Work-related musculoskeletal disorders (WMDs) affect a high percentage of operators performing repeated weight lifting and load carrying in industrial scenarios. Since upper limb muscles are affected in the process, the assistance provided by upper body exoskeletons is increasingly needed to prevent WMDs and their consequent cost to the health system. This paper presents the evaluation of Flexos, a portable, bilateral, shoulder exoskeleton prototype designed to assist logistic and industrial operators in performing occupational tasks. An in-lab assessment was conducted on twelve healthy subjects - 9 males, 3 females - to evaluate Flexos capability in assisting the user during the execution of isometric, dynamic, and carrying-load tasks. Different metrics were extracted from time-series signals to assess the effort related to five targeted muscles surrounding the shoulder complex. Despite the limited experimental size and the prototypal level of the device, Flexos managed to cover almost all the shoulders range of motion - 89.2% flexion/extension, and 88.4% internal/external rotation - and to globally decrease muscular activity in occupational activities, particularly when isometric contractions are required for a prolonged time, with average reductions of -27.2% for the static task, -18.6% for the dynamic task and -23.4% for the carrying-load task.

Patients suffering from neurological and musculoskeletal disorders often experience impaired upper limb function, significantly reducing their quality of life. In recent years, wearable robots have emerged as a promising solution to facilitate rehabilitation and assist in daily activities. Among these, tendon-driven actuation has been widely adopted; however, such systems face challenges in achieving precise position control compared to direct motor-driven systems. This is primarily due to the hysteresis and backlash resulting from the high compliance and elasticity of tendons, necessitating effective compensation strategies. In this paper, we implement an embedded compact sensor system for end-effector-level position tracking in a soft wearable robot designed for forearm pronation/supination and grasping motions. By integrating sensors at the end-effector, we enable real-time motion data acquisition and establish a closed-loop feedback mechanism that effectively compensates for the limitations of tendon-driven actuation, thereby enhancing overall control accuracy. Based on the embedded end-effect-level sensing system, we introduce a novel wearable robot kit for motion tracking that comprising two parts: a sensor-only exosuit for real-time capture of user hand and forearm movements, and a motor-equipped exosuit that replicates and assists movements based on the sensor feedback. This Leader-Follower Control Mode allows for accurate capture and rapid response to user motion intent, offering a new solution for applications in tele-control, mirror therapy, and motion synchronization.

Real-time continuous locomotion mode recognition and seamless timely transition detection is critical for the exoskeleton robot. This study aims to present a comprehensive and innovative framework for locomotion mode recognition and transition prediction, exclusively utilizing inertial measurement unit (IMU) signals from the exoskeleton. In this framework, a CNN-BiLSTM model was developed and trained to be the classifier and a novel majority filter was designed to reduce the transition misjudgment rate. Moreover, a comprehensive evaluation system encompassing eight dimensions for the classifier, incorporating evaluation metrics specifically for transition misjudgment, was proposed. We collected locomotion motion data from six subjects wearing a rigid exoskeleton robot using six IMU sensors on the exoskeleton. The proposed method achieves a high level of recognition accuracy, with an overall average of 99.58 for the five steady locomotion modes (level ground walking (LG), stair ascent/descent (SA/SD), and ramp ascent/descent (RA/RD)) across six subjects following the transition decision. All transitions are recognizable, and the majority can be predicted in advance, with an average prediction time of 353 ms. Furthermore, the implementation of majority filter resulted in an average 87.04 reduction in the transition misjudgment rate among six subjects, thereby decreasing the average transition misjudgment rate to 4.82. Finally, the model was tested on a Jetson Nano to verify its real-time performance. The results presented above were obtained under the condition where either leg could function as the first transition leg and revealed that the developed system was capable of achieving precise locomotion mode recognition and timely transition prediction, with high real-time performance.

