Moved by the necessity, also related to the ongoing COVID-19 pandemic, of the design of innovative solutions in the context of digital health, and digital medicine, Wireless Body Area Networks (WBANs) are more and more emerging as a central system for the implementation of solutions for well-being and healthcare. In fact, by elaborating the data collected by a WBAN, advanced classification models can accurately extract health-related parameters, thus allowing, as examples, the implementations of applications for fitness tracking, monitoring of vital signs, diagnosis, and analysis of the evolution of diseases, and, in general, monitoring of human activities and behaviours. Unfortunately, commercially available WBANs present some technological and economic drawbacks from the point of view, respectively, of data fusion and labelling, and cost of the adopted devices. To overcome existing issues, in this paper, we present the architecture of a low-cost WBAN, which is built upon accessible off-the-shelf wearable devices and an Android application. Then, we report its technical evaluation concerning resource consumption. Finally, we demonstrate its versatility and accuracy in both medical and well-being application scenarios.

In medicine and sport science, postural evaluation is an essential part of gait and posture correction. There are various instruments for quantifying the postural system’s efficiency and determining postural stability which are considered state-of-the-art. However, such systems present many limitations related to accessibility, economic cost, size, intrusiveness, usability, and time-consuming set-up. To mitigate these limitations, this project aims to verify how wearable devices can be assembled and employed to provide feedback to human subjects for gait and posture improvement, which could be applied for sports performance or motor impairment rehabilitation (from neurodegenerative diseases, aging, or injuries). The project is divided into three parts: the first part provides experimental protocols for studying action anticipation and related processes involved in controlling posture and gait based on state-of-the-art instrumentation. The second part provides a biofeedback strategy for these measures concerning the design of a low-cost wearable system. Finally, the third provides algorithmic processing of the biofeedback to customize the feedback based on performance conditions, including individual variability. Here, we provide a detailed experimental design that distinguishes significant postural indicators through a conjunct architecture that integrates state-of-the-art postural and gait control instrumentation and a data collection and analysis framework based on low-cost devices and freely accessible machine learning techniques. Preliminary results on 12 subjects showed that the proposed methodology accurately recognized the phases of the defined motor tasks (i.e., rotate, in position, APAs, drop, and recover) with overall F1-scores of 89.6% and 92.4%, respectively, concerning subject-independent and subject-dependent testing setups.

Work-related musculoskeletal disorders (WRMSDs) are still today one of the primary health risks for workers worldwide. Adopting exoskeletons is emerging as a preventive measure in alleviating the musculoskeletal system’s workload. This work investigates the effect of a semi-active exoskeleton, a new class of wearable devices, in a simulated industrial environment. The study found a consistent decrease in the average activity of the Anterior Deltoid, Upper Trapezius, and Rectus Abdominis in four different material handling and lifting tasks when the exoskeleton was used. Using the exoskeleton to move a weight between two shelves reduced the activity of 10 measured muscles belonging to the arm and trunk, with a notable 42.6% reduction for the Anterior Deltoid and 46.4% for the Upper Trapezius. The study also showed reduced peak force and slower lifting motion, potentially decreasing the likelihood of WRMSDs. Lastly, using the exoskeleton did not require additional cognitive exertion from the subjects, who also consistently reported a 44.9% reduction in their perceived Effort and a 34.0% reduction in Physical Demand. Despite some negative feedback regarding limited mobility, the study demonstrated the potential of semi-active exoskeletons, achieving good results in tasks and with weights where passive upper-limb exoskeletons are not typically used.