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Article

Sensing System for Plegic or Paretic Hands Self-Training Motivation †

1
Institute of Automatic Control, Lodz University of Technology, Stefanowskiego 18, 90-537 Lodz, Poland
2
Miejskie Centrum Medyczne im. dr Karola Jonschera, Milionowa 14, 93-113 Lodz, Poland
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Dominik, I.; Prączko-Pawlak, E.; Zubrycki, I. Motivating wearable device for plegic hand rehabilitation. In Proceedings of the 2021 Signal Processing Symposium (SPSympo), Lodz, Poland, 20–23 September 2021.
Sensors 2022, 22(6), 2414; https://doi.org/10.3390/s22062414
Submission received: 31 December 2021 / Revised: 6 March 2022 / Accepted: 7 March 2022 / Published: 21 March 2022

Abstract

Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2—a system intended to support self-exercising, remind about it, and motivate patients. We propose a glove-based device with an on-device machine-learning-based exercise scoring, a tablet-based interface, and a web-based application for therapists. The feasibility of on-device inference and the accuracy of correct exercise classification was evaluated on four healthy participants. Whole system use was described in a case study with a patient with a paretic hand. The anomaly classification has an accuracy of 91.3% and f1 value of 91.6% but achieves poorer results for new users (78% and 81%). The case study showed that patients had a positive reaction to exercising with Przypominajka, but there were issues relating to sensor glove: ease of putting on and clarity of instructions. The paper presents a new way in which sensor systems can support the rehabilitation of after-stroke patients with an on-device machine-learning-based classification that can accurately score and contribute to patient motivation.
Keywords: stroke; stroke rehabilitation; paresis; plegia; wearable device; sensor glove; sensor system; Internet of Medical Things stroke; stroke rehabilitation; paresis; plegia; wearable device; sensor glove; sensor system; Internet of Medical Things

Share and Cite

MDPI and ACS Style

Zubrycki, I.; Prączko-Pawlak, E.; Dominik, I. Sensing System for Plegic or Paretic Hands Self-Training Motivation. Sensors 2022, 22, 2414. https://doi.org/10.3390/s22062414

AMA Style

Zubrycki I, Prączko-Pawlak E, Dominik I. Sensing System for Plegic or Paretic Hands Self-Training Motivation. Sensors. 2022; 22(6):2414. https://doi.org/10.3390/s22062414

Chicago/Turabian Style

Zubrycki, Igor, Ewa Prączko-Pawlak, and Ilona Dominik. 2022. "Sensing System for Plegic or Paretic Hands Self-Training Motivation" Sensors 22, no. 6: 2414. https://doi.org/10.3390/s22062414

APA Style

Zubrycki, I., Prączko-Pawlak, E., & Dominik, I. (2022). Sensing System for Plegic or Paretic Hands Self-Training Motivation. Sensors, 22(6), 2414. https://doi.org/10.3390/s22062414

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