Overhead work is a frequent cause of shoulder work-related musculoskeletal disorders. Exoskeletons offering arm support have the potential to reduce shoulder strain, without requiring large scale reorganization of the workspace. Assessment of such systems however requires to take multiple factors into consideration. This paper presents a thorough in-lab assessment of PAEXO, a novel passive exoskeleton for arm support during overhead work. A list of evaluation criteria and associated performance metrics is proposed to cover both objective and subjective effects of the exoskeleton, on the user and on the task being performed. These metrics are measured during a lab study, where 12 participants perform an overhead pointing task with and without the exoskeleton, while their physical, physiological and psychological states are monitored. Results show that using PAEXO reduces shoulder physical strain as well as global physiological strain, without increasing low back strain nor degrading balance. These positive effects are achieved without degrading task performance. Importantly, participants' opinions of PAEXO are positive, in agreement with the objective measures. Thus, PAEXO seems a promising solution to help prevent shoulder injuries and diseases among overhead workers, without negatively impacting productivity.

The number one cause of disability in the world is low-back pain, with mechanical loading as one of the major risk factors. To reduce mechanical loading, exoskeletons have been introduced in the workplace. Substantial reductions in back muscle activity were found when using the exoskeleton during static bending and manual materials handling. However, most exoskeletons only have one joint at hip level, resulting in loss of range of motion and shifting of the exoskeleton relative to the body. To address these issues, a new exoskeleton design has been developed and tested. The present study investigated the effect of the SPEXOR passive exoskeleton on compression forces, moments, muscle activity and kinematics during static bending at six hand heights and during lifting of a box of 10 kg from around ankle height using three techniques: Free, Squat and Stoop. For static bending, the exoskeleton reduced the compression force by 13-21% depending on bending angle. Another effect of the exoskeleton was that participants substantially reduced lumbar flexion. While lifting, the exoskeleton reduced the peak compression force, on average, by 14%. Lifting technique did not modify the effect of the exoskeleton such that the reduction in compression force was similar. In conclusion, substantial reductions in compression forces were found as a result of the support generated by the exoskeleton and changes in behavior when wearing the exoskeleton. For static bending, lumbar flexion was reduced with the exoskeleton, indicating reduced passive tissue strain. In addition, the reduced peak compression force could reduce the risk of compression induced tissue failure during lifting.

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

This article presents a systematic review on wearable robotic devices that use human-in-the-loop optimization (HILO) strategies to improve human–robot interaction. A total of 46 HILO studies were identified and divided into upper and lower limb robotic devices. The main aspects from HILO were identified, reviewed, and classified in four areas: 1) human–machine systems; 2) optimization methods; 3) control strategies; and 4) experimental protocols. A variety of objective functions (physiological, biomechanical, and subjective), optimization strategies, and optimized control parameters configurations used in different control strategies are presented and analyzed. An overview of experimental protocols is provided, including metrics, tasks, and conditions tested. Moreover, the relevance given to training or adaptation periods was explored. We outline an HILO framework that includes current wearable robots, optimization strategies, objective functions, control strategies, and experimental protocols. We conclude by highlighting current research gaps and defining future directions to improve the development of advanced HILO strategies in upper and lower limb wearable robots.