This letter presents a pneumatic artificial muscle-based elbow exosuit designed for driving and assisting the elbow joint. By combining the bionic attachment method inspired by the brachioradialis muscle and the pennation architecture of the artificial muscle, the elbow exosuit achieves high output torque while maintaining a slim profile of only 10 mm thickness. To guide the design process, the letter utilizes the force balance condition of infinitesimal parts of the artificial muscle fiber and the virtual work principle to calculate the path change of the muscle wrapped around the arm surface. This approach helps determine key parameters and obtain the theoretical assisting torque. To validate the model, a test bench is constructed to measure the relationship between assisting torque and flexion angle, demonstrating the accuracy of the theoretical calculations. Furthermore, a bionic elbow joint is built to verify the driving ability of the exosuit. Preliminary human feasibility test is conducted, and users report no discomfort. The results indicate a 37.5% decrease in muscle effort in the bicep brachii when lifting a 4 kg weight and a 30.5% decrease during the holding task. A stroke survivor successfully bends the arm and raises his hand to the mouth while wearing the exosuit. These findings demonstrate the feasibility in driving and providing assistance to the user and significant potential for assisting individuals in their daily lives.

Stroke is a leading cause of long-term disability worldwide, necessitating efficient motor function assessment to guide personalized rehabilitation. The Fugl–Meyer Assessment for the Upper Extremity (FMA-UE) is a clinical gold-standard tool, but it is time consuming and requires trained clinicians, which limits its frequency of use and accessibility. While wearable sensors and deep learning offer promising avenues for remote assessment, accurately estimating detailed sub-scores of specific motor functions remains a significant challenge. This work introduces a deep learning framework for automated estimation of FMA-UE total and subdivision scores. Data was collected from 15 participants using four inertial measurement units (IMUs) positioned on the arm and trunk. Each participant performed seven specialized functional motions designed for comprehensive joint synergy involvement within ten minutes. A therapist-rated FMA-UE provided true scores. The proposed model leverages the integration of an LSTM-based autoencoder and mixup augmentation to enhance generalization and robustness. Evaluated through a leave-one-subject-out cross-validation (LOSOCV), the estimator demonstrated strong performance, achieving R² values exceeding 0.82. Pearson’s correlation coefficient r was more than 0.90, and the normalized root-mean-square errors (NRMSE) were below 0.14 for all subparts (A–D). Crucially, the total FMA-UE score was estimated with an NRMSE of 0.0678. These results show that a concise, sensor-based assessment can reliably predict detailed motor function scores.

Artificial muscles have been in development for decades and play a crucial role in the field of wearable robotics. However, a universal device for both sensing and regulating forces in artificial muscles is still absent, rendering scenario-required force control unfeasible. Inspired by Golgi tendon organs, this work proposes bionic ExoTendon for force sensing and regulating in artificial muscles as a general solution. Leveraging the principles of triboelectrification and electrostatic induction, the ExoTendon offers high linearity, minimal hysteresis, and adjustable sensitivity and range, making it suitable as the tendon of artificial muscles. Specifically, the ExoTendon effectively enable adjustment to the pretension of the artificial muscles, yielding various assistive forces under the same input. With the optimized pretension level and closed-loop force control, the artificial muscle–based exosuit achieves self–force regulation, substantially improving walking balance and speed in stroke subjects under low input, demonstrating its promise in wearable robotics and rehabilitation medicine.

The pediatric period is a crucial window for motor function learning and growth. Individuals with central nervous system injuries like cerebral palsy commonly display severe crouch gait in the lower limbs. Hyperflexion of the knee joints promotes the forward trunk and increases reliance on the handle frame of a walker for support. In this study, we developed a quantitative partial body weight training system integrated with a soft pneumatic exosuit to assist the knee extension during the stance phase of the gait cycle. In the preliminary results for five pediatric cerebral palsy subjects, compared to the baseline condition, excessive knee flexion ameliorated with the assistance of the soft pneumatic exosuit. The peak knee extension and range of motion increased by 19.72° (±3.47°) and 15.46° (±5.06°), respectively. With exosuit assistance, the subjects demonstrated improved gait retraining compared to baseline. They were able to bear significantly more body weight on their affected limb, as evidenced by a 33.3% increase in the fraction of body weight measured by the force plate. Additionally, they relied less on the handrail for support during walking. With more extended knee joints to bear the load over gravity, the pediatric subjects transferred the reliance from external support and upper limbs back to the lower limbs as a more independent status during the loading response to terminal stance.