Background: Muscle coordination is important for rational and effective planning of therapeutic and exercise interventions using equipment that mimics functional movements. Our study was the first to assess muscle coordination during flywheel (FW) squats. Methods: Time-of-peak electromyographic activation order was assessed separately for 8, 4, and 3 leg muscles under four FW loads. A sequential rank agreement permutations tests (SRA) were conducted to assess activation order and Kendall’s tau was used to assess the concordance of activation order across subjects, loads and expected order of activation. Results: SRA revealed a latent muscle activation order at loads 0.05, 0.075, and 0.1, but not at 0.025 kg·m². Kendall’s tau showed moderate-to-strong concordance between the expected (proximal-to-distal) and the observed muscle activation order only at a load 0.025 kg·m², regardless of the number of muscles analyzed. Muscle activation order was highly concordant between loads 0.05, 0.075, and 0.1 kg·m². Conclusions: The results show a specific role of each muscle during the FW squat that is load-dependent. While the lowest load follows the proximal-to-distal principle of muscle activation, higher loads lead to a reorganization of the underlying muscle coordination mechanisms. They require a specific and stable muscle coordination pattern that is not proximal-to-distal.

The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architecture that harnesses the potential of edge devices, reduces network latency, and lowers infrastructure cost to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. To address these concerns, we introduce a decentralized architecture tailored for large scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. The solution combines a blockchain consensus algorithm and verifiable random functions to ensure scalability, fault tolerance, transparency, and no single point of failure. We build on our previously presented theoretical simulations with many protocol improvements and an implementation tested in a use case related to monitoring a Slovenian cultural heritage building located in Bled, Slovenia.