Activity Detection Using RF and Sensor Data Fusion

 This master’s thesis addresses the need to enhance activity recognition for monitoring and guidance in health and wellness applications. Despite the growing expectations, the challenge lies within effectively gathering user activity data. This study aims to assess the ability of RF (Radio Frequency) data available in smart wireless devices and or wearable devices to improve HAR (Human Activity Recognition) by leveraging the RSSI (Received Signal Strength Indicator) information. The study was performed using on-body wireless sensor nodes provided by the Bosch Research Team. Unlike previous approaches that have utilized RF data to determine coarse grain location tracking, this study emphasizes leveraging the RF data from wireless protocols such as BLE (Bluetooth Low Energy) to give valuable insights into fine grain relative positioning that help to understand precise human biomechanical motions. This novel approach focuses on utilizing the existing infrastructure present in wireless devices to improve human activity recognition which could be helpful for fitness and wellness-related applications. It also has the potential to reduce power usage by enabling motion sensors to be used less. The preliminary results conducted by 25-40 participants prove that there is valuable information given by RF data resulting in a 30% reduction in errors.