A wearable system for long-term ubiquitous monitoring of common motor symptoms in patients with Parkinson's disease
Abstract
PERFORM is a system for the monitoring, assessment and management of patient with Parkinson's disease (PD). It comprises of three subsystems: (i) Multi-Sensor Monitoring Unit, (ii) the Local Base Unit, and (iii) the Centralized Hospital unit. The wearable, multi-sensor monitoring unit (WMSMU) of the PERFORM system is presented in this work. This unit plays an essential role in the overall PERFORM system since it is responsible to record and pre-process accelerometer and gyroscope signals that are later used by the various components of the Local Base Unit in order to classify and quantify the symptoms and motor status of the PD patients. The WMSMU was evaluated in a large set of pilot studies in PD patients.
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The PERFORM system is a wearable platform for Parkinson's disease remote monitoring and management, based on a Body Area Network (BAN) of sensors.
“This system, called PERFORM, is based on a Body Area Network (BAN) of sensors which has already been validated both from the technical and clinical point for view.”
From Wearability Assessment of a Wearable System for Parkinson’s Disease Remote Monitoring Based on a Body Area Network of Sensors (2014) · cites this paper as [15]
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How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review
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(2014) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2014.6864288
- Authors
- Tsipouras, M. G., Tzallas, A. T., Karvounis, E. C., Tsalikakis, D. G., Cancela, J., Pastorino, M., Waldmeyer, M. T. A., Konitsiotis, S., & Fotiadis, D. I.
- Affiliations
- University of Ioannina (GR)
- University of Western Macedonia (GR)
- Universidad Politécnica de Madrid (ES)
- ORCID
- Venue
- IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
- Publisher
- IEEE
- Published
- 2014-06
- Volume
- 54
- Pages
- 173-176
- ISSN
- 2641-3590 (ISSN-L)
- OpenAlex
- W2060221488
- Cited by
- 13
- References
- 7
- Citations by year
- 2020: 3 · 2018: 1 · 2017: 2 · 2016: 1 · 2015: 3 · 2014: 3
- FWCI
- 1.61
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- Parkinson's disease, wearable sensors, accelerometer, gyroscope, motor symptom monitoring, ubiquitous health monitoring, symptom classification
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