The lower-limb muscle activities during the daily lower-limb motions such as sitting down, standing up, squatting, walking, ascending and descending stairs motions have been studied to enable power-assist robotic systems to estimate human lower-limb motions based on muscle electromyographic (EMG) signals. The relationship between the lower-limb motions and the acti-vity levels of main muscles con-cerning the daily lower-limb motions are analyzed in this study. The human intention for lower-limb motion can be estimated based on the activation pattern of lower-limb mus-cles. The analyzed results would be used to design the controllers of the lower-limb power-assist robotic systems for physically weak persons.

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Background: Gait restoration is an integral part of rehabilitation of brain lesioned patients. Modern concepts favour a task-specific repetitive approach, i. e. who wants to regain walking has to walk, while tone-inhibiting and gait preparatory manoeuvres had domi-nated therapy before. Following the first mobilization out of the bed, the wheelchair-bound patient should have the possibility to practise complex gait cycles as soon as possible. Steps in this direction were treadmill training with partial body weight support and most recently gait machines enabling the repetitive training of even surface gait and even of stair climbing. Results: With treadmill training harness-secured and partially relieved wheelchair-mobilised patients could practise up to 1000 steps per session for the first time. Controlled trials in stroke and SCI pati-ents, however, failed to show a superior result when compared to walking exercise on the floor. Most like-ly explanation was the effort for the therapists, e.g. manually setting the paretic limbs during the swing phase resulting in a too little gait intensity. The next steps were gait machines, either consisting of a powered exoskeleton and a treadmill (Lokomat, AutoAmbulator) or an electromechanical solution with the harness secured patient placed on movable foot plates (Gait Trainer GT I). For the latter, a large multi-centre trial with 155 non-ambulatory stroke patients (DEGAS) revealed a superior gait ability and compe-tence in basic activities of living in the experimental group. The HapticWalker continued the end effector concept of movable foot plates, now fully programmable and equipped with 6 DOF force sensors. This device for the first time enables training of arbitrary walking situations, hence not only the simulation of floor walking but also for example of stair climbing and perturbations. Conclusion: Locomotor therapy is a fascinating new tool in rehabilitation, which is in line with modern principles of motor relearning pro-moting a task-specific repetitive approach. Sophisticated technical developments and positive randomized controlled trials form the basis of a growing acceptance worldwide to the benefits or our patients.

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Carrying a payload directly on the body is an unavoidable aspect of human life. Human bipedal locomotion knows no equal: people travel on foot to virtually every corner of the globe. Despite the efficiency and convenience of wheeled apparatus, uneven terrain, enclosed environments and accessibility limits require virtually every transportation task to include a phase in which material goods must be physically carried by a person. As of today, no artificial intelligence or programmed behavior has been able to match a human’s ability to balance and maneuver in unstructured real-world environments. The Berkeley Lower Extremity Exoskeleton solves the problem of supporting and carrying heavy loads on the body and allows a person to navigate unencumbered by the weight of the payload they are carrying. The Berkeley Lower Extremity Exoskeleton is an anthropomorphic and energetically autonomous robotic device comprised of two legs, a backpack, a harness system and a control computer that provides a wearable load support platform. This thesis presents a control scheme called Sensitivity Amplification Control that enables an exoskeleton to support a payload and shadow the movement of the wearer in an intuitive and unobtrusive manner. The control algorithm developed here increases the closed-loop system sensitivity to its wearer’s forces and torques without any measurement from the wearer. This strategy requires an accurate dynamic model of the system but does not require direct measurements from the human. The trade-off between not having sensors to measure human action and the sacrificed robustness due to model parameter variation is described. A modification to the controller is also explored that partially circumvents this limitation.

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Metabolic studies have shown that there is a metabolic cost associated with carrying load (T. M. Griffen, et al., 2003). In previous work, a lightweight, underactuated exoskeleton has been described that runs in parallel to the human and supports the weight of a payload (C. J. Walsh, et al., 2006). A state-machine control strategy is written based on joint angle and ground-exoskeleton force sensing to control the joint actuation at thisexoskeleton hip and knee. 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. The control is motivated by examining human walking data. 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. The passive spring at the ankle engages in controlled dorsiflexion to store energy that is later released to assist in powered plantarflexion. Preliminary studies show that the state machines for the hip and knee work robustly and that the onset of walking can be detected in less than one gait cycle. Further, it is found that an efficient, underactuated leg exoskeleton can effectively transmit payload forces to the ground during the walking cycle.

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Using CMU actuators, a Prototype of Mechanical Assistance Device for the Wrist’s Flexion Movement (PMA) was developed and probed in a mechanical model, in order to be implemented in a future as a dynamic powered orthosis or as a rehabilitation assistant instrument. Two Mayor Actuators conformed by three CMU actuators arranged in a series configuration, allows to an artificial hand to be placed in four predefined positions: 0º, 20º, 40º and 60º. The synchronism and control of the actuators is achieved with the Programmable Control Module (PCM). It is capable to drive up to six CMU actuators, and possess two different modes of execution: a Manual mode and an Exercise mode. In the Manual Mode, the position of the hand responds directly to the commands of the keyboard of the front panel, and in the Exercise mode, the hand realizes a repetitive and programmed movement. The prototype was tested in 100 positions in the Manual Mode and for 225 works cycles in the Exercise Mode. The relative repetition error was less than 5% for both test. This prototype only consumes 4,15W, which makes it possible to be powered by small rechargeable batteries, allowing its use as a portable device.

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