# Soft Rehabilitation and Nursing-Care Robots: A Review and Future Outlook
## 4 Discussion and Outlook
The compliance of the rehabilitation robot should be adjusted according to the patient's wishes. How to change the auxiliary control strategy according to the patient's immediate thought is a problem that researchers need to think about, which allows the patient to participate in the rehabilitation process to the greatest extent possible. The brain-computer interfaces and visual feedback may be good solutions. For example, Refs. [114; 115] propose some innovative control strategies. Furthermore, patients in different age groups, different disability levels and different rehabilitation stages need different control strategies and individualized training modes, which puts requirements of intelligence on SRNCRs. In addition, the specific control strategies and training modes should be set up after communication with the rehabilitation experts, which allows the patient to get the best, most timely and most suitable rehabilitation treatment. Besides, machine learning is a way to make computers intelligent, and it is a hot topic of research at this stage. As the core of artificial intelligence, the application of machine learning extends to many fields of artificial intelligence, which has a great impact on various fields. Similarly, machine learning has a very broad application prospect in the modeling and control strategies of soft robots. It can build a more accurate model of SRNCR and optimize the control strategy. For example, both [116; 117] use machine learning methods to control and model SRNCRs. Therefore, it is foreseeable that in the future machine learning will become an important method for modeling and controlling SRNCRs. In the process of rehabilitation and nursing-care, the human-robot interaction has a great influence on the medical effect of rehabilitation equipment. Effective human-computer interaction design can accurately translate the user's intention into the behavior of the rehabilitation robot, which allows patients to participate actively in the rehabilitation process to the greatest extent. In 2017, [PERSON] et al. proposed an interactive strategy to control the behavior of rehabilitation gloves by using binocular vision tracking technology to detect commands of \"winking\" [114]. This control method controls gloves by detecting a pre-defined \"wink\" signal. In their study, the eye tracking methods were compared with speech recognition and EMG gesture based methods in controlling the soft glove. The experimental results show that the average error rate of \"wink\" control is the lowest, followed by the speech recognition and the EMG gesture based control. At the same time, [PERSON] et al. designed a new wearable brain-controlled mechanical glove (Figure 8a) [115]. The gloves use an emotive insight headset to receive signals from the brain, which is an electroencephalogram sensor (Figure 8b). The signals from the sensor are processed by a pic microcontroller. The processed signal is transmitted into the servo motor to control the movement of fingers and hands. Besides, in the field of SRNCR based on pneumatic fiber braids and elastomeric polymers, [PERSON]. [118] proposed two assistant controllers of pneumatic direct driven SRNCR based on an inverse model of human limbs. Based on the adaptive feedforward model, the device can provide enough support for the patient's movement and maintain the high flexibility of the soft actuator. The first controller is based on prior information of model parameter estimation (MPE). When patients are active, the controller allows patients to find their personal trajectories without forcing them to use predetermined trajectories. The second controller is based on the function approximation technique using radial basis function (RBF), which does not require prior information. This controller only allows the patients to produce small deviations from the desired trajectory.