Analysis of motor unit activities during multiple motor tasks by real-time EMG decomposition: perspective for myoelectric control

Online Decomposition of Surface Electromyogram Into Individual Motor Unit Activities Using Progressive FastICA Peel-Off

Surface electromyogram (SEMG) decomposition provides a promising tool for decoding and understanding neural drive information non-invasively. In contrast to previous SEMG decomposition methods mainly developed in offline conditions, there are few studies on online SEMG decomposition. A novel method for online decomposition of SEMG data is presented using the progressive FastICA peel-off (PFP) method. The proposed online method utilized a two-stage approach, consisting of an offline prework stage for initializing high-quality separation vectors through the offline PFP algorithm, and an online decomposition stage for estimating source signals of different motor units by applying these vectors to the input SEMG data stream. Specifically, a new successive multi-threshold Otsu algorithm was developed in the online stage for determining each motor unit spike train (MUST) precisely with fast and simple computations (to replace a time-consuming iterative threshold setting in the original PFP method). The performance of the proposed online SEMG decomposition method was evaluated by both simulation and experimental approaches. When processing simulated SEMG data, the online PFP method achieved a decomposition accuracy of 97.37%, superior to that (95.1%) of an online method with a traditional k-means clustering algorithm for MUST extraction. Our method was also found to achieve superior performance at higher noise levels. For decomposing experimental SEMG data, the online PFP method was able to extract an average of 12.00 ± 3.46 MUs per trial, with a matching rate of 90.38%, with respect to the expert-guided offline decomposition results. Our study provides a valuable way of online decomposition of SEMG data with advanced applications in movement control and health.

Characterization of Motor Unit Firing and Twitch Properties for Decoding Musculoskeletal Force in the Human Ankle Joint In Vivo

Understanding how motor units (MUs) contribute to skeletal mechanical force is crucial for unraveling the underlying mechanism of human movement. Alterations in MU firing, contractile and force-generating properties emerge in response to physical training, aging or injury. However, how changes in MU firing and twitch properties dictate skeletal muscle force generation in healthy and impaired individuals remains an open question. In this work, we present a MU-specific approach to identify firing and twitch properties of MU samples and employ them to decode musculoskeletal function in vivo. First, MU firing events were decomposed offline from high-density electromyography (HD-EMG) of six lower leg muscles involved in ankle plantar-dorsi flexion. We characterized their twitch responses based on the statistical distributions of their firing properties and employed them to compute MU-specific activation dynamics. Subsequently, we decoded ankle joint moments by linking our framework to a subject-specific musculoskeletal model. We validated our approach at different ankle positions and levels of activation and compared it with traditional EMG-driven models. Our proposed MU-specific formulation achieves higher generalization across conditions than the EMG-driven models, with significantly lower coefficients of variation in torque predictions. Furthermore, our approach shows distinct neural strategies across a large repertoire of contractile conditions in different muscles. Our proposed approach may open new avenues for characterizing the relationship between MU firing and twitch properties and their influence on force capacity. This can facilitate the development of targeted rehabilitation strategies tailored to individuals with specific neuromuscular conditions.

Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.