## 2 Related Works  
Over the past few decades, there has been quite a lot of work being conducted in this field of hand rehabilitation robotics. According to recent surveys by World Health Organization (WHO), more than 15% of people in the world are suffering from physical or mental disabilities, among which a huge number of them are suffering especially from motor impairments due to various stroke and spinal cord injuries. An outline and analyses of the elemental reasons for motor impairments and the necessity of developing rehabilitation devices are discussed in [18]. A hand extension robot orthosis glove is proposed in the research paper [19] for testing with stroke survivors with severe hand impairment. The patients showed significant improvement when performing different functional tasks. A significant increase in extension of the index finger of the patient was also observed after using the device. The authors' focus is on development and testing of the glove and hence, IoT-based cloud benefits are not considered. The paper [20] presents an EMG (Electromyogram)-based wearable hand robot with user-driven functional movement training for stroke patients. The device aided the patient with performing everyday activities and hand exercises. This device also does not log user exercise data into cloud storage via IoT for future analysis. A neuromuscular-electrical-stimulation-based wearable system for hand function restoration is presented in [21]. The device helped the impaired patients in restoring their grasping function. The device was able to provide stimulation to both intrinsic and extrinsic muscles of the hand, which improved the clinical efficacy and flexibility of the device. The wearable is designed for the forearm with electrodes attached to the palm and is quite complex to wear and use. In addition, there is no provision for live sensor data storage using IoT. In [22], the authors proposed a wearable, spring-powered hand exoskeleton to be used in the therapy of stroke patients. This exoskeleton device helped in significantly improving both range of motion and maximum extension angles of the hand. As this device operation is spring-based and no electronics are involved for sensing or control, it was relatively cheaper. However, the disadvantage is that there was no possibility of storage of user exercise data. An EMG-based, IoT-enabled, smart wearable armband, along with real-time assistance from a robot hand, is used in the therapy of stroke patients [23]. The system also helped in successfully identifying gestures of the user. The cost of the proposed device could be very high. One research paper [24] presents a wearable hand rehabilitation system with two soft gloves, one to be worn on the non-affected hand and the other to be worn on the affected hand. This is a complex system with sophisticated sensors, actuators, and controllers along with machine-learning algorithms to recognize the gestures of the non-affected hand and to facilitate rehabilitation tasks for the affected hand. A five-fingered exoskeleton, which is made by poly-pyrrole strain sensors, allows for 19 Degree of Freedom (DOF) of independent movement for each phalange [25]. The novel strain sensors made the device portable and also made the exoskeleton, a lightweight one. Figure 1: The proposed wearable device for hand therapy. Authors of the research work [26] are successful in designing a well-developed mechanical device for control of speed, position, and torque of actuators for each finger joint. In [27], mimicking individual hand movements of a healthy person to control the hand orthotic device is proposed. This system can provide a grasping sensation to the operator. A voice-based hand orthotic device is presented in [28]. A wearable, low-cost hand orthotic device with a graphical user interface control is proposed in [29]. As we see in all the research work above, the focus is on making a successful wearable device for hand therapy but integration of the system with IoT cloud is not considered.