The assessment of risks due to biomechanical overload in manual material handling is nowadays mainly based on observational methods in which an expert rater visually inspects videos of the working activity. Currently available sensing wearable technologies for motion and muscular activity capture enables to advance the risk assessment by providing reliable, repeatable, and objective measures. However, existing solutions do not address either a full body assessment or the inclusion of measures for the evaluation of the effort. This article proposes a novel system for the assessment of biomechanical overload, capable of covering all areas of ISO 11228, that uses a sensor network composed of inertial measurement units (IMU) and electromyography (EMG) sensors. The proposed method is capable of gathering and processing data from three IMU-based motion capture systems and two EMG capture devices. Data are processed to provide both segmentation of the activity and ergonomic risk score according to the methods reported in the ISO 11228 and the TR 12295. The system has been tested on a challenging outdoor scenario such as lift-on/lift-off of containers on a cargo ship. A comparison of the traditional evaluation method and the proposed one shows the consistency of the proposed system, its time effectiveness, and its potential for deeper analyses that include intra-subject and inter-subjects variability as well as a quantitative biomechanical analysis.
The improvements in efficiency of electronic components and miniaturization is quickly pushing wearable devices. Kinetic human energy harvesting is a way to power these components reducing the need of batteries replacement since walking or running is how humans already expend much of their daily energy. This work explores the case of kinetic energy from bending of a piezoelectric patch. For assessing the quality of the system, a testing setup has been designed and controlled by means of knee joint recordings obtained from a large motion dataset. The promising result of the chosen patch is an output power of 2.6μW associated to a run activity.
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