# Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review
## 2 Materials and Methods
### The Development of Hand Rehabilitation Robot
#### 2.1.2 The End-Effector Hand Rehabilitation Robot
In 2019, Xi'an Jiao tong University invented an attention-controlled hand exoskeleton for the rehabilitation of finger extension and flexion using a rigid-soft combined mechanism [47], as shown in Figure 7. Active rehabilitation training is achieved using an attentional value threshold measured by an electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. The spring layer flexes and slides due to the linear motion input provided by the linear motor, and then the structure becomes like a circular sector that supports the finger flexion/extension movement when the structure is attached to the finger. In 2020, [PERSON], [PERSON] et al. designed the RELab toneoxo [48], a fully wearable assistive soft hand exoskeleton for everyday activities, as shown in Figure 8. It consists of a hand moduleattached to the hand and a backpack containing electronics, motors, and batteries. The backpack and the hand module are connected by a force transfer system based on a Bowden cable and can be connected by a clip mechanism. In the hand module, three DOFs enable power, precision and lateral gripping: combined actuated flexion/extension of 2-5 digits, separate actuated flexion/extension of the thumb, and manual lateral and pad opposition of the thumb. The two DC motors are controlled by force feedforward and actuate the finger and thumb flexion/extension separately via a rack and pinion mechanism. The Myo armband wireless surface electromyographic EMG sensor was used for monitoring. Figure 5: (**a**) Close-up view of the Exo-Glove Poly; (**b**) Close-up view of the flexible rehabilitation glove designed by the National University of Singapore; (**c**) Close-up view of the Compliant Hand Based on a Novel Pneumatic Actuator designed by Harvard University [38; 39; 40; 41; 42; 43; 44; 45]. Figure 6: Close-up view of the portable, actuated glove-orthosis [46]. In 2022, [PERSON] et al. of the University of Hong Kong designed a probabilistic model-based learning control of a soft pneumatic glove for hand rehabilitation [49], as shown in Figure 9a. This soft-body pneumatic glove, a probabilistic model-based learning control method for an integrated \"hand-soft-body robot\" system and a task-oriented, intention-driven training model is proposed. [PERSON] et al. of the Institute of Robotics and Mechatronics, German Aerospace Center (DLR) designed an EMG-Driven machine learning control of a soft glove for grasping assistance and rehabilitation [50], as shown in Figure 9b. This glove is able to assist flexion of the index and middle finger, and flexion of the thumb, respectively, by means of a tendon-driven system. Two movements, namely flexion of the thumb and flexion of the fingers, are independently assisted by motors that pull and release the respective tendon cable. This glove has an intention recognition control system via EMG. Compiling an overview of hand rehabilitation robotics in recent years was shown in Table 1.  
Figure 8: Close-up view of the RELab tenoexo [48]. Figure 7: An attention-controlled hand exoskeleton. (**A**) CAD drawing of the index finger actuator; (**B**) bending motion generated by the proposed mutli-segment mechanism with a spring layer; (**C**) segment thicknesses (unit: mm); and (**D**) overview of the hand exoskeleton prototype [47]. Figure 9: (**a**) Close-up view of the probabilistic model-based learning control of a soft pneumatic glove; (**b**) Close-up view of the EMG-Driven machine learning control of a soft glove [49; 50].