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.

Introduction. Impaired proprioception severely affects the control of gross and fine motor function. However, clinical assessment of proprioceptive deficits and its impact on motor function has been difficult to elucidate. Recent advances in haptic robotic interfaces designed for sensorimotor rehabilitation enabled the use of such devices for the assessment of proprioceptive function. Purpose. This study evaluated the feasibility of a wrist robot system to determine proprioceptive discrimination thresholds for two different DoFs of the wrist. Specifically, we sought to accomplish three aims: first, to establish data validity; second, to show that the system is sensitive to detect small differences in acuity; third, to establish test-retest reliability over repeated testing. Methodology. Eleven healthy adult subjects experienced two passive wrist movements and had to verbally indicate which movement had the larger amplitude. Based on a subject’s response data a psychometric function was fitted and the wrist acuity threshold was established at the 75% correct response level. A subset of five subjects repeated the experimentation three times (T1, T2 and T3) to determine the test-retest reliability. Results. Mean threshold for wrist flexion was 2.15°± 0.43° and 1.52°± 0.36° for abduction. Encoder resolutions were 0.0075° (flexion-extension) and 0.0032° (abduction-adduction). Motor resolutions were 0.2° (flexion-extension) and 0.3° (abduction-adduction). Reliability coefficients were rT2-T1=0.986 and rT3-T2=0.971. Conclusions. We currently lack established norm data on the proprioceptive acuity of the wrist to establish direct validity. However, the magnitude of our reported thresholds are physiological plausible and well in line with available threshold data obtained at the elbow joint. Moreover, system has high resolution and is sensitive enough to detect small differences in acuity. Finally, the system produces reliable data over repeated testing.

The development of a portable assistive device to aid patients affected by neuromuscular disorders has been the ultimate goal of assistive robots since the late 1960s. Despite significant advances in recent decades, traditional rigid exoskeletons are constrained by limited portability, safety, ergonomics, autonomy and, most of all, cost. In this study, we present the design and control of a soft, textile-based exosuit for assisting elbow flexion/extension and hand open/close. We describe a model-based design, characterisation and testing of two independent actuator modules for the elbow and hand, respectively. Both actuators drive a set of artificial tendons, routed through the exosuit along specific load paths, that apply torques to the human joints by means of anchor points. Key features in our design are under-actuation and the use of electromagnetic clutches to unload the motors during static posture. These two aspects, along with the use of 3D printed components and off-the-shelf fabric materials, contribute to cut down the power requirements, mass and overall cost of the system, making it a more likely candidate for daily use and enlarging its target population. Low-level control is accomplished by a computationally efficient machine learning algorithm that derives the system’s model from sensory data, ensuring high tracking accuracy despite the uncertainties deriving from its soft architecture. The resulting system is a low-profile, low-cost and wearable exosuit designed to intuitively assist the wearer in activities of daily living.

In the field of rehabilitation robotics, transparent, precise and intuitive control of hand exoskeletons still represents a substantial challenge. In particular, the use of compliant systems often leads to a trade-off between lightness and material flexibility, and control precision. In this letter, we present a compliant, actuated glove with a control scheme to detectthe user’s motion intent, which is estimated by a machine learning algorithm based on muscle activity. Six healthy study participants used the glove in three assistance conditions during a force reaching task. The results suggest that active assistance from the glove can aid the user, reducing the muscular activity needed to attain a medium-high grasp force, and that closed-loop control of a compliant assistive glove can successfully be implemented by means of a machine learning algorithm.

Reproducing the mechanisms of human locomotion is a hard challenge. Assistive wearable devices in this context need to be lightweight, portable, and to adapt to the wearer’s walking pattern. Aiming to combine the aforementioned features, we developed a soft wearable exosuit to assist hip flexion during walking. The main feature of the device is underactuation, i.e., a single actuator is used to assist bilateral hip flexion. A control strategy based on Adaptive Oscillators and gait phase estimation is implemented to deliver flexible assistance and to adapt to changes in walking pattern. The system was tested on six healthy subjects. Preliminary results show good performance of the control algorithm to track human gait and to deliver symmetrical assistance to both legs. First kinematic and muscular assessments revealed promising results at walking speeds near to the natural human walking, where the device did not alter hip kinematics and decreased the effort required by muscles contributing to hip flexion. Once validated on a larger sample of subjects, the device can have the potentialities to be used in clinical and wellness applications.