Design of a wearable to improve single-device motion classification of arm motions

Inertial Measurement Unit (IMU) based wearable sensors have found common use to track arm activity in daily life. However, classifying a high number of arm motions with single IMU-based systems remains a challenging task. In this study, we propose a single-device wearable which incorporates a thermal sensor and an inertial sensor. The system was evaluated in a study incorporating 11 healthy participants, where 24 different arm motions were recorded and predicted with a machine learning classifier. This study found that 24 arm motions could be classified with 93.55% accuracy. Further, the passive infrared thermal sensor significantly increased classification accuracy from 75% to 93.55% , p=