The wide adoption of occupational shoulder exoskeletons in industrial settings remains limited. Passive exoskeletons were proved effective in a limited amount of application scenarios, such as (quasi-)static overhead handling tasks. Quasi-active devices, albeit representing an improved version of their passive predecessors, do not allow full modulation of the amount of assistance delivered to the user, lacking versatility and adaptability in assisting various dynamic tasks. Active occupational shoulder exoskeletons could overcome these limitations by controlling the shape of the delivered torque profile according to the task they aim to assist. However, most existing active devices lack compactness and wearability. This prevents their implementation in working environments. In this work, we present a new active shoulder exoskeleton, named Active Exo4Work (AE4W). It features a new flexible shaft-driven remote actuation unit that allows the positioning of the motors close to the wearer’s center of mass while it maintains a kinematic structure that is compatible with the biological motion of the shoulder joint. in vitro and in vivo experiments have been conducted to investigate the performance of AE4W. Experimental results show that the exoskeleton is kinematically compatible with the user’s workspace since it does not constrain the natural range of motion of the shoulder joint. Moreover, this device can effectively provide different types of assistance while the user executes various dynamic tasks, without altering perceived comfort.

Machine – human interaction systems have been proposed to improve motion learning efficiency. We developed a pneumatic-driven motion teaching system that provides feedback to the learner by simultaneously presenting visual and torque information. We achieved a lightweight, soft, and user-safety haptic system using a pneumatic artificial muscle (PAM). The PAM’s shrink force was estimated based on its characteristic model and the suit link system, and the suit generated external torque. However, accurate force control was challenging due to the time delay of the feedback control, the loosening of the soft suit, and modeling errors of the driving PAM caused by hysteresis. To improve the force control performance of the motion teaching suit, this article’s contributions are to develop a novel suit in which PAMs for drive and force estimation are connected in series and implement a 2-degree-of-freedom (DOF) force control system using force estimation values in this suit and to confirm the effectiveness of the proposed hardware and software. This article contains three topics: (a) the development of novel suit hardware, (b) force estimation using a sealed small PAM, and (c) a proposal of force control using a 2-DOF controller. The effect of loosening the soft suit is reduced in the novel-developed suit. A sealed small PAM with small deformation and little hysteresis is adopted for force estimation. The time delay in feedback control is decreased by adopting the proposed novel 2-DOF control. Finally, the proposed suit and its control system were evaluated in experiments and achieved the desired performance.

Elastic textiles play a critical role in passive wearable solutions for musculoskeletal load management in both passive exosuits and resistance clothing. These textiles, based on their ability to stretch and retract, can exhibit ambivalence in their load-modulating effects when used in occupational, rehabilitation, exercise, or everyday activity settings. While passive exosuits and resistance garments may appear similar in design, they have opposing goals: to reduce the musculoskeletal load in the case of exosuits and to increase it in the case of resistance clothing. Despite this intrinsic connection, these two approaches have not been extensively linked together. This review aims to fill this gap by examining the common and distinct principles of elastic textiles in passive exosuits and resistance clothing, shedding light on their interactions and the complex dynamics of musculoskeletal load systems. The effectiveness of different designs in passive exosuits that mimic musculoskeletal function and resistance clothing that increase the workload for strength training are critically reviewed. Current challenges in practical implementation and opportunities to improve critical issues, such as preload, thermal comfort, skin friction, and donning and doffing are also highlighted.

State-of-the-art controllers for active back exosuits rely on body kinematics and state machines. These controllers do not continuously target the lumbosacral compression forces or adapt to unknown external loads. The use of additional contact or load detection could make such controllers more adaptive; however, it can be impractical for daily use. Here, we developed a novel neuro-mechanical model-based controller (NMBC) that uses a personalized electromyography (EMG)-driven musculoskeletal (MSK) model to estimate lumbosacral joint loading. NMBC provided adaptive, subject- and load-specific assistive forces proportional to estimates of the active part of biological joint moments through a soft back support exosuit. Without a priori information, the maximum assistive forces of the cable were modulated across weights. Simultaneously, we applied a non-adaptive, kinematic-dependent, trunk inclination-based controller (TIBC). Both NMBC and TIBC reduced the mean and peak biomechanical metrics, although not all reductions were significant. TIBC did not modulate assistance across weights. NMBC showed larger reductions of mean than peak values, significant reductions during the erect stance and the cumulative compressive loads by 21% over multiple cycles in a cohort of 10 participants. Overall, NMBC targeted mean lumbosacral compressive forces during lifting without a priori information of the load being carried. This may facilitate the adoption of non-hindering wearable robotics in real-life scenarios. As NMBC is informed by an EMG-driven MSK model, it is possible to tune the timing of NMBC-generated torque commands to the exosuit (delaying or anticipating commands with respect to biological torques) to target further reduction of peak or mean compressive forces and muscle fatigue.

