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For the elderly, the motor function of walking is significant for the maintenance and improvement of their quality of life (QOL). Walking patterns have been postulated to be associated with a decline in walking ability. The objective of this study was to develop a wearable sensor that can capture changes in walking ability in daily life. To this end, motion capture was employed to measure the lower limb joint angles and the trajectories of the lower limbs and knees for each subject while walking at a normal walking speed and to compare the results between elderly and young patients. The differences in walking patterns between the elderly and young patients were analyzed in terms of elevation angles, and an index was calculated to capture changes during walking based on elevation angles and trajectories. [ABSTRACT FROM AUTHOR]

In this paper, we propose a double air chambers artificial muscle to eliminate the hysteresis in the extension and contraction movement of pneumatic artificial muscles. In this paper, the basic structure of the double air chambers artificial muscle is a rubberless artificial muscle with a particularly large hysteresis loop. The double air chambers artificial muscle aims to eliminate hysteresis by directly pressurizing the inside and outside of the air chamber and actively deforming the air chamber. The hysteresis is reduced by the pressure outside of the air chamber (external pressure). Since the appropriate external pressure varies depending on the contraction force and amount of contraction, we proposed a method to regulate the appropriate external pressure by feedback control. The experimental results show that hysteresis was eliminated in the static characteristics. It was also found that the output gain decreased, and the phase lag increased as the target frequency increased. The output gain did not change with increasing load. The phase lag tended to improve with the PID controller compared to the PI controller. These results suggest that the combination of double air chambers artificial muscle and external pressure-regulated feedback control can achieve non-hysteresis position control, and it is useful as an actuator in mechatronic systems. [ABSTRACT FROM AUTHOR]

Most BCI systems used in neurorehabilitation detect EEG features indicating motor intent based on machine learning, focusing on repetitive movements, such as limb flexion and extension. These machine learning methods require large datasets and are time consuming, making them unsuitable for same-day rehabilitation training following EEG measurements. Therefore, we propose a BMI system based on fuzzy inference that bypasses the need for specific EEG features, introducing an algorithm that allows patients to progress from measurement to training within a few hours. Additionally, we explored the integration of electromyography (EMG) with conventional EEG-based motor intention estimation to capture continuous movements, which is essential for advanced motor function training, such as skill improvement. In this study, we developed an algorithm that detects the initial movement via EEG and switches to EMG for subsequent movements. This approach ensures real-time responsiveness and effective handling of continuous movements. Herein, we report the results of this study. [ABSTRACT FROM AUTHOR]