Upper limb dysfunction (ULD) is common following a stroke, spinal cord injury, trauma, and occupational accidents. Post-stroke patients with ULD need long-term assistance from therapists for their rehabilitation, which generally occurs at the hospital or outpatient clinic. Physical therapists are unavailable because of geographical, financial, and scheduling concerns, and continuity of care needs to be improved due to the need to travel to multiple locations for therapy. As a result, providing specific, tailored therapy programs is challenging due to the absence of feedback and real-time monitoring. An effective telerehabilitation system can address this issue and is more cost-effective for healthcare providers and patients than traditional inpatient or person-to-person rehabilitation. Remotely operating robotic devices and using advanced technology improves patient and healthcare provider safety and reduces injuries. In this study, we developed a novel telerehabilitation framework for rehabilitation robots utilizing PTC’s Industrial Internet of Things (IIoT) platform to remotely provide robot-aided therapies for individuals with ULD. With the developed telerehabilitation framework, an operator can teleoperate the rehab robots to deliver Upper-limb (UL) exercises via an Augmented Reality (AR) based graphical user interface (GUI). This AR platform communicates bidirectionally using ThingWorx IIOT. It leverages the digital twin (DT) structure facilitated by Vuforia studio to visualize the physical robot motions happening in remote places. The telerehabilitation framework was validated through a commercially available robot (xArm 5), an exoskeleton (SREx), and an end-effector type rehabilitation robot (DMRbot) developed at Biorobotics Lab, UWM. The experiment results show that the telerehabilitation system can successfully provide UL rehab exercises in 2D and 3D planes via AR. The proposed framework is developed to facilitate robust and more promising robot-aided rehabilitation sessions remotely, and it can also be applied in other medical applications.

Geriatric disorders, strokes, spinal cord injuries, trauma, and workplace injuries are all prominent causes of upper limb disability. A two-degrees-of-freedom (DoFs) end-effector type robot, iTbot (intelligent therapeutic robot) was designed to provide upper limb rehabilitation therapy. The non-linear control of iTbot utilizing modified sliding mode control (SMC) is presented in this paper. The chattering produced by a conventional SMC is undesirable for this type of robotic application because it damages the mechanical structure and causes discomfort to the robot user. In contrast to conventional SMC, our proposed method reduces chattering and provides excellent dynamic tracking performance, allowing rapid convergence of the system trajectory to its equilibrium point. The performance of the developed robot and controller was evaluated by tracking trajectories corresponding to conventional passive arm movement exercises, including several joints. According to the results of experiment, the iTbot demonstrated the ability to follow the desired trajectories effectively.

A significant number of individuals in the United States use assistive devices to enhance their mobility, and a considerable portion of those who depend on such aids require assistance from another individual in performing daily living activities. The introduction of robotic grippers has emerged as a transformative intervention, significantly contributing to the cultivation of independence. However, there are few grippers in the fields, which help with mimicking human hand-like movements (mostly grasping and pinching, with adoptive force control) to grasp and carry objects. Additionally, the data are not available even on how many Activities of Daily Living (ADL) objects they can handle. The goal of the research is to offer a new three-fingered gripper for daily living assistance, which can both grasp and pinch with adaptive force, enabling the capabilities of handling wide-ranging ADL objects with a minimal footprint. It is designed to handle 90 selective essential ADL objects of different shapes (cylindrical, irregular, rectangular, and round), sizes, weights, and textures (smooth, rough, bumpy, and rubbery). The gripper boasts a meticulously engineered yet simple design, facilitating seamless manufacturing through 3D printing technology without compromising its operational efficacy. The gripper extends its functionality beyond conventional grasping, featuring the capability to pinch (such as holding a credit card) and securely hold lightweight objects. Moreover, the gripper is adaptable to grasping various objects with different shapes and weights with controlled forces. In evaluation, the developed gripper went through rigorous load tests and usability tests. The results demonstrated that the users picked and placed 75 objects out of 90 daily objects. The gripper held and manipulated objects with dimensions from 25 mm to 80 mm and up to 2.9 kg. For heavy-weight objects (like books) where the centroid is far apart from the grasping areas, it is difficult to hold them due to high torque. However, objects’ textures have no significant effect on grasping performance. Users perceived the simplicity of the gripper. Further investigation is required to assess the utility and longevity of grippers. This study contributes to developing assistive robots designed to enhance object manipulation, thereby improving individuals’ independence and overall quality of life.

