Next Article in Journal
CoSOV1Net: A Cone- and Spatial-Opponent Primary Visual Cortex-Inspired Neural Network for Lightweight Salient Object Detection
Next Article in Special Issue
The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches
Previous Article in Journal
Mobility-Aware Resource Allocation in IoRT Network for Post-Disaster Communications with Parameterized Reinforcement Learning
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Sollerman Hand Function Sub-Test “Write with a Pen”: A Computer-Vision-Based Approach in Rehabilitation Assessment

by
Orestis N. Zestas
and
Nikolaos D. Tselikas
*
Communication Networks and Applications Laboratory (CNALab), Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, Greece
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(14), 6449; https://doi.org/10.3390/s23146449
Submission received: 12 June 2023 / Revised: 12 July 2023 / Accepted: 13 July 2023 / Published: 17 July 2023

Abstract

Impaired hand function is one of the most frequently persistent consequences of stroke. Throughout the rehabilitation process, physicians consistently monitor patients and perform kinematic evaluations in order to assess their overall progress in motor recovery. The Sollerman Hand Function Test (SHT) is a valuable assessment tool used to evaluate a patient’s capacity to engage in daily activities. It holds great importance in the field of medicine as it aids in the assessment of treatment effectiveness. Nevertheless, the requirement for a therapist’s physical presence and the use of specialized materials make the test time-consuming and reliant on clinic availability. In this paper, we propose a computer-vision-based approach to the “Write with a pen” sub-test, originally included in the SHT. Our implementation does not require extra hardware equipment and is able to run on lower-end hardware specifications, using a single RGB camera. We have incorporated all the original test’s guidelines and scoring methods into our application, additionally providing an accurate hand spasticity evaluator. After briefly presenting the current research approaches, we analyze and demonstrate our application, as well as discuss some issues and limitations. Lastly, we share some preliminary findings from real-world application usage conducted at the University campus and outline our future plans.
Keywords: sollerman hand function test; computer vision; rehabilitation assessment; upper-limb rehabilitation; fine motor skills; shape detection sollerman hand function test; computer vision; rehabilitation assessment; upper-limb rehabilitation; fine motor skills; shape detection

Share and Cite

MDPI and ACS Style

Zestas, O.N.; Tselikas, N.D. Sollerman Hand Function Sub-Test “Write with a Pen”: A Computer-Vision-Based Approach in Rehabilitation Assessment. Sensors 2023, 23, 6449. https://doi.org/10.3390/s23146449

AMA Style

Zestas ON, Tselikas ND. Sollerman Hand Function Sub-Test “Write with a Pen”: A Computer-Vision-Based Approach in Rehabilitation Assessment. Sensors. 2023; 23(14):6449. https://doi.org/10.3390/s23146449

Chicago/Turabian Style

Zestas, Orestis N., and Nikolaos D. Tselikas. 2023. "Sollerman Hand Function Sub-Test “Write with a Pen”: A Computer-Vision-Based Approach in Rehabilitation Assessment" Sensors 23, no. 14: 6449. https://doi.org/10.3390/s23146449

APA Style

Zestas, O. N., & Tselikas, N. D. (2023). Sollerman Hand Function Sub-Test “Write with a Pen”: A Computer-Vision-Based Approach in Rehabilitation Assessment. Sensors, 23(14), 6449. https://doi.org/10.3390/s23146449

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop