Hand exoskeletons have significant potential in labor-intensive fields by mitigating hand grip fatigue, enhancing hand strength, and preventing injuries.However, most traditional hand exoskeletons are driven by motors whose output force is limited under constrained installation conditions. In addition, they also come with the disadvantages of high power consumption, complex and bulky assistive systems, and high instability.In this work, we develop a novel hand exoskeleton integrated with magnetorheological (MR) clutches that offers a high force-to-power ratio to improve grip endurance. The clutch features an enhanced structure design, a micro roller enhancing structure, which can significantly boost output forces. The experimental data demonstrate that the clutch can deliver a peak holding force of 380 N with a consumption of 1.48 W, yielding a force-to-power ratio of 256.75N/W, which is 2.35 times higher than the best reported actuator used for hand exoskeletons. The designed MR hand exoskeleton is highly integrated and comprises an exoskeleton frame, MR clutches, a control unit, and a battery. Evaluations through static grip endurance tests and dynamic carrying and lifting tests confirm that the MR hand exoskeleton can effectively reduce muscle fatigue, extend grip endurance, and minimize injuries. These findings highlight its strong potential for practical applications in repetitive tasks such as carrying and lifting in industrial settings.

The complex ways in which humans utilize their bodies in sports and martial arts are remarkable, and human motion analysis is one of the most effective tools for robot body design and control. On the other hand, motion analysis is not easy, and it is difficult to measure complex body motions in detail due to the influence of numerous muscles and soft tissues, mainly ligaments. In response, various musculoskeletal simulators have been developed and applied to motion analysis and robotics. However, none of them reproduce the ligaments but only the muscles, nor do they focus on the shoulder complex, including the clavicle and scapula, which is one of the most complex parts of the body. Therefore, in this study, a detailed simulation model of the shoulder complex including ligaments is constructed. The model will mimic not only the skeletal structure and muscle arrangement but also the ligament arrangement and maximum muscle strength. Through model predictive control based on the constructed simulation, we confirmed that the ligaments contribute to joint stabilization in the first movement and that the proper distribution of maximum muscle force contributes to the equalization of the load on each muscle, demonstrating the effectiveness of this simulation.

The hybridisation of robot-assisted gait training and functional electrical stimulation (FES) can provide numerous physiological benefits to neurological patients. However, the design of an effective hybrid controller poses significant challenges. In this over-actuated system, it is extremely difficult to find the right balance between robotic assistance and FES that will provide personalised assistance, prevent muscle fatigue and encourage the patient's active participation in order to accelerate recovery. In this paper, we present an adaptive hybrid robot-FES controller to do this and enable the triadic collaboration between the patient, the robot and FES. A patient-driven controller is designed where the voluntary movement of the patient is prioritised and assistance is provided using FES and the robot in a hierarchical order depending on the patient's performance and their muscles' fitness. The performance of this hybrid adaptive controller is tested in simulation and on one healthy subject. Our results indicate an increase in tracking performance with lower overall assistance, and less muscle fatigue when the hybrid adaptive controller is used, compared to its non adaptive equivalent. This suggests that our hybrid adaptive controller may be able to adapt to the behaviour of the user to provide assistance as needed and prevent the early termination of physical therapy due to muscle fatigue.

Assessing the progression of muscle fatigue for daily exercises provides vital indicators for precise rehabilitation, personalized training dose, especially under the context of Metaverse. Assessing fatigue of multi-muscle coordination-involved daily exercises requires the neuromuscular features that represent the fatigue-induced characteristics of spatiotemporal adaptions of multiple muscles and the estimator that captures the time-evolving progression of fatigue. In this paper, we propose to depict fatigue by the features of muscle compensation and spinal module activation changes and estimate continuous fatigue by a physiological rationale model. First, we extract muscle synergy fractionation and the variance of spinal module spikings as features inspired by the prior of fatigue-induced neuromuscular adaptations. Second, we treat the features as observations and develop a Bayesian Gaussian process to capture the time-evolving progression. Third, we solve the issue of lacking supervision information by mathematically formulating the time-evolving characteristics of fatigue as the loss function. Finally, we adapt the metrics that follow the physiological principles of fatigue to quantitatively evaluate the performance. Our extensive experiments present a 0.99 similarity between days, a over 0.7 similarity with other views of fatigue and a nearly 1 weak monotonicity, which outperform other methods. This study would aim the objective assessment of muscle fatigue.

A new wearable gadget that fixes to the arm can measure blood sugar and muscle fatigue at the gym and alcohol levels at the pub. Created in California, the prototype can continuously monitor three health stats – glucose, alcohol and lactate levels – either separately or simultaneously in real-time. About the size of three poker chips stacked together, it is applied to the skin painlessly through a Velcro-like patch of microscopic needles. These needles take readings from fluid under the skin and then sends the data wirelessly to a custom smartphone app. Researchers hope to commercialise the device, which could provide a single solution for diabetes patients in everyday life.

Functional Electrical Stimulation (FES) is an established and safe technique for contracting muscles by stimulating the skin above a muscle to induce its contraction. However, an open challenge remains on how to restore motor abilities to human limbs through FES, as the problem of controlling the stimulation is unclear. We are taking a robotics perspective on this problem, by developing robot learning algorithms that control the ultimate humanoid robot, the human body, through electrical muscle stimulation. Human muscles are not trivial to control as actuators due to their force production being non-stationary as a result of fatigue and other internal state changes, in contrast to robot actuators which are wellunderstood and stationary over broad operation ranges. We present our Deep Reinforcement Learning approach to the control of human muscles with FES, using a recurrent neural network for dynamic state representation, to overcome the unobserved elements of the behaviour of human muscles under external stimulation. We demonstrate our technique both in neuromuscular simulations but also experimentally on a human. Our results show that our controller can learn to manipulate human muscles, applying appropriate levels of stimulation to achieve the given tasks while compensating for advancing muscle fatigue which arises throughout the tasks. Additionally, Figure 1: Our 3 scenarios for FES control: (a) arm vertical motion our technique can learn quickly enough to be implemented in in simulation (b) and human volunteers, (c) arm horizontal motion real-world human-in-the-loop settings.

A new research project in Australia is using motion detectors and muscle sensors to track sheep shearers in an effort to minimize on the-job-injuries. Sheep shearers are six times more likely to be injured in the workplace than the average Australian worker. Data from sensors attached to sheep shearers will be used to model worker movement throughout the workday and test new ways of doing the job without risking injury. The study, a joint project between University of Melbourne and the trade group Australian Wool Innovation, uses sensors to measure electrical activity in muscles. These sensors are placed directly on the skin of the lower back and upper thighs, the ABC reported, while motion detectors are placed around the joints to track a worker's posture and shearing motions.