Activity Recognition using RF and IMU Sensor Data Fusion

In this paper, we evaluate the potential of radio frequency data present in Bluetooth Low Energy(BLE) wireless signals to complement and improve Human Activity Recognition (HAR). HAR is prevalent in several applications catering to health, fitness, and well-being, driving the demand for robust, low-cost and efficient sensing systems. We utilized on-body wireless sensors to leverage the received signal strength indicator (RSSI) and inertial measurement unit (IMU) information to gain deeper insight into fine-grain relative positioning that reflects on hard-to-distinguish motion patterns. Our analysis shows detection accuracy of 91.91% with IMU only for up to 13 different health and fitness activities, which can be improved up to 94.54% when combining RF data with the IMU data. The data was collected with 42 participants wearing 5 wireless sensors.