A Critical Review on Factors Affecting the User Adoption of Wearable and Soft Robotics.

In recent years, the advent of soft robotics has changed the landscape of wearable technologies. Soft robots are highly compliant and malleable, thus ensuring safe human-machine interactions. To date, a wide variety of actuation mechanisms have been studied and adopted into a multitude of soft wearables for use in clinical practice, such as assistive devices and rehabilitation modalities. Much research effort has been put into improving their technical performance and establishing the ideal indications for which rigid exoskeletons would play a limited role. However, despite having achieved many feats over the past decade, soft wearable technologies have not been extensively investigated from the perspective of user adoption. Most scholarly reviews of soft wearables have focused on the perspective of service providers such as developers, manufacturers, or clinicians, but few have scrutinized the factors affecting adoption and user experience. Hence, this would pose a good opportunity to gain insight into the current practice of soft robotics from a user's perspective. This review aims to provide a broad overview of the different types of soft wearables and identify the factors that hinder the adoption of soft robotics. In this paper, a systematic literature search using terms such as "soft", "robot", "wearable", and "exoskeleton" was conducted according to PRISMA guidelines to include peer-reviewed publications between 2012 and 2022. The soft robotics were classified according to their actuation mechanisms into motor-driven tendon cables, pneumatics, hydraulics, shape memory alloys, and polyvinyl chloride muscles, and their pros and cons were discussed. The identified factors affecting user adoption include design, availability of materials, durability, modeling and control, artificial intelligence augmentation, standardized evaluation criteria, public perception related to perceived utility, ease of use, and aesthetics. The critical areas for improvement and future research directions to increase adoption of soft wearables have also been highlighted.

A Learning-Based Approach to Sensorize Soft Robots.

Soft actuators and their sensors have always been separate entities with two distinct roles. The omnidirectional compliance of soft robots thus means that multiple sensors have to be used to sense different modalities in the respective planes of motion. With the recent emergence of self-sensing actuators, the two roles have gradually converged to simplify sensing requirements. Self-sensing typically involves embedding a conductive sensing element into the soft actuator and provides multiple state information along the continuum. However, most of these self-sensing actuators are fabricated through manual methods, which results in inconsistent sensing performance. Soft material compliance also imply that both actuator and sensor exhibit nonlinear behaviors during actuation, making sensing more complex. In this regard, machine learning has shown promise in characterizing the nonlinear behavior of soft sensors. Beyond characterization, we show that applying machine learning to soft actuators eliminates the need to implant a sensing element to achieve self-sensing. Fabrication is done using 3D printing, thus ensuring that sensing performance is consistent across the actuators. In addition, our proposed technique is able to estimate the bending curvature of a soft continuum actuator and the external forces applied to the tip of the actuator in real time. Our methodology is generalizable and aims to provide a novel way of multimodal sensing for soft robots across a variety of applications.