Summary: Wearable devices are fast evolving to address mobility and autonomy needs of elderly people who would benefit from physical assistance. Recent developments in soft robotics provide important opportunities to develop soft exoskeletons (also called exosuits) to enable both physical assistance and improved usability and acceptance for users. The XoSoft EU project has developed a modular soft lower limb exoskeleton to assist people with low mobility impairments. In this paper, we present the design of a soft modular lower limb exoskeleton to improve person’s mobility, contributing to independence and enhancing quality of life. The novelty of this work is the integration of quasi-passive elements in a soft exoskeleton. The exoskeleton provides mechanical assistance for subjects with low mobility impairments reducing energy requirements between 10% and 20%. Investigation of different control strategies based on gait segmentation and actuation elements is presented. A first hip–knee unilateral prototype is described, developed, and its performance assessed on a post-stroke patient for straight walking. The study presents an analysis of the human–exoskeleton energy patterns by way of the task-based biological power generation. The resultant assistance, in terms of power, was 10.9% ± 2.2% for hip actuation and 9.3% ± 3.5% for knee actuation. The control strategy improved the gait and postural patterns by increasing joint angles and foot clearance at specific phases of the walking cycle.

Revealing human movement requires lightweight, flexible systems capable of detecting mechanical parameters (like strain and pressure) while being worn comfortably by the user, and not interfering with his/her activity. In this work we address such multifaceted challenge with the development of smart garments for lower limb motion detection, like a textile kneepad and anklet in which soft sensors and readout electronics are embedded for retrieving movement of the specific joint. Stretchable capacitive sensors with a three-electrode configuration are built combining conductive textiles and elastomeric layers, and distributed around knee and ankle. Results show an excellent behavior in the ~30% strain range, hence the correlation between sensors’ responses and the optically tracked Euler angles is allowed for basic lower limb movements. Bending during knee flexion/extension is detected, and it is discriminated from any external contact by implementing in real time a low computational algorithm. The smart anklet is designed to address joint motion detection in and off the sagittal plane. Ankle dorsi/plantar flexion, adduction/abduction, and rotation are retrieved. Both knee and ankle smart garments show a high accuracy in movement detection, with a RMSE less than 4° in the worst case.

Occupational back-support exoskeletons are becoming a more and more common solution to mitigate work-related lower-back pain associated with lifting activities. In addition to lifting, there are many other tasks performed by workers, such as carrying, pushing, and pulling, that might benefit from the use of an exoskeleton. In this work, the impact that carrying has on lower-back loading compared to lifting and the need to select different assistive strategies based on the performed task are presented. This latter need is studied by using a control strategy that commands for constant torques. The results of the experimental campaign conducted on 9 subjects suggest that such a control strategy is beneficial for the back muscles (up to 12% reduction in overall lumbar activity), but constrains the legs (around 10% reduction in hip and knee ranges of motion). Task recognition and the design of specific controllers can be exploited by active and, partially, passive exoskeletons to enhance their versatility, i.e., the ability to adapt to different requirements.

Studies on back-support exoskeletons focus mostly on the evaluation of single devices. Also, these studies suggest that passive exoskeletons might be preferred when performing static tasks, whereas active ones, could show their advantage in dynamic tasks. To verify this hypothesis and provide a comparison between different devices, in this study, we analyse the performance of the XoTrunk and the Laevo v2.56, an active back-support exoskeleton and a passive one, respectively. We analyse these devices highlighting their ability to reduce back muscle activation, perceived discomfort and usability. The study was carried out by simulating a dynamic and a static task. The results show that both exoskeletons reduce back-muscle activation levels, although the XoTrunk provides a reduction that was more than twice as large as the Laevo (e.g., 41% vs 16%, respectively). The results also show that, overall, in the dynamic condition, the XoTrunk has better scores than the Laevo. Conversely, in the static task, the participants in the study had a preference for the passive device, Laevo. The results presented in this work can help exoskeleton end-users to properly select the best device to fit their needs.

One of the critical aspects in the design of an assistive wearable robot is the energy efficiency of the actuation system, since it significantly affects the weight and consequently the comfort of the system. Several strategies have been used in previous research, mostly based on energy harvesting, compliant elements for mechanical energy accumulation (springs or elastic cords), ratchets, and clutches. However, the design of the optimal actuator arrangement is highly dependent on the task, which increases significantly the complexity of the design process. In this letter, we present an energy efficiency analysis and design optimization of an actuation system applied to a soft module lower limb exoskeleton. Instead of performing a comparison between predefined mechanism arrangements, we solve a full optimization problem that includes not only the mechanism parameters, but also the mechanism architecture itself. The optimization is performed for a walking task using gait data from a stroke subject, and the result is a set of actuator arrangements with optimal parameters for the analyzed task and selected user. The optimized mechanism is able to reduce the energy requirements by 20-65%, depending on the joint. The proposed mechanism is currently under development within the XoSoft EU project, a modular soft lower-limb exoskeleton to assist people with mobility impairments.

Assistive strategies for occupational back-support exoskeletons have focused, mostly, on lifting tasks. However, in occupational scenarios, it is important to account not only for lifting but also for other activities. This can be done exploiting human activity recognition algorithms that can identify which task the user is performing and trigger the appropriate assistive strategy. We refer to this ability as exoskeleton versatility. To evaluate versatility, we propose to focus both on the ability of the device to reduce muscle activation (efficacy) and on its interaction with the user (dynamic fit). To this end, we performed an experimental study involving $ 10 $ healthy subjects replicating the working activities of a manufacturing plant. To compare versatile and non-versatile exoskeletons, our device, XoTrunk, was controlled with two different strategies. Correspondingly, we collected muscle activity, kinematic variables and users’ subjective feedbacks. Also, we evaluated the task recognition performance of the device. The results show that XoTrunk is capable of reducing muscle activation by up to $ 40\% $ in lifting and $ 30\% $ in carrying. However, the non-versatile control strategy hindered the users’ natural gait (e.g., $ -24\% $ reduction of hip flexion), which could potentially lower the exoskeleton acceptance. Detecting carrying activities and adapting the control strategy, resulted in a more natural gait (e.g., $ +9\% $ increase of hip flexion). The classifier analyzed in this work, showed promising performance (online accuracy > 91%). Finally, we conducted 9 hours of field testing, involving four users. Initial subjective feedbacks on the exoskeleton versatility, are presented at the end of this work.