Conventional upper limb rehabilitation methods often encounter significant obstacles, including high costs, limited accessibility, and reduced patient adherence. Emerging technological solutions, such as telerehabilitation, virtual reality (VR), and wearable sensor-based systems, address some of these challenges but still face issues concerning supervision quality, affordability, and usability. To overcome these limitations, this study presents an innovative and cost-effective rehabilitation system based on advanced computer vision techniques and artificial intelligence (AI). Developed using Python (3.11.5), the proposed system utilizes a standard webcam in conjunction with robust pose estimation algorithms to provide real-time analysis of patient movements during guided upper limb exercises. Instructional exercise videos featuring an NAO robot facilitate patient engagement and consistency in practice. The system generates instant quantitative feedback on movement precision, repetition accuracy, and exercise phase completion. The core advantages of the proposed approach include minimal equipment requirements, affordability, ease of setup, and enhanced interactive guidance compared to traditional telerehabilitation methods. By reducing the complexity and expense associated with many VR and wearable-sensor solutions, while acknowledging that some lower-cost and haptic-enabled VR options exist, this single-webcam approach aims to broaden access to guided home rehabilitation without specialized hardware.

Background: Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. Objective: This study aims to present the development of a novel framework for the collaborative robot using mixed reality. Methods: The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality–based collaborative system. Results: A thorough experiment was conducted to validate the novel framework for mixed reality–based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5–degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. Conclusions: Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance.

Background: Upper-limb impairments following conditions such as stroke and spinal cord injury contribute significantly to long-term disability. Many survivors of stroke face limited access to rehabilitation due to geographical, financial, or scheduling barriers, leaving unmet therapeutic needs. Objective: This study conducted a preliminary evaluation of the usability of a novel telerehabilitation platform integrating a portable, desktop-mounted robot (DMRbotV3) with a mixed reality HoloLens 2 application to support accessible and adaptive upper-limb neurorehabilitation. Methods: This was a pilot usability study. Six participants, 3 (50%) stroke survivors (≥3 months after the event) recruited from a hospital stroke registry and 3 (50%) occupational therapists (≥1 year of clinical experience) recruited through convenience sampling in the Greater Milwaukee region, completed a single 2-hour session using the telerehabilitation platform in the BioRobotics laboratory at the University of Wisconsin-Milwaukee. Participants tried out the system, which delivered passive, active, and resistive exercises through DMRbotV3 combined with interactive mixed reality displays, and then completed a customized usability questionnaire. Data collected included (1) robotic parameters from onboard sensors (joint position, velocity, and interaction forces); (2) participant usability feedback from questionnaires to assess usability, satisfaction, and user engagement; and (3) documentation of adverse events and safety concerns. Descriptive statistics (mean scores and ranges) were used to analyze usability ratings and performance parameters. Results: The system was safe and well tolerated, with no adverse events reported. All participants completed the session, and usability scores averaged ≥4.0 across all items, reflecting high satisfaction and engagement with mixed reality integration. The robotic system demonstrated smooth performance, with controlled joint velocity profiles (–10 m/s to +10 m/s) and adaptive interaction forces up to approximately 25 N. Conclusions: This preliminary study supports the usability of combining robotics and mixed reality technologies into a telerehabilitation platform for upper-limb neurorehabilitation. Participant feedback also identified opportunities for refinement to enhance adaptability and personalization of therapy.