Assistive devices aim to mitigate the effects of physical disability by aiding users to move their limbs or by rehabilitating through therapy. These devices are commonly embodied by robotic or exoskeletal systems that are still in development and use the electromyographic (EMG) signal to determine user intent. Not much focus has been placed on developing a neuromuscular interface (NI) that solely relies on the EMG signal, and does not require modifications to the end user's state to enhance the signal (such as adding weights). This paper presents the development of a flexible, physiological model for the elbow joint that is leading toward the implementation of an NI, which predicts joint motion from EMG signals for both able-bodied and less-abled users. The approach uses musculotendon models to determine muscle contraction forces, a proposed musculoskeletal model to determine total joint torque, and a kinematic model to determine joint rotational kinematics. After a sensitivity analysis and tuning using genetic algorithms, subject trials yielded an average root-mean-square error of 6.53° and 22.4° for a single cycle and random cycles of movement of the elbow joint, respectively. This helps us to validate the elbow model and paves the way toward the development of an NI.

The results from this case study highlight the infancy of BCIs as a form of assistive technology for people with cerebral palsy. Existing commercial BCIs are not designed according to the needs of end-users. Implications for Rehabilitation Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces are not designed for practical assistive technology use for people with cerebral palsy. Practical brain-computer interface assistive technologies may need to be flexible to suit individual needs.

The results from this case study highlight the importance of creating a dynamic, relevant and engaging training environment for BCIs. Implications for Rehabilitation Customising a training paradigm to suit the users' interests can influence adherence to assistive technology training. Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces, which require little set up time, may be used as access technology for individuals with severe disabilities.

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Assistive robots have made great contributions to disabled people in physiotherapy and rehabilitation areas. The interface between patients and medical devices plays a significant role for patients to operate these kinds of robots. This review introduces the current research and development of neuromuscular interfaces, especially the new research directions with special focus on modelling of musculoskeletal systems for interfacing purposes. The paper also summarises the function and prominent advantage of using surface electromyography (sEMG) signals for the interface. The elbow joint was used as an example to go through the working steps of both human biological systems and neuromuscular interfaces. Further developments were also discussed to improve the interface to meet medical demands.