Analysis of muscle activity by electrophysiological techniques is commonly used to analyze biomechanics. Although the simultaneous and intuitive understanding of both muscle activity and body motion is important in various fields, it is difficult to realize. This paper proposes a novel technique for visualizing physiological signals related to muscle activity by means of surface electromyography. We developed a wearable light-emitting interface that indicates lower-limb muscle activity or muscular tension on the surface of the body in real time by displaying the shape of the activated muscle. The developed interface allows users to perceive muscle activity in an intuitive manner by relating the level of the muscle activity to the brightness level of the glowing interface placed on the corresponding muscle. In order to verify the advantage of the proposed method, a cognitive experiment was conducted to evaluate the system performance. We also conducted an evaluation experiment using the developed interface in conjunction with an exoskeleton robot, in order to investigate the possible applications of the developed interface in the field of neuro-rehabilitation.

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In the effort to make robot-assisted upper limb passive movement training effective for neurologic injuries suffered from stroke and spinal cord injury (SCI), a new fuzzy adaptive closed-loop supervisory control method for passive joint movement training is proposed. Firstly, high-level supervisory controller for the desired passive range of motion (PROM) is designed based on the impaired limb’s joint motion recovery, and then low-level closed-loop position tracking controller is presented to drive the robot stably and smoothly to stretch the impaired limb to move along the predefined trajectory. The suggested strategy was applied to the four degrees of freedom (DOF) Whole Arm Manipulator (WAM) rehabilitation robot to evaluate its performance. Experimental results carried out on the 4-DOF WAM rehabilitation robot show the effectiveness and potentialities of the fuzzy adaptive passive movement control in clinical application.

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This project aims to design and develop a working prototype of a walking assist device. Spring and brushed DC motors are adopted to assist human being standing or walking by lessening the amount of force subjected to the knee. Two DC motors are controlled by the high current capable motor driver which is specifically designed for heat management. Moreover, an 8051 microcontroller provides interface between the sensor ADC input, keypad manual input, intelligent controller and the DC motors.

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Objective: We evaluate and quantify the effects of human, robot, and environmental factors on perceived trust in human-robot interaction (HRI).

Background: To date, reviews of trust in HRI have been qualitative or descriptive. Our quantitative review provides a fundamental empirical foundation to advance both theory and practice.

Method: Meta-analytic methods were applied to the available literature on trust and HRI. A total of 29 empirical studies were collected, of which 10 met the selection criteria for correlational analysis and 11 for experimental analysis. These studies provided 69 correlational and 47 experimental effect sizes.

Results: The overall correlational effect size for trust was r̄ = +0.26, with an experimental effect size of d̄ = +0.71. The effects of human, robot, and environmental characteristics were examined with an especial evaluation of the robot dimensions of performance and attribute-based factors. The robot performance and attributes were the largest contributors to the development of trust in HRI. Environmental factors played only a moderate role.

Conclusion: Factors related to the robot itself, specifically, its performance, had the greatest current association with trust, and environmental factors were moderately associated. There was little evidence for effects of human-related factors.

Application: The findings provide quantitative estimates of human, robot, and environmental factors influencing HRI trust. Specifically, the current summary provides effect size estimates that are useful in establishing design and training guidelines with reference to robot-related factors of HRI trust. Furthermore, results indicate that improper trust calibration may be mitigated by the manipulation of robot design. However, many future research needs are identified.

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