This work proposes a novel virtual reality system which makes use of wearable sensors for testing and validation of cooperative workplaces from the ergonomic point of view. The main objective is to show, in real time, the ergonomic evaluation based on a muscular activity analysis within the immersive virtual environment. The system comprises the following key elements: a robotic simulator for modeling the robot and the working environment; virtual reality devices for human immersion and interaction within the simulated environment; five surface electromyographic sensors; and one uniaxial accelerometer for measuring the human ergonomic status. The methodology comprises the following steps: firstly, the virtual environment is constructed with an associated immersive tutorial for the worker; secondly, an ergonomic toolbox is developed for muscular analysis. This analysis involves multiple ergonomic outputs: root mean square for each muscle, a global electromyographic score, and a synthetic index. They are all visualized in the immersive environment during the execution of the task. To test this methodology, experimental trials are conducted on a real use case in a human–robot cooperative workplace typical of the automotive industry. The results showed that the methodology can effectively be applied in the analysis of human–robot interaction, to endow the workers with self–awareness with respect to their physical conditions.

In this paper, we present a biomechanical analysis of the upper body, which includes upper-limb, neck and trunk, during the execution of overhead industrial tasks. The analysis is based on multiple performance metrics obtained from a biomechanical analysis of the worker during the execution of a specific task, i.e. an overhead drilling task, performed at different working heights. The analysis enables a full description of human movement and internal load state during the execution of the task, thought the evaluation of joint angles, joint torques and muscle activations. A digital human model is used to simulate and replicate the worker’s task in a virtual environment. The experiments were conduced in laboratory setting, where four subjects, with different anthropometric characteristics, have performed 48 drilling tasks in two different working heights defined as low configuration and middle configuration. The results of analysis have impact on providing the best configuration of the worker within the industrial workplace and/or providing guidelines for developing assistance devices which can reduce the physical overloading acting on the worker’s body.

This paper presents IART, a novel inertial wearable system for automatic detection of infringements and analysis of sports performance in race walking. IART algorithms are developed from raw inertial measurements collected by a single sensor located at the bottom of the vertebral column (L5–S1). Two novel parameters are developed to estimate infringements: loss of ground contact time and loss of ground contact step classification; three classic parameters are indeed used to estimate performance: step length ratio, step cadence, and smoothness. From these parameters, five biomechanical indices customized for elite athletes are derived. The experimental protocol consists of four repetitions of a straight path of 300 m on a long-paved road, performed by nine elite athletes. Over a total of 1620 steps (54 sequences of 30 steps each), the average accuracy of correct detection of loss of ground contact events is equal to 99%, whereas the correct classification of the infringement is equal to 87% for each step sequence, with a 92% of acceptable classifications. A great emphasis is dedicated on the user-centered development of IART: an intuitive radar chart representation is indeed developed to provide practical usability and interpretation of IART indices from the athletes, coaches, and referees perspectives. The results of IART, in terms of accuracy of its indices and usability from end-users, are encouraging for its usage as tool to support athletes and coaches in training and referees in real competitions.

Introduction and Objectives: Fabrication processes for spinal orthoses require accurate three-dimensional (3D) models of the patients' trunk. Current methods for 3D reconstruction used in this field mainly include laser or structured light scanning; these methods are time expensive and invasive, especially for patients with partial disabilities. Therefore, a theoretically instant system for data acquisition of anatomical structure is highly desirable. The objective of this work is to show the feasibility of using digital photogrammetry for human body digitization to generate accurate 3D models of the patients' trunk for spinal orthoses fabrication. Materials and Methods: Multiple synchronized two-dimensional images of the human torso are captured from different points of view using a photogrammetric scanner. A 3D model is generated using the state-of-the-art algorithms for point cloud and surface reconstruction. The digitized model is then used as input for the standard computer-aided design (CAD)/computer-aided manufacturing (CAM) process of fabrication. R4D from Rodin4D is used as prosthetics and orthotics CAD software. A robotic cell constituted by a six-axis KUKA KR 30–3 is used for milling a polyurethane foam. Vacuum forming is then adopted to generate the orthosis. Two spinal orthoses are fabricated using this approach and a classical one; then, they are evaluated using quantitative and qualitative metrics. Results: The data acquisition using this approach lasts 50 milliseconds. The 3D reconstruction accuracy averages 0.21 ± 1.27 mm, which suits for the considered health care scenario. Results of the initial fitting of the orthoses fabricated with the presented method show better performances in terms of time (44%), product quality (35%), and patient experience (30%). Conclusions: Digital photogrammetry can be used to enhance the data acquisition and data processing of anatomical surfaces for the CAD/CAM process of spinal orthoses. The data acquisition time, almost instant, allows an easy compliance of many patients. The data processing allows generating accurate models of the patient's body. The overall process generates orthoses with a better quality with respect to those manufactured using conventional procedures.