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As the population ages, the number of elderly people suffering from systemic diseases such as stroke increases. To address this problem, various wearable walking assistive robots have been developed to promote physical exercise for stroke prevention. Wearable assistive robots have shown the ability to improve human mobility. However, most of these robots are heavy, bulky, and impractical. In this study, we developed a compact ankle assistive robot for elderly users to promote walking exercise. By informing the user of correct motion and timing, the robot can guide the user to achieve a healthy gait by only assisting their ankle joint. The robot provides faster-than-ankle motion to allow the user to feel supported while walking. Users can adjust the robot's assistance parameters through a graphical user interface (GUI) according to their needs. Furthermore, we proposed a gait-adaptive method for ankle assistive robots to adapt to the user's changing gait. Hence, the robot can automatically adjust the parameters to provide more accurate walking assistance. Finally, the results of an evaluation experiment demonstrated the feasibility of human gait adaptation. The proposed methods have the advantages of low cost and easy implementation. [ABSTRACT FROM AUTHOR]

The world's aging population is increasing. The number of elderly individuals having walking impairments is also increasing. Adequate exercise is becoming necessary for them. Therefore, several walking assistive devices have been developed or are under development. However, elderly individuals may have low motivation for exercising, or they may experience physical damage by excessive fatigue. This study proposed a method to enable elderly individuals to exercise with a positive emotion and prevent damage such as muscle fatigue. We proposed a 3D human condition model to control the walking assistive device. It includes the arousal, pleasure, and fatigue dimensions. With regard to the arousal and pleasure dimensions, we used heartbeat and electromyography (EEG) signals to train a deep neural network (DNN) model to identify human emotions. For fatigue detection, we proposed a method based on near-infrared spectroscopy (NIRS) to detect muscle fatigue. All the sensors are portable. This implies that it can be used for outdoor activities. Then, we proposed a walking strategy based on a 3D human condition model to control the walking assistive device. Finally, we tested the effectiveness of the automatic control system. The wearing of the walking assistive device and implementation of the walking strategy can delay the fatigue time by approximately 24% and increase the walking distance by approximately 16%. In addition, we succeeded in visualizing the distribution of emotion during each walking method variation. It was verified that the walking strategy can improve the mental condition of a user to a certain extent. These results showed the effectiveness of the proposed system. It could help elderlies maintain higher levels of motivation and prevent muscle damage by walking exercise, using the walking assistive device. [ABSTRACT FROM AUTHOR]

A tele-rehabilitation system that can achieve remote interaction between a physical therapist (PT) and a patient was developed. Patients need to execute rehabilitation exercises to maintain upper limb function. However, it is difficult for them to travel to hospitals without aid. This system is equipped with a PC and a Kinect sensor at the hospital side (i.e., the PT), and a PC and an upper limb assistive device in the patient's home. The PT displays the motion in front of a Kinect sensor, which identifies the motion. In addition, the device on the home side assists the motion of the patient using the Internet. When the device receives a force higher than the safety value from the patient at any particular point on it, vibrators at the corresponding point on the PT's arm start to vibrate. Thereby, the PT can identify the patient's condition and limitations. The time delays in the transmission of data of device motion and the vibrators were measured and compared. As a result, the PT could identify the patient's condition faster than the motion of the device. [ABSTRACT FROM AUTHOR]