IoT-based wearable devices are significantly contributing to personalized and pervasive modern healthcare. Traditional healthcare systems are handicapped by several factors, including shortages of physicians, nurses, healthcare devices, hospital beds, healthcare cost, etc. Conventional therapy is carried out either at a hospital or at home by certified therapists which is not affordable for many in developing nations. In this research work, we present IoT-based monitoring and an evaluation of key parameters and indicators of a wearable device used during the rehabilitation process in stroke patients’ hand therapy which can be operated by the users at home without the need for therapists. Sensors along with a controller board are used for signal acquisition, processing, and monitoring. The efficiency of therapy can be increased through real-time follow-up and feedback from therapists. Three different control methods are proposed and studied: smartphone-based speech, smartphone-based touch, and Internet of Things (IoT)-based dashboard. In addition, four different architectures, including: therapist therapy wearable, dual therapy wearable, user therapy wearable, and multiuser therapy wearable architectures are discussed. A rehabilitation therapy-based case study using the proposed wearable device with multiple volunteers was conducted. Therapists can remotely operate the device and train the users. Users can benefit and save on costs without the need to visit hospitals or therapy centers or hire therapists. Based on the results of the experiments with volunteers, we can confidently say that the proposed IoT-based wearable device can enhance the quality of life and well-being of the users.

Position and depth (PD) estimation is one of the key characteristics of autonomous robots. Robots are often challenged to visualize an alien environment remotely, alongside the control mechanism, and estimate the PD of objects. For the robot to reach out to an object, it needs to know the object's position in a 3-D space. The design of the robot's vision system is crucial. In this research work, we propose a human intervention-based hybrid approach for estimation of PD of an object. Human intervention in the form of a mouse click on the laser spot of the object image/in the video created by a camera-laser setup is used to estimate the PD of an object. An error correction model is developed and evaluated for better performance of the proposed method. A comparison of the proposed method with that of the image processing method revealed that a hybrid approach is almost 50% better in accuracy. The test results indicate that this method could be very helpful in finding the depth of the object with better accuracy in a teleoperated semiautonomous robot.

In this research work we propose a mechanism where six actuator nodes which represent the six degrees of freedom (DOF) of a robotic arm are connected via the Controller Area Network (CAN) bus to forms its communication network and receives commands from a common controller (Raspberry Pi). A simulation environment is created in the Gazebo simulator of host simulator and real time analysis of the robotic arm is done via Rviz. Through the simulation, the robotic arm receives instructions from a joystick integrated to the same host PC. The position of each joint with respect to the command from the joystick is sent over WiFi to the common controller and an indication that the message has been received is shown on hardware using the CAN network setup. The arrangement creates an effective framework for learning the working of complex robotic systems with several DOFs.

Robotic systems are rapidly emerging as easy-to-use rehabilitation tools that enhance several of health-related infirmities. Frequently, stroke and spinal cord -injuries are impair normal healthful living by paralysis, albeit the affected patient's brains may remain functional. Such victims need long-term rehabilitation, by qualified therapists to regain motor and sensory controls, beyond the affordable reach of common citizens. This is where reasonably priced robotic exoskeleton systems come to their rescue. In the paper, we propose a novel design for hand orthosis, which is a lightweight pliable prototype can be calibrated to fit any average-sized hand. This device is controlled with a voice recognition module. Efficiency, delay, accuracy, realization, and robustness. Empirical results validate the efficacy, reliability, and robustness of the orthotic device that can be used in the rehabilitation of the stroke patients.