## 1 Introduction  
The growing interest in the application of gesture recognition (GR) using surface electromyography (sEMG) signals has been evident in recent years. A search on the SCOPUS database using the keywords (\"Electromyography\" or \"EMG\") and \"Gesture Recognition\" yielded 884 references dating back to 1993. This is a highly interdisciplinary field, with the majority of papers falling under the categories of \"Computer Science\" and \"Engineering\" with 637 and 557 references, respectively, with 351 being common to both. Other areas with a significant number of references include \"Mathematics,\" \"Physics and Astronomy,\" and \"Medicine\" with 169, 144, and 124 references, respectively. Of those, none, 18, and 9 do not belong to either \"Computer Science\" or \"Engineering\". There are numerous medical applications for this technology for both people with disabilities and healthy individuals, as discussed in [1]. EMG signals have also been used in the design [2] and control of prostheses [3]. Applications in robot therapy were presented in [4], and [5] describes an elbow rehabilitation system using a remote-controlled intelligent robot. The use of EMG signals for disease diagnosis is an area with many applications, including the progression of primary lateral sclerosis [6], dysphagia [7], ptetlofemoralpain syndrome [8], neuromuscular disorders [9], and the evaluation of mobility loss in patients with musculoskeletal disorders [10]. Some of the uses of sEMG-based GR mentioned before can be grouped in human-machine interface (HMI). sEMG HMI allows the user to control or command other systems, such as wheelchairs, prosthetics, drones, robot arms, or exoskeletons. This use has been extensively studied by specific reviews on the topic. These reviews showed the necessity of low-complexity classifiers [11; 12; 13; 14], which translates into reduced preprocessing, lower latency, and lower power consumption. These characteristics are critical when talking about wearable devices, as shown in review [15]. One of the great challenges facing GR systems is their use in wearable devices. A wearable device is a type of embedded system that is designed to be worn on the body, such as smartwatches, fitness trackers, and clothing embedded with sensors. These devices have several advantages, including convenience, portability, and personalization, but also have some limitations, such as limited battery life, privacy, security concerns, and limited computational power. The processing that takes place at the end devices is what is known as edge computing. Edge computing has several advantages, such as privacy, given that no data is uploaded to a third-party cloud server; lower and reliable latency, as data are not required to be uploaded, and the latency does not depend on the network availability; and lower cost or simplicity because the system does not require a modem or connection to a network. On the other hand, the resources are limited by the embedded systems, and the use of batteries limits the use of more complex algorithms, such as complex neural networks (NNs) [16]. Therefore, this leaves room for simpler lineal models, such as the one presented in this work. To ensure continuity of operation, a fault-tolerant system can be implemented, which allows the system to continue functioning correctly in the presence of hardware or software faults by detecting, diagnosing, and recovering from errors in real-time. This is achieved by using techniques such as redundancy, error detection and correction, diversity, and isolation. They can be combined to accomplish different levels of fault tolerance depending on the specific requirements of the system and application. As wearables are becoming more and more important in different fields, their fault-tolerance feature is increasingly important in many critical systems, such as aerospace, military, medical, and industrial systems, where a _failure_ could have serious consequences. In these applications, the lack of robustness could lead to material or even personal damage.