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals.

We develop an autonomous system to detect and evaluate physical therapy exercises using wearable motion sensors. We propose the multi-template multi-match dynamic time warping (MTMM-DTW) algorithm as a natural extension of DTW to detect multiple occurrences of more than one exercise type in the recording of a physical therapy session. While allowing some distortion (warping) in time, the algorithm provides a quantitative measure of similarity between an exercise execution and previously recorded templates, based on DTW distance.