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Closed-loop control is important for amputees to manipulate myoelectric prostheses intuitively and dexterously. Tactile feedback can help amputees improve myoelectric control performance for grasping objects. To investigate the effects of different tactile feedback, we performed experiments on six amputees and six able-bodied subjects via electrotactile stimulation. Using a virtual environment, six kinds of objects with different weights and stiffnesses were used for grasping tasks. Five feedback conditions (no feedback, pressure feedback, slip feedback, pressure + slip feedback, and vision feedback) were considered. Nine evaluation indexes and three control objectives (rapidity, economy, and stability) were proposed. Under the five feedback conditions, our study investigated four issues: 1) three types of grasping-related failures; 2) four types of grasping-related time measures; 3) average grasping force; 4) standard deviation of the grasping force. Results indicate that: 1) slip feedback is better than pressure feedback; 2) pressure + slip feedback can improve grasping rapidity; 3) slip feedback significantly contributes to grasping economy and stability; and 4) pressure + slip feedback can perform as well as vision feedback.

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This paper introduces and validates quantitative performance measures for a rhythmic target-hitting task. These performance measures are derived from a detailed analysis of human performance during a month-long training experiment where participants learned to operate a 2-DOF haptic interface in a virtual environment to execute a manual control task. The motivation for the analysis presented in this paper is to determine measures of participant performance that capture the key skills of the task. This analysis of performance indicates that two quantitative measures-trajectory error and input frequency-capture the key skills of the targethitting task, as the results show a strong correlation between the performance measures and the task objective of maximizing target hits. The performance trends were further explored by grouping the participants based on expertise and examining trends during training in terms of these measures. In future work, these measures will be used as inputs to a haptic guidance scheme that adjusts its control gains based on a real-time assessment of human performance of the task. Such guidance schemes will be incorporated into virtual training environments for humans to develop manual skills for domains such as surgery, physical therapy, and sports.

This paper presents the design and kinematics of a four degree-of-freedom upper extremity rehabilitation robot for stroke therapy, to be used in conjunction with the Mirror Image Movement Enabler (MIME) system. The RiceWrist is intended to provide robotic therapy via force-feedback during range-of-motion tasks. The exoskeleton device accommodates forearm supination and pronation, wrist flexion and extension, and radial and ulnar deviation in a compact design with low friction and backlash. Joint range of motion and torque output of the electric-motor driven device is matched to human capabilities. The paper describes the design of the device, along with three control modes that allow for various methods of interaction between the patient and the robotic device. Passive, triggered, and active-constrained modes, such as those developed for MIME, allow for therapist control of therapy protocols based on patient capability and progress. Also presented is the graphical user interface for therapist control of the interactions modes of the RiceWrist, basic experimental protocol, and preliminary experimental results.