Research Article
Wireless Sensor Networks for Telerehabilitation of Parkinson’s Disease Using Rhythmic Auditory Stimulation
@INPROCEEDINGS{10.1007/978-3-030-99194-4_10, author={Stephen John Destura and Glorie Mae Mabanta and John Audie Cabrera and Jhoanna Rhodette Pedrasa}, title={Wireless Sensor Networks for Telerehabilitation of Parkinson’s Disease Using Rhythmic Auditory Stimulation}, proceedings={Pervasive Computing Technologies for Healthcare. 15th EAI International Conference, Pervasive Health 2021, Virtual Event, December 6-8, 2021, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2022}, month={3}, keywords={Parkinson’s disease Wireless sensor network Rhythmic auditory stimulation}, doi={10.1007/978-3-030-99194-4_10} }- Stephen John Destura
Glorie Mae Mabanta
John Audie Cabrera
Jhoanna Rhodette Pedrasa
Year: 2022
Wireless Sensor Networks for Telerehabilitation of Parkinson’s Disease Using Rhythmic Auditory Stimulation
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-030-99194-4_10
Abstract
Parkinson’s Disease (PD) is a neurodegenerative disease affecting mainly the elderly. Patients affected by PD may experience slowness of movements, loss of automatic movements, and impaired posture and balance.
Physical therapy is highly recommended to improve their walking where therapists instruct patients to perform big and loud exercises. Rhythmic Auditory Stimulation (RAS) is a method used in therapy where external stimuli are used to facilitate movement initiation and continuation.
Aside from face-to-face therapy sessions, home rehabilitation programs are used by PD patients with mobility issues and who live in remote areas. Telerehabilitation is a growing practice amid the COVID-19 pandemic.
This work describes the design and implementation of a wireless sensor network to remotely and objectively monitor the rehabilitation progress of patients at their own homes. The system, designed in consultation with a physical therapist, includes insole sensors which measure step parameters, a base station as a phone application which facilitates RAS training sessions and communication interface between the therapist and patients, and an online server storing all training results for viewing. Step data from the system’s real-time analysis were validated against post-processed and reconstructed signals from the raw sensor data gathered across different beats. The system has an accuracy of at least 80% and 72% for the total steps and correct steps respectively.
