Analyzing optimal muscle dynamics during handstands: a comprehensive investigation of skilled gymnasts

Abstract

This study aimed to evaluate the muscle dynamics involved in single and double-arm handstands performed by five skilled gymnasts, with a mean age of 23.6 ± 1.94 years. Myoware Muscle Sensor (AT-04-001) (MMS) signals were collected from three key upper limb muscles: Wrist Flexor (WF), Triceps Brachii (TB), and Lateral Deltoid (LD), all on the dominant side of the body. To facilitate data collection and processing, Internet of Things (IoT) programs were employed, utilizing Arduino IDE and Python 3.10.0 applications to connect the NodeMCU (ESP8266) via server and client code. The NodeMCU's display frequency range was set at 0–1024 Hz, with a Baud (Bd) rate of 9600 in Arduino, which is a crucial parameter for visualizing the data accurately. It continuously reads the serial and plotter monitor signals from the MMS through the A0 pin. The processed data was transmitted wirelessly by the NodeMCU, displayed on a monitor, and recorded for analysis. Participants executed three trials of their maximum handstand performance on the floor; the gymnasts peak level of 15 seconds performance was used for analysis (i.e., the middle time of the best trial; e.g., maximum performance was 45 seconds in this 16–30sec used). The observed muscle stimulation range was from a minimum of 109617 Hz to a maximum of 151292 Hz. Overall, one-arm handstands (52.79%) demonstrated better muscle activation than double-arm handstands (47.21%). The analysis revealed a statistically significant positive Correlation (r) and Probability (p) between the WF and LD (r = 0.870, p = 0.001). However, no significant correlations were observed between the WF and the TB (r = 0.507, p = 0.134) or the TB and the LD (r = 0.491, p = 0.150). To conclude, despite comparable inter-muscle contributions, the study suggests a significant association between WF and LD. Future research can build on these findings, exploring additional muscle groups and refining training protocols for performance optimization and injury prevention. This study encourages broader investigations, including more muscles in the upper and lower body, with advanced research laboratories and larger sample sizes.