Research in lower limb wearable robotic control has largely focused on reducing the metabolic cost of walking or compensating for a portion of the biological joint torque, for example, by applying support proportional to estimated biological joint torques. However, due to different musculotendon unit (MTU) contractile speed properties, less attention has been given to the development of wearable robotic controllers that can steer MTU dynamics directly. Therefore, closed-loop control of MTU dynamics needs to be robust across fiber phenotypes, that is ranging from slow type I to fast type IIx in humans. The ability to perform closed-loop control the in-vivo dynamics of MTUs could lead to a new class of wearable robots that can provide precise support to targeted MTUs for preventing onset of injury or providing precision rehabilitation to selected damaged tissues. In this paper, we introduce a novel closed-loop control framework that utilizes nonlinear model predictive control to keep the peak Achilles tendon force within predetermined boundaries during diverse range of cyclic force production simulations in the human ankle plantarflexors. This control framework employs a computationally efficient model comprising a modified Hill-type MTU contraction dynamics component and a model of the ankle joint with parallel actuation. Results indicate that the closed-form muscle-actuation model’s computational time is in the order of microseconds and is robust to different muscle contraction velocity properties. Furthermore, the controller achieves tendon force control within a time frame below $ 18\mathrm{ms} $, aligning with the physiological electromechanical delay of the MTU and facilitating its potential for future real-world applications.

Real-time measurement of head rotation, a primary human body movement, offers potential advantages in rehabilitating head or neck motor disorders, promoting seamless human–robot interaction, and tracking the lateral glance of children with autism spectrum disorder for effective intervention. However, existing options such as cameras capturing the entire face or skin-attached sensors have limitations concerning privacy, safety, and/or usability. This research introduces a novel method that employs a battery-free RFID tag-based wearable sensor for monitoring head orientation, as a substitute for the existing options like camera. By attaching a pair of passive RFID tags to the front of the head at a specific distance from each other, the signal strength of each tag within the pair differs based on the discrepancy in distance from the RFID reader caused by head rotation. Important parameters including distance between the tags, distance from the reader, and tag types, are investigated to suggest optimal sensor design. In tests involving random head rotations by 10 healthy adults, there was a significant correlation between the orientation of the head and gaze in the yaw direction and the differences in signal strength from the sensor pairs. The correlation coefficients ($ {r}^2 $) were satisfactory, at 0.88 for head and 0.83 for left eye pupil orientations. However, the sensor failed to estimate pitch rotations for head and gaze, due to the insufficient vertical spacing between the tags. No demographic factors appeared to influence the results.

Wearable pressure sensors with high sensitivity, fast response time, and low detection limit have great potential for blood pressure monitoring and early diagnosis of hypertension. This article introduces a piezoresistive pressure sensor based on carbon nanotubes (CNTs), polyaniline (PAni), and fabric (CNT/PAni/fabric) for health monitoring applications. This sensor is made by using two layers of linen fabric coated with CNT and PAni. These layers are placed on a polyester fabric substrate. One of the coated layers has a mesh structure, which increases the sensitivity of the sensor and lowers its detection limit. The CNT/PAni/fabric sensor has a high sensitivity of 2.035 kPa−1 at pressures from 0 to 0.2 kPa, a response time of 290 ms, and a detection limit of 1.5 Pa. These features make it suitable for measuring blood pressure. The results obtained by measuring blood pressure using the pulse transit time method on four people, compared with the values obtained using the digital sphygmomanometer, show a discrepancy ranging between 0.019% and 1.62%. Also, the average error and standard deviation for the sensor measurement in systolic and diastolic pressures are 0.56 ± 0.33 and 0.57 ± 0.46, respectively, which shows that measurement with this sensor can be an alternative to existing devices.

Assessment of occupational exoskeletons should ideally include longitudinal and multistage studies in real working scenarios to prove their effectiveness and sustainability in real in-field contexts and to help generalize the findings for specific scenarios. This work presents a comprehensive assessment methodology implemented as a multistage experimental campaign for rail industry workers using a back-support exoskeleton (StreamEXO). This work demonstrates that a sector/task-specific exoskeleton developed to address work task-specific requirements generates beneficial performance and user experience results. The experimental work in this paper involves collecting data from nine workers over multiple days of testing. During this testing, workers did not report hindrances to their work operations, with an acceptance rate of 86%. In addition, worker fatigue was reduced by 16.9% as measured through metabolic consumption, and 51% when assessed by perceived effort. This work supports the hypothesis that sector/task-specific exoskeletons when tailored to meet the needs of workers and the work tasks can produce demonstrable benefits in real industrial sectors.