Human beings adapt their motor patterns in response to their surroundings, utilizing sensory modalities such as visual inputs. This context-informed adaptive motor behavior has increased interest in integrating computer vision algorithms into robotic assistive technologies, marking a shift towardscontext aware control. However, such integration has rarely been achieved so far, with current methods mostly relying on data-driven approaches. In this study, we introduce a novel control framework for a soft hip exosuit, employing instead a physics-informed computer vision method grounded on geometric modeling of the captured scene for assistance tuning during stairs and level walking. This approach promises to provide a viable solution that is more computationally efficient and does not depend on training examples. Evaluating the controller with six subjects on a path comprising level walking and stairs, we achieved an overall detection accuracy of$93.0\pm 1.1\%$. Computer vision-based assistance provided significantly greater metabolic benefits compared to non-vision-based assistance, with larger energy reductions relative to being unassisted during stair ascent ($-18.9 \pm 4.1\%$vs.$-5.2 \pm 4.1\%$) and descent ($-10.1 \pm 3.6\%$vs.$-4.7 \pm 4.8\%$). Such a result is a consequence of the adaptive nature of the device, enabled by the context aware controller, that allowed for more effective walking support: i.e. the assistive torque showed a significant increase while ascending stairs ($+33.9\pm 8.8\%$) and decrease while descending stairs ($-17.4\pm 6.0\%$) compared to a condition without assistance modulation enabled by vision. These results highlight the potential of the approach, promoting effective real-time embedded applications in assistive robotics.

Sitting, standing, and walking are fundamental activities crucial for maintaining independence in daily life. However, aging or lower limb injuries can impede these activities, posing obstacles to individuals' autonomy. In response to this challenge, we developed the LM-Ease (lower-limb movement ease), a compact and soft wearable robot designed to provide hip assistance. Its purpose is to aid users in carrying out essential daily activities such as sitting, standing, and walking. The LM-Ease features a fully actuated tendon-driven system that seamlessly transitions between assistance actuation profiles tailored for sitting, standing, and walking movements. This device provides the user with gravity support during stand-to-sit, and offers hip extension assistance pulling force during sit-to-stand and walking. Our preliminary results show that with the LM-Ease, healthy young adults (n$=$8) had significantly lower muscle activation: average reduction of 15.6% during stand-to-sit and 17.8% during sit-to-stand. Furthermore, with LM-Ease, participants demonstrated a 12.7% reduction in metabolic cost during ground walking. These evidences suggest that the LM-Ease holds potential in reducing muscular activation and energy expenditure during these fundamental daily activities. It could serve as a valuable tool for individuals seeking assistance in enhancing lower limb mobility, thereby bolstering their independence and overall quality of life.

The intention-detection strategy used to drive an exosuit is fundamental to evaluate the effectiveness and acceptability of the device. Yet, current literature on wearable soft robotics lacks evidence on the comparative performance of different control approaches for online intention-detection. In the present work, we compare two different and complementary controllers on a wearable robotic suit, previously formulated and tested by our group; a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a force control that estimates human torques using an inverse dynamics model (dynamic arm). We test them on a cohort of healthy participants performing tasks replicating functional activities of daily living involving a wide range of dynamic movements. Our results suggest that both controllers are robust and effective in detecting human–motor interaction, and show comparable performance for augmenting muscular activity. In particular, the biceps brachii activity was reduced by up to 74% under the assistance of thedynamic armand up to 47% under themyoprocessor, compared to a no-suit condition. However, themyoprocessoroutperformed thedynamic armin promptness and assistance during movements that involve high dynamics. The exosuit work normalized with respect to the overall work was$68.84 \pm 3.81\%$when it was ran by themyoprocessor, compared to$45.29 \pm 7.71\%$during thedynamic armcondition. The reliability and accuracy of motor intention detection strategies in wearable device is paramount for both the efficacy and acceptability of this technology. In this article, we offer a detailed analysis of the two most widely used control approaches, trying to highlight their intrinsic structural differences and to discuss their different and complementary performance.

