The restoration of motor functions of patients with spinal cord injury (SCI) is one of important subjects for study. For this purpose, methods of functional neuromuscular stimulation (FNS) have been investigated in medical science and practice during these three decades. However, we have not achieved complete restoration of motor functions in SCI patients. On the other hand, we have achieved useful devices in human-scaled transportation by using power assist technology. Thus, applying power assist technology to the problem of restoring motor functions is one of possible solutions and sounds practical. In this paper, we propose a new hybrid system to combine power assist technology and FNS for restoring motor functions of lower extremity in SCI patients. Both powered orthosis and FNS are used to generate and control the joints moments of lower extremity in the proposed hybrid system. The main role of powered orthosis to compensate the joints moments generated by FNS and to enhance the controllability of FNS with the actuators. The proposed hybrid control system has been experimentally evaluated in gait motions by measuring the angle trajectories and generated moments around the knee and hip joints in the cases when only actuators are used and both FNS and actuators of the orthosis are used. The results prove that the control method for the hybrid system is useful to restore motor functions of lower extremity in SCI patients.
This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants were running in place for about 100 s. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.
This paper presents an adaptive simulator for a manual wheelchair to reduce the user's upper limb load during wheelchair manipulation and to increase the efficiency of wheelchair propulsion. The proposed simulator provides an optimal position of the handrim/lever and the desired angular position of the seat and backrest of the wheelchair based on the user's body function.
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