The value of heart rate variability (HRV) in the fields of health, disease, and exercise science has been established through numerous investigations. The typical mobile-based HRV device simply records interbeat intervals, without differentiation between noise or arrythmia as can be done with an electrocardiogram (ECG). The intent of this report is to validate a new single channel ECG device, the Movesense Medical sensor, against a conventional 12 channel ECG. A heterogeneous group of 21 participants performed an incremental cycling ramp to failure with measurements of HRV, before (PRE), during (EX), and after (POST). Results showed excellent correlations between devices for linear indexes with Pearson’s r between 0.98 to 1.0 for meanRR, SDNN, RMSSD, and 0.95 to 0.97 for the non-linear index DFA a1 during PRE, EX, and POST. There was no significant difference in device specific meanRR during PRE and POST. Bland–Altman analysis showed high agreement between devices (PRE and POST: meanRR bias of 0.0 and 0.4 ms, LOA of 1.9 to −1.8 ms and 2.3 to −1.5; EX: meanRR bias of 11.2 to 6.0 ms; LOA of 29.8 to −7.4 ms during low intensity exercise and 8.5 to 3.5 ms during high intensity exercise). The Movesense Medical device can be used in lieu of a reference ECG for the calculation of HRV with the potential to differentiate noise from atrial fibrillation and represents a significant advance in both a HR and HRV recording device in a chest belt form factor for lab-based or remote field-application.

The interplay between biarticular and monoarticular muscles of the knee and hip joints during bipedal squats (SQBP) requires adequate central‐nervous control mechanisms to enable smooth and dynamic movements. Here, we investigated motor control between M. vastus medialis (VM), M. vastus lateralis (VL), and M. rectus femoris (RF) in 12 healthy male recreational athletes during SQBP with three load levels (50%, 62.5%, and 75% of 3‐repetition maximum) following a standardized strength training protocol (3 sets of 10 repetitions). To quantify differences in motor control mechanisms in both time and frequency domains, we analyzed (1) muscle covariation via correlation analyses, as well as (2) common neural input via intermuscular coherence (IMC) between RF, VM, and VL. Our results revealed significantly higher gamma IMC between VM‐VL compared with RF‐VL and RF‐VM for both legs. Correlation analyses demonstrated significantly higher correlation coefficients during ascent periods compared with descent periods across all analyzed muscle pairs. However, no load‐dependent modulation of motor control could be observed. Our study provides novel evidence that motor control during SQBP is characterized by differences in common input between biarticular and monoarticular muscles. Additionally, muscle activation patterns show higher similarity during ascent compared with descent periods. Future research should aim to validate and extend our observations as insights into the underlying control mechanisms offer the possibility for practical implications to optimize training concepts in elite sports and rehabilitation.