An innovative sensor suit is developed, which can be conveniently put on by an operator to detect his or her motion intention by non-invasively monitoring his or her muscle conditions such as the shape, the stiffness and the density. This sensor suit is made of soft and elastic fabrics embedded with arrays of MEMS sensors such as muscle stiffness sensor, ultrasonic sensors, accelerometers and optical fiber sensors, to measure different types of human muscle conditions. Compared with conventional sensors used in man machine interfaces, the proposed sensor suit is soft, flexible, lightweight, easy to wear, accurate and reliable. More importantly, the sensor suit is auto-adaptive to an individual operator and does not impede motion against the operator. Furthermore, a failsafe mechanism using special hardware and active sensing software is incorporated in the sensor suit to ensure the safety and reliability. As a result, the sensor suit is ideal for use in man-machine systems, particularly the DARPA exoskeleton power suits. In this case, the sensor suit will be worn by the operator under the exoskeleton power suits. The sensor suit, with its distributed sensing capability, will provide accurate and reliable information about the operator’s motion intention which is needed for controlling the power suit to accurately follow the operator s motion intention.

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Metabolic studies have shown that there is a metabolic cost associated with carrying a load (Griffin et al, 2003). Further studies have shown that by applying forward propulsive forces a person can walk with a reduced metabolic rate (Farley & McMahon, 1992 and Gottschall & Kram, 2003). Previous work on exoskeleton design has not considered the passive dynamics of walking and has focused on fully actuated systems that are inefficient and heavy. In this thesis, an under-actuated exoskeleton is presented that runs parallel to the human leg. The exoskeleton component design is based on the kinematics and kinetics of human walking. The joint components of the exoskeleton in the sagittal plane consist of a force-controllable actuator at the hip, a variable-damper mechanism at the knee and a passive spring at the ankle. A state-machine control strategy is written based on joint angle and ground-exoskeleton force sensing. Positive, non-conservative power is added at the hip during the walking cycle to help propel the mass of the human and payload forward. At the knee, the damper mechanism is turned on at heel strike as the exoskeleton leg is loaded and turned off during terminal stance to allow knee flexion.

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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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