The large-scale adoption of occupational exoskeletons (OEs) will only happen if clear evidence of effectiveness of the devices is available. Performing product-specific field validation studies would allow the stakeholders and decision-makers (e.g., employers, ergonomists, health, and safety departments) to assess OEs’ effectiveness in their specific work contexts and with experienced workers, who could further provide useful insights on practical issues related to exoskeleton daily use. This paper reviews present-day scientific methods for assessing the effectiveness of OEs in laboratory and field studies, and presents the vision of the authors on a roadmap that could lead to large-scale adoption of this technology. The analysis of the state-of-the-art shows methodological differences between laboratory and field studies. While the former are more extensively reported in scientific papers, they exhibit limited generalizability of the findings to real-world scenarios. On the contrary, field studies are limited in sample sizes and frequently focused only on subjective metrics. We propose a roadmap to promote large-scale knowledge-based adoption of OEs. It details that the analysis of the costs and benefits of this technology should be communicated to all stakeholders to facilitate informed decision making, so that each stakeholder can develop their specific role regarding this innovation. Large-scale field studies can help identify and monitor the possible side-effects related to exoskeleton use in real work situations, as well as provide a comprehensive scientific knowledge base to support the revision of ergonomics risk-assessment methods, safety standards and regulations, and the definition of guidelines and practices for the selection and use of OEs.

This paper introduces the design and development of a novel pressure-sensitive foot insole for real-time monitoring of plantar pressure distribution during walking. The device consists of a flexible insole with 64 pressure-sensitive elements and an integrated electronic board for high-frequency data acquisition, pre-filtering, and wireless transmission to a remote data computing/storing unit. The pressure-sensitive technology is based on an optoelectronic technology developed at Scuola Superiore Sant’Anna. The insole is a low-cost and low-power battery-powered device. The design and development of the device is presented along with its experimental characterization and validation with healthy subjects performing a task of walking at different speeds, and benchmarked against an instrumented force platform.

To assist people with disabilities, exoskeletons must be provided with human-robot interfaces and smart algorithms capable to identify the user’s movement intentions. Surface electromyographic (sEMG) signals could be suitable for this purpose, but their applicability in shared control schemes for real-time operation of assistive devices in daily-life activities is limited due to high inter-subject variability, which requires custom calibrations and training. Here, we developed a machine-learning-based algorithm for detecting the user’s motion intention based on electromyographic signals, and discussed its applicability for controlling an upper-limb exoskeleton for people with severe arm disabilities. Ten healthy participants, sitting in front of a screen while wearing the exoskeleton, were asked to perform several reaching movements toward three LEDs, presented in a random order. EMG signals from seven upper-limb muscles were recorded. Data were analyzed offline and used to develop an algorithm that identifies the onset of the movement across two different events: moving from a resting position toward the LED (Go-forward), and going back to resting position (Go-backward). A set of subject-independent time-domain EMG features was selected according to information theory and their probability distributions corresponding to rest and movement phases were modeled by means of a two-component Gaussian Mixture Model (GMM). The detection of movement onset by two types of detectors was tested: the first type based on features extracted from single muscles, whereas the second from multiple muscles. Their performances in terms of sensitivity, specificity and latency were assessed for the two events with a leave one-subject out test method. The onset of movement was detected with a maximum sensitivity of 89.3% for Go-forward and 60.9% for Go-backward events. Best performances in terms of specificity were 96.2 and 94.3% respectively. For both events the algorithm was able to detect the onset before the actual movement, while computational load was compatible with real-time applications. The detection performances and the low computational load make the proposed algorithm promising for the control of upper-limb exoskeletons in real-time applications. Fast initial calibration makes it also suitable for helping people with severe arm disabilities in performing assisted functional tasks.

This paper presents the H-PULSE, a novel semi-passive upper-limb exoskeleton for worker assistance, with motorized tuning of the assistive level. The H-PULSE presents novel design features compared to other passive industrial exoskeletons for the upper limbs, namely joint angle sensors for measuring shoulder flexion/extension and a novel active mechanism for regulating the assistance level. These features could enhance the effectiveness of the system. Along with the presentation of the exoskeleton design, this paper reports on the system experimental evaluation with human subjects. The H-PULSE was assessed in prolonged static overhead tasks under different conditions of assistive support. The set of metrics to evaluate the effects of the device included shoulder muscular activity, heart rate, and subjective user feedback. Results show that the exoskeleton can reduce the users’ muscular activity and the heart rate. Subjective questionnaires allowed the assessment of perceived exoskeleton effectiveness. In this study, the H-PULSE exoskeleton was found to be potentially effective in reducing the muscular strain while reducing the global fatigue level during prolonged continuous overhead activities.

Wearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients’ engagement. Nevertheless, it is not yet clear what the most commonly used sensor configurations are, and it is also not clear which biofeedback components are used for which pathology. To explore these aspects and estimate the effectiveness of wearable device biofeedback rehabilitation on balance and gait, we conducted a systematic review by electronic search on MEDLINE, PubMed, Web of Science, PEDro, and the Cochrane CENTRAL from inception to January 2020. Nineteen randomized controlled trials were included (Parkinson’s n = 6; stroke n = 13; mild cognitive impairment n = 1). Wearable devices mostly provided real-time biofeedback during exercise, using biomechanical sensors and a positive reinforcement feedback strategy through auditory or visual modes. Some notable points that could be improved were identified in the included studies; these were helpful in providing practical design rules to maximize the prospective of wearable device biofeedback rehabilitation. Due to the current quality of the literature, it was not possible to achieve firm conclusions about the effectiveness of wearable device biofeedback rehabilitation. However, wearable device biofeedback rehabilitation seems to provide positive effects on dynamic balance and gait for PwND, but higher-quality RCTs with larger sample sizes are needed for stronger conclusions.