Accurate On-Body Distance Estimation Using BLE RSSI and IMU Sensor Fusion

Abstract

It is useful to estimate on-body distances for human motion tracking and rehabilitation monitoring. We perform accurate short-range ($< $1 m) distance estimation by fusing bluetooth low energy received signal strength indicator (RSSI) with inertial measurement unit (IMU) orientation data. We developed a custom wearable system using ESP32 microcontrollers and IMUs and conducted a study with N = 6 participants performing a series of defined arm movements. We trained a long short-term memory (LSTM) model, termed QuaternionRNN, which leverages temporal sequences of normalized quaternions and RSSI to predict the distance between sensors on the waist and wrist. The model achieved a median absolute distance error of ~3 cm on a generalized dataset, and between 4 and 7 cm in subjectwise cross-validation. We compared this to unscented Kalman filters with RSSI alone and RSSI plus orientation, and an RSSI-only LSTM, and found QuaternionRNN to have 2–4x lower errors. This work establishes a cost-effective and accurate solution for short-range nonline-of-sight distance sensing.