Most wearable robots such as exoskeletons and prostheses can operate with dexterity, while wearers do not perceive them as part of their bodies. In this perspective, we contend that integrating environmental, physiological, and physical information through multi-modal fusion, incorporating human-in-the-loop control, utilizing neuromuscular interface, employing flexible electronics, and acquiring and processing human-robot information with biomechatronic chips, should all be leveraged towards building the next generation of wearable robots. These technologies could improve the embodiment of wearable robots. With optimizations in mechanical structure and clinical training, the next generation of wearable robots should better facilitate human motor and sensory reconstruction and enhancement.

This article presents U 2PNet, a novel unsupervised underwater image restoration network using polarization for improving signal-to-noise ratio and image quality in underwater imaging environments. Traditional methods for underwater image restoration using polarization require specific cues or pairs of underwater polarization datasets, which limit their practical applications. Our proposed method requires only one mosaicked polarized image of the scene and does not require datasets for pretraining or specific cues. We design two subnetworks (T-net and B textsubscript $\infty $ -net) to accurately estimate the transmission map and background light, and unique nonreference loss functions to ensure effective restoration. Our experiments are based on an indoor polarization simulated dataset and a real polarization image dataset constructed from our underwater robotic platform equipped with polarization cameras. Experiment results demonstrate that our proposed method achieves state-of-the-art performance on both simulated and real underwater polarization images. The code and datasets will be available at https://github.com/polwork/U-2Pnet .

Soft exo-suit could facilitate walking assistance activities (such as level walking, upslope, and downslope) for unimpaired individuals. In this article, a novel human-in-the-loop adaptive control scheme is presented for a soft exo-suit, which provides ankle plantarflexion assistance with unknown human-exosuit dynamic model parameters. First, the human-exosuit coupled dynamic model is formulated to express the mathematical relationship between the exo-suit actuation system and the human ankle joint. Then, a gait detection approach, including plantarflexion assistance timing and planning, is proposed. Inspired by the control strategy that is used by the human central nervous system (CNS) to handle interaction tasks, a human-in-the-loop adaptive controller is proposed to adapt the unknown exo-suit actuator dynamics and human ankle impedance. The proposed controller can emulate human CNS behaviors which adapt feedforward force and environment impedance in interaction tasks. The resulting adaptation of actuator dynamics and ankle impedance is demonstrated with five unimpaired subjects and implemented on a developed soft exo-suit. The human-like adaptivity is performed by the exo-suit in several human walking speeds and illustrates the promising potential of the novel controller.

Variable stiffness mechanisms establish a bridge between rigid, precise robots and soft, compliant robots. In certain applications it is necessary to exert high forces while having compliance, such as in wearable robots, high payload soft grippers, and manipulators. Negative pressure based jamming approaches have been widely used to tune variable stiffness. However, this variation of stiffness and strength is limited to negative one atmospheric pressure. To enable a wider pressure range which potentially enable wider stiffness range, we proposed a Positive Pressure Jamming (PPJ) approach. The PPJ is fabricated by embedding a pneumatic actuator inside a cylindrical non-stretchable fabric sleeve which contains granules. Pressurizing air into the pneumatic actuator will jam the granules, thus stiffening the soft body. To add degrees of freedom for the PPJ, we place a pair of friction pads in parallel between two PPJ units which act as a self-locking revolute joint. Both the PPJs and self-locking joint can be stiffened at the same time thus enabling the structure to be jammed in multiple configurations. Experiments showed that approximately six-fold stiffness increment (0.69N/mm to 4.02N/mm) could be achieved by our PPJ when input pressure is changed from 68 kPa to 172 kPa with exerted force ranges from 4N to 23N. Furthermore, we apply the proposed PPJ method into limb support devices, demonstrating a promising potential that PPJ has in wearable robot field.

Elevated impact loading can be detrimental to runners as it has been linked to the increased risk of tibial stress fracture and plantar fasciitis. The objective of this study was to investigate the combined effects of foot strike pattern, step rate, and anterior trunk lean gait modifications on impact loading in runners. Nineteen healthy runners performed 12 separate gait modification trials involving: three foot strike patterns (rearfoot, midfoot, and forefoot strike), two step rates (natural and 10% increased), and two anterior trunk lean postures (natural and 10-degree increased flexion). Overall, forefoot strike combined with increased step rate led to the lowest impact loading rates, and rearfoot strike combined with anterior trunk lean led to the highest impact loading rates. In addition, there were interaction effects between foot strike pattern and step rate on awkwardness and effort, such that it was both more natural and easier to transition to a combined gait modification involving forefoot strike and increased step rate than to an isolated gait modification involving either forefoot strike or increased step rate. These findings could help to inform gait modifications for runners to reduce impact loading and associated injury risks. (C) 2019 Elsevier Ltd. All rights reserved.