Year: 2019
Type: article
Abstract: 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 singledevice 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=<0.05.The performance of the generalized classifier indicates that the device could classify arm motions on a user without prior training.
Source: Summit (Simon Fraser University)
Author: Jordan Lui
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3016827797