Enhancing physical fitness testing: integrating Bluetooth connectivity for improved hand grip dynamometry
BACKGROUND: Manual physical fitness testing devices have inherent limitations in precision, accuracy, efficiency, and data analysis capabilities, potentially compromising result quality and reliability. The aims of the study were to advance physical fitness testing by integrating Bluetooth connectivity into a digital hand grip dynamometer.METHODS: The study comprised two phases. Phase 1 focused on adapting the Bluetooth transmitter box to synchronize with the hand grip dynamometer, displaying data on a mobile app. Phase 2 evaluated the reliability between the modified and original dynamometer versions. A cohort of 30 sports science students (15 males, 15 females), aged 18-22, performed squeeze force measurements using the digital hand grip dynamometer. Assessments were conducted thrice for both hands, followed by a trial using the original dynamometer after a 10-minute break. The results revealed that integrating Bluetooth into the dynamometer simplified access to squeeze force data.RESULTS: The adapted dynamometer featured a microcontroller unit (MCU) with a variable resistor, transmitting processed data to a dedicated mobile app. A strong positive correlation (r=0.951) between the modified and original versions highlighted the modified dynamometer’s reliability in squeeze force measurement.CONCLUSIONS: Integrating Bluetooth into the hand grip dynamometer holds promise, broadening its utility across various domains. This innovation can overcome limitations of manual testing devices, enhancing usability and data analysis. Continued research and refinement may advance hand strength measurement. This work sets the stage for future exploration, extending its impact beyond fitness testing into diverse applications.
Authors
- Tanida Julvanichpong (ORCID: https://orcid.org/0000-0002-8324-4056)
- Chatkamon Singnoy
- Sanchai EARDPRAB
Institutions
- Burapha University (TH)
Publication Details
- Journal
- Gazzetta Medica Italiana Archivio per le Scienze Mediche
- Published
- 2024-09-01
- DOI
- https://doi.org/10.23736/s0393-3660.23.05303-2
- Primary Topic
- Context-Aware Activity Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00