# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 1 Introduction  
Wearable, wireless sensors have become prominent in the last decade or two in a wide range of applications. In the healthcare sector, these sensors can be used for remote patient monitoring, diagnostics, and research. These sensors enable clinicians and researchers to collect data continuously from subjects both in and outside of a traditional laboratory or clinical setting. Wearable, wireless biosensors have been used for fitness applications such as heart rate monitoring, sleep monitoring, and exercise tracking in devices such as the Apple Watch or Google Pixel Watch [1, 2], as well as in human-machine interfaces for prosthetics, orthotics and robotic control applications [3, 4], and virtual and augmented reality controllers [5]. In this work, the goal is to assess BLE 5.3 for use in a multi-channel, wireless, biosensor network for a wearable electromyogram (EMG)-driven prosthesis controller. Real-time myoelectric prosthesis control requires low latency, high wireless transmission throughput communication protocols and multiple peripheral nodes communicating with a single central node (i.e., a star network). For myoelectrically controlled prostheses, wireless EMG eliminates cumbersome cabling and is essential for osseeintegrated devices that lack a socket in which to run wiring and house sensors. In a prosthesis controller, performance decreases approximately linearly with the total latency, which should be under 100 ms to ensure that wearers do not experience a performance-reducing lag between muscle activation and prosthesis response [6]. A delay from wireless transmission reduces the available EMG smoothing time, resulting in higher variance control signals [7]. Additionally, multiple battery-powered sensors are commonly placed at different EMG sites, which requires multiple simultaneous wireless connections. With the rise of wearable, wireless sensors, there have been improvements in wireless communication protocols such as Bluetooth Low Energy (BLE), Wi-Fi, ZigBee, and NFC. The choice of wireless communication protocol is made to ensure that latency, power usage, range, and compatibility are suitable for the target application. BLE is commonly used for wearable systems, as it offers a low-power, low-cost solution for close range (less than 400 m for BLE 5) applications [8] and is highly configurable--making it easy to adapt for low latency and low power applications. Additionally, BLE is implemented in many common devices such as smartphones and laptops, making the integration of BLE-based sensors with existing infrastructure straightforward [9]. Backwards compatibility is guaranteed, so devices running the latest BLE release can operate with legacy BLE systems [10]. Wi-Fi, ZigBee, and NFC have also been used to design wireless biosensors. Wi-Fi is also widely available in modern smartphones and PCs. In comparison to BLE, Wi-Fi can send larger amounts of data at faster rates but at the expense of higher power consumption, which is detrimental in battery-powered systems [11]. ZigBee has been used for biomedical and industrial applications. It operates indoors and outdoors and offers transmission distances from 10 to 100 m. However, ZigBee supports a smaller maximum data rate of 250 kbps [12] compared to the 2 Mbps maximum data rate offered by BLE 5 [10]. This lower rate is insufficient for EMG acquisition, which, for research applications, should be sampled at a minimum of 2000 Hz [13; 14], whilst many real-time products are sampled at a minimum of 1000 Hz (as we have done herein). In comparison to BLE, ZigBee offers a shorter operation range and has limited adoption, making interoperability a concern and a less robust security protocol, which makes it a less secure option for health-related applications [11]. More recently, NFC has also found applications in wearable, wireless, battery-less biosensors.