Implementation of a Wrist-Worn Wireless Sensor System with Machine Learning-Based Classification for Indoor Human Tracking

This work presents the development of a wrist-worn wireless sensor system for high-accuracy indoor human zone tracking. The proposed system employs machine learning techniques to combine data from multiple sources, including a Received Signal Strength Indicator (RSSI) from wireless signals, three-axis acceleration, and three-axis angular velocity. A prototype wearable wireless sensor device was implemented using a SparkFun Thing Plus-XBee3 microcontroller supporting the Zigbee/IEEE 802.15.4 standard at 2.4 GHz, integrated with a six-degree-of-freedom IMU sensor (MPU-6050). Experiments using one wrist-worn sensor as a transmitter and one base station as a receiver were conducted in a two-story residential building environment covering three zones (i.e., staircase area, living room, and dining room) under static and dynamic test scenarios. Classification performances of 33 machine learning classifiers with different data feature groups and window sizes were evaluated. The results demonstrate the achievement of wrist-worn wireless sensor system development. The system exhibits high communication reliability with a packet delivery ratio (PDR) of 99.99% and can efficiently track data signals in real time. Results indicate that using only raw RSSI data achieves 75.0% accuracy in classifying human zones. However, when statistical RSSI features and accelerometer data fusion are applied, accuracies significantly increase to 98.7% (static scenario, wide neural network with a window size of 25) and 99.6% (dynamic scenario, Fine k-NN). These results demonstrate the system’s potential for indoor human tracking applications.