Wearable robots are undergoing a disruptive transition, from the rigid machines that populated the science-fiction world in the early 1980s to lightweight robotic apparel, hardly distinguishable from our daily clothes. In less than a decade of development, soft robotic suits have achieved important results in human motor assistance and augmentation. In this article, we start by giving a definition of soft robotic suits and proposing a taxonomy to classify existing systems. We then critically review the modes of actuation, the physical human–robot interface and the intention-detection strategies of state-of-the-art soft robotic suits, highlighting the advantages and limitations of different approaches. Finally, we discuss the impact of this new technology on human movements, for both augmenting human function and supporting motor impairments, and identify areas that are in need of further development.

In recent years, bowden-cable transmissions have been developed and utilized widely in many robotic applications due to advantages in durability, lightweight, safety, and flexibility. Especially, over the last decade, a substantial number of soft wearable exoskeletons using bowden cables for motion transmission have been designed for human assistance, empowerment and rehabilitation. The major advantage of soft assistive devices driven by bowden-cable transmissions is to allow decentralizing the actuation stages proximally such that their mass has the least effect on the end-effector. Besides the advantage, the main drawback of the bowden cable-driven system comes from the presence of nonlinearities such as friction and backlash hysteresis that affects their control accuracy. Hence, in this paper, we introduce a mathematical model for backlash hysteresis and propose a solution based on the nonlinear adaptive control to compensate for the backlash effect. The backlash hysteresis model and control scheme are validated first on a custom-designed test bench and then applied to control a soft exoskeleton in a preliminary human trial.

Robotic platforms developed to assist conventional motor therapy and to investigate human motor control have received an increasing interest over the past decades. However, for most of the proposed solutions, bulkiness and expensiveness have limited the use of such devices to specialized clinics that can afford their cost. This paper presents the H-Man, a two degree-of-freedom planar device designed according to three main principles: cost-effectiveness, portability and ease of control. The key component of the device is a planar H-shaped cable differential mechanism which ensures a constant Jacobian and homogeneous perceived inertia over the entire workspace. The paper presents the mechanical design as well as the performance evaluation in terms of perceived impedance.

Human arm muscular stiffness measurement is often a complex procedure which is of great interest for many disciplines from biomechanics to medicine and robotics. Modulation of impedance represents the principal mechanism underlying control of movements and interaction with external environment. Past literature proposed several methods to estimate multijoint hand stiffness while postural maintaining and dynamic tasks, mainly performed by means of planar robotic manipulanda. Despite these approaches are still considered robust and accurate, the computational burden of the robotic controller and hardware limitations make them not easy to implement. In the present paper a novel mechanism conceived for measuring multijoint planar stiffness by in single trial and in a reduced execution time is described and tested in different configurations. The device consisted in a mechanical rotary mechanism which applies cyclic radial perturbation to human arm of a known displacement and the force is acquired by means of a 6-axes commercial load cell. The outcomes suggest that the system is not only reliable in standalone mode but allows obtaining a reliable bi-dimensional estimation of arm stiffness even plugged in a planar manipulandum, dramatically reducing the amount of time for measurement and allowing to decouple the two controllers of the planar manipulator on which is mounted and the device itself.

This paper presents the design, and performance of a high fidelity three degree-of-freedom wrist exoskeleton robot, for neuroscience study, training and rehabilitation. The IIT-Wrist is intended to provide kinesthetic feedback during the training of motor skills or rehabilitation of reaching movements. Motivation for such applications is based on findings that show robot-assisted physical therapy aids in the rehabilitation process following neurological injuriesIn the present paper the IIT-Wrist haptic robot is described in terms of kinematics and haptics features to meet specific requirements for a safety human.machine interaction. In relation with a feasibility study in the field of robot therapy a preliminary training of stroke patient was perfrormed. The task consisted in tracking a target using one degree of freedom at time: Flexion/Extension, adduction/abduction, pronation/supination separately. The target motion is harmonic and tracking is aided by a suitable force field. The preliminary study with three patients shows the stability and the efficacy of the control scheme.