| We describe a body sensor system that detects human activities in real-time. The system consists of wearable computers known as sensor nodes (motes) that can sense information, process them and transmit the results to a Personal Device like Smart phone, PDA or Personal Computer. The motes are attached to different parts of the human body, namely waist and right thigh. Daily living activity monitoring is important in improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing for time-series pattern matching because of its robustness to variations in time domain and speed as opposed to other template matching methods such as Euclidean Distance. Despite of this flexibility, for the application of activity recognition, DTW can only find the similarity between template of a movement and the incoming samples, when the location and orientation of sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to false classifications. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand. To measure this performance of DTW, we need infinite closely spaced sensors which are impractical. To deal with this problem, we use the marker based optical motion capture system and generate inertial sensor data for different location and orientation on the body. We study the performance of the DTW under these conditions and determine the worst-case sensor location variations, the algorithm can accommodate. |
