###### Abstract
We have previously conducted a study to determine the most commonly used approaches in RTBF applications [16] and were surprised to find that none of the articles we reviewed explicitly discussed specific software mechanisms for handling real-time constraints. Even when timing requirements were mentioned, they were often vague or not directly related to software implementation. For instance, in [17], the authors stated that \"the microcontroller continuously calculates the current position,\" but did not provide specific details about the timing constraints. In another study, [18], the authors reported choosing a time window of 12 s and a sampling frequency of 64 Hz for EEG signals, but did not specify any timing requirements for the accelerometer readings, which they described as \"instantaneous measurements\". The most stringent timing requirements we found were in [11], where a capture rate of 1000 Hz was required, but even in this article, no details were provided on how the system ensured precise timing and synchronization. Wearable sensor devices for RTBF systems must execute multiple processes in parallel, including acquiring sensor data at uniform time intervals, formatting and assembling data packets, and transmitting sensor data wirelessly while maintaining minimal latency. Ensuring precise timing and synchronization across these processes presents a technical challenge, particularly given the limited computational resources of battery-powered microcontrollers. Several Real-Time Operating System (RTOS) solutions are available for real-time embedded applications, including FreeRTOS, ChibiOS, and NuttX. These systems provide features like pre-emptive multitasking, priority-based scheduling, and inter-task communication, making them suitable for general-purpose real-time applications. However, for RTBF applications, these features introduce unnecessary overhead, making them less suitable for resource-constrained wearable devices. To address these challenges, we developed a lightweight periodic scheduler tailored to the specific needs of real-time biomechanical feedback applications. Unlike full RTOS solutions, which provide complex task management features, our scheduler follows a cooperative round-robin execution model, ensuring minimal overhead and precise timing control. This design allows us to achieve the following:  
* Minimal overhead--It is lean and efficient, avoiding excessive memory usage or processing time that could interfere with other processes. * Task-specific design--It performs only the essential functions required for our wireless sensor device, without unnecessary complexity. * Configurability--It is adaptable to various application scenarios, such as streaming, logging, or event detection. * Cost-effective and energy-efficient design--Since wireless systems often operate on battery power, energy efficiency is important for prolonged use. However, considering that our device is intended for shorter operational intervals in environments with accessible charging options, cost efficiency also plays a key role. The scheduler should therefore maintain a balance between low power consumption and affordability, ensuring an optimal trade-off between performance, energy usage, and price. The primary research question addressed in this study is the following: What is the maximum sensor sampling frequency that can be supported by a given wearable device in streaming mode?