Abstract:
The human arm is an essential limb for performing the bare minimum tasks of daily living. It is highly dexterous, primarily due to the redundancy in the musculoskeletal system. The redundancy facilitates the human central nervous system to individually and simultaneously modulate joint stiffness and joint torques to achieve stability and precision during a specific task. This ability of the human arm diminishes in the case of motor impairment. However, robot-based rehabilitation paradigms are proven helpful in the recovery of motor skills. During upper limb rehabilitation training, external forces assist limb motion, and humans respond to these forces by altering limb stiffness. The arm’s musculoskeletal stiffness must be estimated to compute the altered limb stiffness due to an intervention. In this context, an upper limb musculoskeletal model to study the stiffness variations during a movement task can help design better robot-based rehabilitation paradigms. In this work, the human upper limb is modelled as a cable-driven serial chain system during the planar motion. The multi-joint stiffness matrix is formulated for the serial chain system, considering the dominant upper limb muscles. Also, the stiffness variations are discussed for four different postures and during a straight-line trajectory anterior to the body. The presented upper limb musculoskeletal system model provides required insights into multi-joint stiffness. Thus, the proposed work has usefulness in designing better upper limb rehabilitation intervention paradigms.