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Human levels of dexterity has not been duplicated in a robotic form to date. Dexterity is achieved in part due to the biomechanical structure, and in part due to the neural control of movement. An anatomically correct test-bed (ACT) hand has been constructed to investigate the importance and behavioral consequences of anatomical features and neural control strategies of the human hand. This paper focused on the role of the human hand's variable moment arm. System identification was conducted on the ACT index finger's two degrees of freedom at the metacarpal-phalange (MCP) joint to provide an understanding of, for the first time, how the moment arms vary with multiple joints moving simultaneously. The specific combination of nonlinear moment arms results in an increased ability to produce force at the fingertip for the same neural input when the finger's flexion and adduction angles increase (that is toward the middle of the hand). This preliminary work will lead to answering what biomechanical and neural functions are required to construct fully dexterous robotic and prosthetic hands in the future.

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Children completed the training protocol, demonstrating the feasibility of the STABEL system. Differences in postural sway velocity post-STABEL training may have been affected by fatigue, warranting further investigation. Limited immediate effects suggest more practice is needed to affect sensory attention; however, entrainment gain changes suggest the STABEL system provoked vestibular responses during balance practice.

Safety is a critical factor when designing a robotic rehabilitation environment. Whole-limb or life-size haptic interaction would allow virtual robotic rehabilitation of daily living activities such as sweeping or shelving. However, it has been too dangerous to implement such an environment with conventional active robots that use motor, hydraulic, or pneumatic actuation. To address this issue, a life-size 6-degree-of-freedom (DOF) brakeactuated manipulator (BAM) was designed and constructed. This paper details the BAM's system models including mechanisms, kinematics, and dynamics, as well as detailed input and friction models. In addition, a new system-identification technique that utilizes human input to excite the robot's dynamics with unscented Kalman filtering was employed to identify system parameters. Noise sources are discussed, and the model is validated through force estimation with inverse dynamics. Model parameters and performance are compared with other commercially available haptic devices. The BAM shows a significantly larger workspace, maximum force, and stiffness over other devices exhibiting its promise toward rehabilitative applications.