Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.

This paper describes a series of experiments designed to verify a new method of electroneurogram (ENG) recording that enables the rate of neural firing within prescribed bands of propagation velocity to be determined in real time. Velocity selective recording (VSR) has been proposed as a solution to the problem of increasing the information available from an implantable neural interface (typically with electrodes in circumferential nerve cuffs) and has been successful in transforming compound action potentials into the velocity domain. The new method extends VSR to naturally-evoked (physiological) ENG in which the rate of neural firing at particular velocities is required in addition to a knowledge of the velocities present in the recording. The experiments, carried out in rats required individual spikes to be distinct and non-overlapping, which could be achieved by a microchannel or small-bore cuff. In these experiments, strands of rat nerve were laid on ten hook electrodes in oil to demonstrate the principle. The new method generates a detailed overview of the firing rates of neurons based on their conduction velocity and direction of propagation. In addition it allows real time working in contrast to existing spike sorting methods using statistical pattern processing techniques. Results show that by isolating neural activity based purely on conduction velocity it was possible to determine the onset of direct cutaneous stimulation of the L5 dermatome.

The addition of sensory feedback to upper-limb prostheses has been shown to improve control, increase embodiment, and reduce phantom limb pain. However, most commercial prostheses do not incorporate sensory feedback due to several factors. This paper focuses on the major challenges of a lack of deep understanding of user needs, the unavailability of tailored, realistic outcome measures and the segregation between research on control and sensory feedback. The use of methods such as the Person-Based Approach and co-creation can improve the design and testing process. Stronger collaboration between researchers can integrate different prostheses research areas to accelerate the translation process.

Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within a multidimensional space. The particles explore the space in an attempt to find minima or maxima to the optimization problem. The motion of the particles is linked, and the overall behavior of the particle swarm is controlled by several parameters. PSO has been proposed as a control strategy for physical swarms of robots that are localizing a source; the robots are analogous to the virtual particles. However, previous attempts to achieve this have shown that there are inherent problems. This paper addresses these problems by introducing a modified version of PSO, as well as introducing new guidelines for parameter selection. The proposed algorithm links the parameters to the velocity and acceleration of each robot, and demonstrates obstacle avoidance. Simulation results from both MATLAB and Gazebo show close agreement and demonstrate that the proposed algorithm is capable of effective control of a robotic swarm and obstacle avoidance.