Research Article
A novel motion monitoring system for activities of daily living
@ARTICLE{10.4108/eai.21-3-2017.152392, author={X. He and A. Farajidavar}, title={A novel motion monitoring system for activities of daily living}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={3}, number={9}, publisher={EAI}, journal_a={PHAT}, year={2017}, month={3}, keywords={inertial measurement unit, motion monitoring, activities of daily living.}, doi={10.4108/eai.21-3-2017.152392} }
- X. He
A. Farajidavar
Year: 2017
A novel motion monitoring system for activities of daily living
PHAT
EAI
DOI: 10.4108/eai.21-3-2017.152392
Abstract
Capability to perform activities of daily living (ADLs) is a major factor in quality of life (QOL). While it can be difficult for the elderly, disabled, or patients with chronic diseases to deal with ADLs, they need to spend a great deal of money on healthcare and assistive technologies to keep a good QOL. The situation can be improved if a real-time ADLs monitoring and recognition system is available to provide health information to physicians, pharmacists, or caregivers to offer timely diagnosis, prescription, or emergency reaction. We have developed a wireless wearable motion monitoring system that is suitable for monitoring ADLs involving limbs. The system consists of six Bluetooth low energy (BLE) transponders that are small and light enough to be mounted on all limbs. Each transponder, called SensorTag (by Texas Instruments), is equipped with a tri-axial accelerometer, a tri-axial magnetometer, and a tri-axial gyroscope for motion monitoring. Each SensorTag can be linked to a smartphone for long-term outdoor monitoring. A graphic user interface is created to acquire signals from BLE receivers, display the sig-nals in real-time, process data, and store for off-line analysis. This system was tested in three scenarios, and signals were analyzed off-line using a quaternion-based motion recon-struction algorithm. First, a SensorTag was examined against a marker-based motion capture system in a linear motion test. Second, a SensorTag was worn on a subject’s wrist to monitor food-intake trajectory. Finally, six SensorTags were worn on wrist, knee, and ankle joints of left and right hands to monitor gait on a straight path. Results showed various er-ror rates in different scenarios, however, the error rates are within an acceptable range, and more importantly the patterns of the motions are reproducible.
Copyright © 2017 X. He and A. Farajidavar, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduc-tion in any medium so long as the original work is properly cited.