The Role of Synergies in Controlling Human-Machine Interfaces

Abstract

Hand mobility is a critical component of everyday functionality, and its impairment due to conditions such as amputations, stroke, and spinal cord injuries can severely diminish an individual's independence and quality of life. The restoration of hand function is imperative, enabling individuals to regain hand functionality through rehabilitation, assistive devices, prosthetics, and exoskeletons. Human-machine interfaces (HMIs), and in particular brain-machine interfaces (BMIs), have shown considerable promise in the field of motor function restoration. The complex biomechanical architecture of the human hand poses challenges for multidimensional motor control in the effective deployment of BMIs. Additionally, the complex modeling required to map brain activity to hand motor functions adds another layer of complexity to BMI deployment. To overcome these challenges, this dissertation investigates the concept of hand synergies, which are considered as modular structures in movement generation, to explore their consistency and generalizability in the context of synergy-based high-dimensional motor control. This research explores the potential capabilities of integrating synergies into HMIs to enhance efficiency of motor control. This dissertation also aims to investigate and explore the potential BMI applications in decoding neural activity to achieve synergy-based hand movement reconstruction, to further enable high-degree-of-freedom (DoF) neuromotor control with the aim of restoring the functionalities of individuals who have experienced a loss of upper limb mobility. Participants engaged in object-grasp experiments, to examine the role of hand synergies, the fundamental movement patterns that facilitate the control of high-DoF systems, in hand movement and hand prehension. The study also investigated the relationship between hand synergies and brain activity during both executed and imagined hand movements. The study reveals the capability of hand synergies in carrying motor strategies, and the correlations between hand synergies with corresponding neural activity allowing for the movement reconstruction and generation. The finding underscores the potential of synergy-based BMIs to enable near-natural hand kinematics generation and reconstruction for individuals unable to perform physical movements. The study presented here could lead to significant potential in dexterous movement reconstruction, producing more dexterous and user-friendly prosthetics and robotic assistance devices, thereby enhancing the capability and accessibility of technology designed to compensate for lost motor functions.