Design of a Wireless Multiple-Mode Human Behavior Evaluation System

Image based motion tracking of human skeletons is widely used in biomechanical research, while electromyography (EMG) contains direct reflections of the muscle activities. A combination of image and EMG data enables a more effective way to quantitatively evaluate the recover level of patients with movement disorders, such as post-stroke patients. This paper proposed a wireless multiple-mode human behavior evaluation system enables simultaneously acquisition of multiple image sensor data and acquisition of EMG data from different muscles. Several wireless wearable EMG sensor nodes are attached to different muscles-of-interest over the body. Each EMG sensor node features an individual acquisition channel. The collected data are transferred through Bluetooth Low Energy. Each EMG node features a size of 2cm × 3cm × 1cm with a weight of 16 gram and a battery life of 20 hours. Two Wi-Fi based wireless image sensors are used. A convolutional neural network based human pose estimation method is proposed to track the skeleton motion features from the captured streams. The fully wireless nature guarantees the usability and flexibility of the proposed system. A data collection and remote analysis platform is also developed for remote support. The proposed system has been tested on patients recovering from stroke.