As the global population ages, effective rehabilitation and mobility aids will become increasingly critical. Gait assistive robots are promising solutions, but designing adaptable controllers for various impairments poses a significant challenge. This paper presented a Human-In-The-Loop (HITL) simulation framework tailored specifically for gait assistive robots, addressing unique challenges posed by passive support systems. We incorporated a realistic physical human-robot interaction (pHRI) model to enable a quantitative evaluation of robot control strategies, highlighting the performance of a speed-adaptive controller compared to a conventional PID controller in maintaining compliance and reducing gait distortion. We assessed the accuracy of the simulated interactions against that of the real-world data and revealed discrepancies in the adaptation strategies taken by the human and their effect on the human's gait. This work underscored the potential of HITL simulation as a versatile tool for developing and fine-tuning personalized control policies for various users.

Robot-assisted feeding has the potential to enhance the independence of individuals requiring assistance, yet the bite transfer process remains particularly challenging, especially for those with complex conditions. In this paper, we present ORBiT, a novel Real2Sim2Real framework designed to optimize bite transfer in robot-assisted feeding. By integrating motion capture-driven, high-fidelity soft-body simulation with systematic parameter tuning, ORBiT effectively replicates realistic head, neck and jaw dynamics during feeding interactions to provide a safe simulation-driven approach to optimize bite transfer strategies. In our approach, motion capture data drives a personalized dynamic head model that, together with a comprehensive parameter search over variables such as entry angle, exit angle, exit depth, height offset, and distance to mouth, identifies the bite transfer parameters that minimize contact forces on the user. The optimal parameters are then transferred to a real-world robotic system and validated through a pilot user study involving five subjects. Results from real user evaluations mirror the trends in simulation, indicating that bite transfer parameters, especially those related to entry and exit angles, substantially affect user comfort and overall satisfaction. Our findings validate that simulation-derived optimizations can effectively guide improvements in bite transfer strategies, laying the groundwork for a safe, personalized approach to robot-assisted feeding. Supplementary videos can be found at: https://youtu.be/a2pklEIAkOA.

Low back pain is a widespread issue that affects people worldwide and can lead to serious conditions such as herniated discs, spinal stenosis, or lumbar radiculopathy. Improper posture while lifting heavy weights is a common cause of back pain, especially among laborers. However, current back exoskeletons are often bulky and require electric motors, making them challenging to use and consuming significant power. Some passive exoskeletons don’t require power, but their fixed stiffness constrains normal motion. This paper presents a novel solution: a pneumatic back exoskeleton made of structured fabrics that can adjust stiffness under various air pressures. Additionally, it includes IMU sensors to detect lifting posture and correct it in real time. The exoskeleton’s effectiveness was tested through lifting experiments, demonstrating that it significantly corrects lifting posture, reduces stress on the lumbar spine, and mitigates back muscle stress. This pneumatic back exoskeleton offers a promising solution to prevent low back pain during weight-lifting tasks and provides guidance for future back exoskeleton designs.

Tremors, which are prevalent symptoms in both Parkinson’s disease (PD) and essential tremor (ET), substantially diminish the quality of life for those affected. Traditional treatments, including pharmaceutical medications and invasive surgical procedures, often come with limitations and side effects, prompting the need for alternative solutions. In this paper, the Tunable Stiffness Glove (TSG) is developed as a non-invasive exoskeleton to suppress wrist tremors. By employing chain mail fabrics with tunable stiffness, the TSG permits natural wrist movement when in a soft state, while transitioning to a jammed state upon the application of negative pressure, effectively suppressing tremors in two directions. Evaluation of the TSG’s efficacy was conducted through comprehensive three-point bending tests and human trials, utilizing commercial mechanical tester, inertial measurement unit (IMU), and electromyography (EMG) sensors. Weighing a mere 92 grams, the TSG demonstrated remarkable tremor suppression rates of 74.86%±5.52% (in the flexion-extension direction) and 66.80%±15.47% (in the adduction-abduction direction). Future enhancements aim to optimize the design for increased damping force and integrate sophisticated control strategies for improved user-exoskeleton interaction.