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find Keyword "weight support" 2 results
  • Exoskeleton robot system based on real-time gait analysis for walking assist

    This paper presents a wearable exoskeleton robot system to realize walking assist function, which oriented toward the patients or the elderly with the mild impairment of leg movement function, due to illness or natural aging. It reduces the loads of hip, knee, ankle and leg muscles during walking by way of weight support. In consideration of the characteristics of the psychological demands and the disease, unlike the weight loss system in the fixed or followed rehabilitation robot, the structure of the proposed exoskeleton robot is artistic, lightweight and portable. The exoskeleton system analyzes the user's gait real-timely by the plantar pressure sensors to divide gait phases, and present different control strategies for each gait phase. The pressure sensors in the seat of the exoskeleton system provide real-time monitoring of the support efforts. And the drive control uses proportion-integral-derivative (PID) control technology for torque control. The total weight of the robot system is about 12.5 kg. The average of the auxiliary support is about 10 kg during standing, and it is about 3 kg during walking. The system showed, in the experiments, a certain effect of weight support, and reduction of the pressure on the lower limbs to walk and stand.

    Release date:2017-04-13 10:03 Export PDF Favorites Scan
  • A pelvic support weight rehabilitation system tracing the human center of mass height

    The body weight support rehabilitation training system has now become an important treatment method for the rehabilitation of lower limb motor dysfunction. In this paper, a pelvic brace body weight support rehabilitation system is proposed, which follows the center of mass height (CoMH) of the human body. It aims to address the problems that the existing pelvic brace body weight support rehabilitation system with constant impedance provides a fixed motion trajectory for the pelvic mechanism during the rehabilitation training and that the patients have low participation in rehabilitation training. The system collectes human lower limb motion information through inertial measurement unit and predicts CoMH through artificial neural network to realize the tracking control of pelvic brace height. The proposed CoMH model was tested through rehabilitation training of hemiplegic patients. The results showed that the range of motion of the hip and knee joints on the affected side of the patient was improved by 25.0% and 31.4%, respectively, and the ratio of swing phase to support phase on the affected side was closer to that of the gait phase on the healthy side, as opposed to the traditional body weight support rehabilitation training model with fixed motion trajectory of pelvic brace. The motion trajectory of the pelvic brace in CoMH mode depends on the current state of the trainer so as to realize the walking training guided by active movement on the healthy side of hemiplegia patients. The strategy of dynamically adjustment of body weight support is more helpful to improve the efficiency of walking rehabilitation training.

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