IoT-Enabled Instrumented Glove for Real-Time Monitoring of Finger Pinch Strength

Quantifying finger strength during Activities of daily living (ADLs) and the improvements observed in individuals undergoing physical therapy due to stroke or arthritis can provide valuable insights into hand function. The developed real-time IoT-based glove for finger strength monitoring is an instrumented device that incorporates Internet of Things technology. This includes utilizing an ESP8266 Wi-Fi module to connect to the selected IoT analytics platform, Blynk, and an Arduino microcontroller as the main control unit. The glove features a 16x2 LCD display to present the user's pinch strength values for the thumb and index finger. Force-sensitive resistor (FSR) sensors were employed to assess this parameter. The measured strength values were initially displayed on the LCD and then transmitted to a Blynk server for real-time graphical representation. The effectiveness of the designed glove was evaluated through participant testing, where individuals wore the glove and performed pinching tasks. Comparisons were made between the pinch strength readings from the instrumented glove and a traditional pinch meter. The sensing results demonstrated a promising degree of accuracy, with a minimum and highest average mean percentage error of 8.23% and 14.08%, respectively. The developed IoT-based pinch strength monitoring glove have the potential to enable physiotherapists to quantitatively evaluate progress in patient pinch strength during object manipulation tasks requiring pinching. Furthermore, the success of rehabilitation programs can also be assessed using this system.

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Publication Details

Journal
Journal of Advanced Research in Applied Sciences and Engineering Technology
Published
2024-12-17
DOI
https://doi.org/10.37934/araset.68.2.207217
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
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article

IoT-Enabled Instrumented Glove for Real-Time Monitoring of Finger Pinch Strength

Nurulaqilla Khamis, Noraishikin Zulkarnain, Ahmad Athif Mohd Faudzi, Ili Najaa Aimi Mohd Nordin et al.
Journal of Advanced Research in Applied Sciences and Engineering Technology
Hand Gesture Recognition Systems
article

IoT-Enabled Instrumented Glove for Real-Time Monitoring of Finger Pinch Strength

Nurulaqilla Khamis, Noraishikin Zulkarnain, Ahmad Athif Mohd Faudzi, Ili Najaa Aimi Mohd Nordin, Tariq Rehman, Sri Abinesh Pillal Murugesh
article en

Abstract

Quantifying finger strength during Activities of daily living (ADLs) and the improvements observed in individuals undergoing physical therapy due to stroke or arthritis can provide valuable insights into hand function. The developed real-time IoT-based glove for finger strength monitoring is an instrumented device that incorporates Internet of Things technology. This includes utilizing an ESP8266 Wi-Fi module to connect to the selected IoT analytics platform, Blynk, and an Arduino microcontroller as the main control unit. The glove features a 16x2 LCD display to present the user's pinch strength values for the thumb and index finger. Force-sensitive resistor (FSR) sensors were employed to assess this parameter. The measured strength values were initially displayed on the LCD and then transmitted to a Blynk server for real-time graphical representation. The effectiveness of the designed glove was evaluated through participant testing, where individuals wore the glove and performed pinching tasks. Comparisons were made between the pinch strength readings from the instrumented glove and a traditional pinch meter. The sensing results demonstrated a promising degree of accuracy, with a minimum and highest average mean percentage error of 8.23% and 14.08%, respectively. The developed IoT-based pinch strength monitoring glove have the potential to enable physiotherapists to quantitatively evaluate progress in patient pinch strength during object manipulation tasks requiring pinching. Furthermore, the success of rehabilitation programs can also be assessed using this system.

Journal of Advanced Research in Applied Sciences and Engineering Technology
NED University of Engineering and Technology (PK), University of Technology Malaysia (MY), National University of Malaysia (MY), Tun Hussein Onn University of Malaysia (MY)
Openalex Percentile: Top 72%
Hand Gesture Recognition Systems
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