Acceleration analysis and detection algorithm for burpees

• Published in: Journal of Physical Education and Sport Vol. 24; no. 5; pp. 1066 - 1073
• Main Authors: Šiška, Ľuboslav, Židek, Daniel, Tkačik, Štefan, Balint, Gheorghe
• Format: Journal Article
• Language: English
• Published: Pitesti Universitatea din Pitesti 01.05.2024
• Subjects: Academic Achievement; Acceleration (Education); Accelerometers; Algorithms; Cellular telephones; Computers; Data Processing; Diagnostic Tests; Exercise intensity; Instructional Effectiveness; Mathematics; Physical Activities; Physical fitness; Physics; Repetition; Sensors; Sport science; Video Technology
• ISSN: 2247-8051; 2247-806X
• DOI: 10.7752/jpes.2024.05122

With the advancement of technology, it is now possible to accurately record and evaluate various physical exercises. In this study we measured acceleration values during the burpee exercise, developed an algorithm to detect it based on recorded data, and assessed its intensity. Ten test subjects (aged 21.HL60 years, height 181.6±10.69 cm and weight 76±12.30 kg) participated in research, performing a total of 60 burpees (6 each) to a sound signal at 7 seconds intervals. The acceleration values were collected using the PHYPHOX mobile app. with the phone attached to the test subject's left arm just below the deltoid muscle. Data from the application were transferred a computer screen, positioned next to the test subject, and then exported to MS Excel. The highest acceleration values of up to 62 ms-2 were achieved in the first phase of the exercise when the palms hit the floor during the transition to the push-up. Slightly lower acceleration values were recorded during the transition from the push-up to the squat and when landing on the ground after a jump. The extension of the legs after landing on the ground was marked as the end of exercise. We used the moving average smoothing method to filter the values from the individual English squats. The beginning of the exercise was identified by the smoothed acceleration value exceeding the limit of 0.5 and not falling below this limit for the next 200 values. The end of the exercise was identified in a similar way, but from the opposite side: the acceleration value had to exceed the limit value of 3 msA(-2) and the next 200 values did not fall below this threshold. This way, we managed to identify the necessary data with 99% accuracy. We expressed the intensity of the Burpee as an average of the corresponding values, and it ranged from 4.06 to 11.37 msA(-2). The research results will be used to diagnose specific motor performance.