The exoskeleton robot, serving as an assistive device worn by the human (orthotic), functions as a human-amplifier. Setting the human machine interface (HMI) at the neuro-muscular level may lead to seamless integration and an intuitive control of theexoskeleton arm as a natural extension of the human body. At the core of the exoskeleton HMI there is a myoprocessor. It is a model of the human muscle, running in real-time and in parallel to the physiological muscle, that predicts joint torque as a function of the joint kinematics and neural activation levels. The study is focused on developing a myoprocessor based on the Hill phenomenological muscle model. Genetic algorithms were used to optimize model internal parameters using an experimental database that provides inputs to the model and allows for performance assessment. The results indicate high correlation between joint moment predictions of the model and the measured data. Consequently, the myoprocessor seems an adequate model, sufficiently robust for further integration into the exoskeleton control system.

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Many places in the world are too rugged or enclosed for vehicles to access. Even today, material transport to such areas is limited to manual labor and beasts of burden. Modern advancements in wearable robotics may make those methods obsolete. Lower extremity exoskeletons seek to supplement the intelligence and sensory systems of a human with the significant strength and endurance of a pair of wearable robotic legs that support a payload. This paper outlines the use of Clinical Gait Analysis data as the framework for the design of such a system at UC Berkeley.

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A method for indirectly assessing the gesture, posture or movement of a body part of a person includes transmitting an ultrasound signal into the soft tissue, particularly the muscle, of body part and manipulating the reflected ultrasound signal to obtain parameter data. The parameter data is compared to reference information to obtain gesture, posture or movement information for the body part. Apparatus includes an ultrasound transmitter and receiver for transmitting, a signal processor, and a processor for storing reference information and determining gesture, posture or movement information for the body part.

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There is a need for wearable powered upper limb exoskeletons able to apply forces to the upper limb for use by people with disabilities and/or limb weakness or injury. The robotic exoskeleton called WOTAS (Wearable Orthosis for Tremor Assessment and Suppression) presented in this paper will provide a means of testing non-grounded control strategies in order to help these people. For instance, biomechanical loading, in particular, viscous loading of the upper limb has been proposed in the literature as a means for suppressing pathologic tremor. This article describes in detail the general concept for WOTAS, outlining the special features of the design and selection of system components.

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The first functional load-carrying and energetically autonomous exoskeleton was demonstrated at U.C. Berkeley, walking at the average speed of 1.3 m/s while carrying a 34 kg (75 lb) payload. Four fundamental technologies associated with the Berkeley Lower Extremity Exoskeleton (BLEEX) were tackled during the course of this project. These four core technologies include: the design of the exoskeleton architecture, control schemes, a body local area network (bLAN) to host the control algorithm and an on-board power unit to power the actuators, sensors and the computers. This article gives an overview of one of the control schemes. The analysis here is an extension of the classical definition of the sensitivity function of a system: the ability of a system to reject disturbances or the measure of system robustness. The control algorithm developed here increases the closed loop system sensitivity to its wearer’s forces and torques without any measurement from the wearer (such as force, position, or electromyogram signal). The control method has little robustness to parameter variations and therefore requires a relatively good dynamic model of the system. The tradeoffs between having sensors to measure human variables and the lack of robustness to parameter variation are described.

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