We have developed an interactive system comprising a soft wearable robot hand and a wireless task board, facilitating the interaction between the hand and regular daily objects for task-oriented training in stroke rehabilitation. A ring-reinforced soft actuator (RSA) to accommodate different hand sizes and enable flexion and extension movements was introduced in this paper. Individually controlled finger actuators assist stroke patients during various grasping tasks. A wireless task board was developed to support the training, allowing for the placement of training objects and seamless interaction with the soft robotic hand. Evaluation with seven stroke subjects shows significant improvements in upper limb functions (FMA), hand-motor abilities (ARAT, BBT), and maximum grip strengths after 20 sessions of this task-oriented training. These improvements were observed to persist for at least 3 months post-training. The results demonstrate its potential to enhance stroke rehabilitation and promote hand-motor recovery. This lightweight, user-friendly interactive system facilitates frequent hand practice and easily integrates into regular rehabilitation therapy routines.

Sensorized insoles provide a tool for gait studies and health monitoring during daily life. For users to accept such insoles, they need to be comfortable and lightweight. Previous research has demonstrated that sensorized insoles can estimate ground reaction forces (GRFs). However, these insoles often assemble commercial components restricting design freedom and customization. Within this work, we incorporated four 3D-printed soft foam-like sensors to sensorize an insole. To test the insoles, we had nine participants walk on an instrumented treadmill. The four sensors behaved in line with the expected change in pressure distribution during the gait cycle. A subset of this data was used to identify personalized Hammerstein–Wiener (HW) models to estimate the 3D GRFs while the others were used for validation. In addition, the identified HW models showed the best estimation performance (on average root mean squared (RMS) error 9.3%, $ {R}^2 $=0.85 and mean absolute error (MAE) 7%) of the vertical, mediolateral, and anteroposterior GRFs, thereby showing that these sensors can estimate the resulting 3D force reasonably well. These results were comparable to or outperformed other works that used commercial force-sensing resistors with machine learning. Four participants participated in three trials over a week, which showed a decrease in estimation performance over time but stayed on average 11.35% RMS and 8.6% MAE after a week with the performance seeming consistent between days two and seven. These results show promise for using 3D-printed soft piezoresistive foam-like sensors with system identification regarding the viability for applications that require softness, lightweight, and customization such as wearable (force) sensors.

This paper investigates hand grasping, a fundamental activity in daily living, by examining the forces and postures involved in the lift-and-hold phases of grasping. We introduce a novel multi-sensory data glove, integrated with resistive flex sensors and capacitive force sensors, to measure the intricate dynamics of hand movement. The study engaged five subjects to capture a comprehensive dataset that includes contact forces at the fingertips and joint angles, furnishing a detailed portrayal of grasp mechanics. Focusing on grasp synergies, our analysis delved into the quantitative relationships between the correlated forces among the fingers. By manipulating one variable at a time—either the object or the subject—our cross-sectional approach yields rich insights into the nature of grasp forces and angles. The correlation coefficients for finger pairs presented median values ranging from 0.5 to nearly 0.9, indicating varying degrees of inter-finger coordination, with the thumb-index and index-middle pairs exhibiting particularly high synergy. The findings, depicted through spider charts and correlation coefficients, reveal significant patterns of cooperative finger behavior. These insights are crucial for the advancement of hand mechanics understanding and have profound implications for the development of assistive technologies and rehabilitation devices.

Advanced myoelectric prostheses feature multiple degrees of freedom (DoFs) and sophisticated control algorithms that interpret user motor intentions as commands. While enhancing their capability to assist users in a wide range of daily activities, these control solutions still pose challenges. Among them, the need for extensive learning periods and users’ limited control proficiency. To investigate the relationship between these challenges and the limited alignment of such methods with human motor control strategies, we examine motor learning processes in two different control maps testing a synergistic myoelectric system. In particular, this work employs a DoF-wise synergies control algorithm tested in both intuitive and non-intuitive control mappings. Intuitive mapping aligns body movements with control actions to replicate natural limb control, whereas non-intuitive mapping (or non-biomimetic) lacks a direct correlation between aspects, allowing one body movement to influence multiple DoFs. The latter offers increased design flexibility through redundancy, which can be especially advantageous for individuals with motor disabilities. The study evaluates the effectiveness and learning process of both control mappings with 10 able-bodied participants. The results revealed distinct patterns observed while testing the two maps. Furthermore, muscle synergies exhibited greater stability and distinction by the end of the experiment, indicative of varied learning processes.