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Advances in Design and Integration of Wearable Sensors for Ergonomics...
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Advances in Design and Integration of Wearable Sensors for Ergonomics 2022-2023

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 4782

Special Issue Editors

Prof. Dr. Nicola Francesco Lopomo

E-Mail Website
Guest Editor
Department of Design, Politecnico di Milano Milano, 20158 Milan, Italy
Interests: bioengineering; biosensors; wearables; rehabilitation; ergonomics; technologies for health; biomechanics; clinical biomechanics; computer-aided surgery
Special Issues, Collections and Topics in MDPI journals
Dr. Carlo Emilio Standoli

E-Mail Website
Guest Editor
Dipartimento di Design, TEDH – Technology and Design for Healthcare, Politecnico di Milano, Milano, Italy
Interests: industrial design; human-product interaction; health design thinking; human centered design; ergonomics; technologies for health; sensors; digital human modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Perego

E-Mail Website
Guest Editor
Dipartimento di Design, Politecnico di Milano, Via Candiani 72, 20158 Milano, Italy
Interests: wearables; human–computer interaction; UX design rehabilitation; tele-medicine; mobile health
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giuseppe Andreoni

E-Mail Website
Guest Editor
Technology and Design for Healthcare Laboratory—Politecnico di Milano, Dip. di Design via Durando 38/A, 20158 Milano, Italy
Interests: wearable sensors; ergonomics; design for health; user-centered design; technologies for health; bioengineering; rehabilitation; assistive technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ergonomics can maximize human wellbeing and the overall efficiency of a working system by integrating different approaches to fully elucidate the interactions among humans and all the elements that make up the system itself. Indeed, ergonomics can intervene not only ex post to correct an existing error but—thanks to proactive methods—can also provide possible instrument designs and virtually assess and identify optimal solutions in advance. Furthermore, in the context of actual complex working activities, ergonomics can provide a global and multi-parametric perspective that surpasses individually applied standard approaches. Indeed, the first goal of this field is to measure the ergonomics of man–machine–environment systems to gather information to drive developments.

Within this framework, the latest advances in wearable technologies have allowed for an ecological collection of a wide variety of relevant physiological and environmental parameters.  Information can be acquired via a pervasive ecosystem consisting of both consumer-oriented wearable devices or smartphones and novel technologies and methodologies, ad hoc developed by scientific research. Without any loss of generality, the availability of wearable motion trackers, inertial measurement units, pressure sensors, eye and facial expression tracking devices, smart sensors for temperature, hearth rate, breathing, EEG and electrodermal activity and muscular activation analysis offers wide possibilities for novel solutions. These approaches provide new opportunities to improve our actual knowledge of individual wellbeing and the working context by integrating a plethora of valuable data, which can be analyzed through novel techniques, including biomechanical modeling, machine learning and data mining.

This Special Issue, “Advances in the Design and Integration of Wearable Sensors for Ergonomics”, aims to highlight several of the latest developments in this specific field. Both research papers and review articles will be considered. We welcome submissions relating to the design of novel sensors or wearable technologies and the development of any novel methodology aiming to integrate quantitative physiological and environmental information, as these are the main goals of ergonomics.

Prof. Dr. Nicola Lopomo
Dr. Carlo Standoli
Dr. Paolo Perego
Prof. Dr. Giuseppe Andreoni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wearable devices and systems
  • wearable ergonomic devices
  • activity-monitoring devices and systems
  • novel methods and systems for integrated ergonomic assessment
  • sensors for assessing wellbeing
  • novel design approaches for ergonomic assessment
  • innovative systems and methods for risk assessment
  • machine learning and deep learning for wearable data analysis
  • experiment design
  • autonomous activity recognition
  • monitoring human–environment interaction
  • integrated monitoring systems (human–activity–environment)
  • usability of wearable systems
  • mhealth and/or ehealth solutions for ergonomics
  • pervasive technologies
  • smart glasses, wearable imaging, projection devices
  • virtual reality and/or augmented reality and/or mixed reality
  • self-tracking
  • ergonomics knowledge representation and reasoning
  • health data acquisition, analysis and mining
  • validity, reliability, usability and effectiveness of self-tracking devices
  • social and psychological investigation into self-tracking devices
  • health monitoring in working environments
  • smart coaching devices and systems for working environments
  • ubiquitous input devices
  • wearable fashion

Published Papers (3 papers)

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Research

22 pages, 10163 KiB  
Article
Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network
by Riccardo Bezzini, Luca Crosato, Massimo Teppati Losè, Carlo Alberto Avizzano, Massimo Bergamasco and Alessandro Filippeschi
Sensors 2023, 23(13), 5885; https://doi.org/10.3390/s23135885 - 25 Jun 2023
Cited by 2 | Viewed by 1069
Abstract
Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis [...] Read more.
Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches. Full article
(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics 2022-2023)
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16 pages, 3714 KiB  
Article
Evaluation of In-Cloth versus On-Skin Sensors for Measuring Trunk and Upper Arm Postures and Movements
by Damien Hoareau, Xuelong Fan, Farhad Abtahi and Liyun Yang
Sensors 2023, 23(8), 3969; https://doi.org/10.3390/s23083969 - 13 Apr 2023
Cited by 3 | Viewed by 1479
Abstract
Smart workwear systems with embedded inertial measurement unit sensors are developed for convenient ergonomic risk assessment of occupational activities. However, its measurement accuracy can be affected by potential cloth artifacts, which have not been previously assessed. Therefore, it is crucial to evaluate the [...] Read more.
Smart workwear systems with embedded inertial measurement unit sensors are developed for convenient ergonomic risk assessment of occupational activities. However, its measurement accuracy can be affected by potential cloth artifacts, which have not been previously assessed. Therefore, it is crucial to evaluate the accuracy of sensors placed in the workwear systems for research and practice purposes. This study aimed to compare in-cloth and on-skin sensors for assessing upper arms and trunk postures and movements, with the on-skin sensors as the reference. Five simulated work tasks were performed by twelve subjects (seven women and five men). Results showed that the mean (±SD) absolute cloth–skin sensor differences of the median dominant arm elevation angle ranged between 1.2° (±1.4) and 4.1° (±3.5). For the median trunk flexion angle, the mean absolute cloth–skin sensor differences ranged between 2.7° (±1.7) and 3.7° (±3.9). Larger errors were observed for the 90th and 95th percentiles of inclination angles and inclination velocities. The performance depended on the tasks and was affected by individual factors, such as the fit of the clothes. Potential error compensation algorithms need to be investigated in future work. In conclusion, in-cloth sensors showed acceptable accuracy for measuring upper arm and trunk postures and movements on a group level. Considering the balance of accuracy, comfort, and usability, such a system can potentially be a practical tool for ergonomic assessment for researchers and practitioners. Full article
(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics 2022-2023)
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23 pages, 4531 KiB  
Article
Multisensory Cues for Gait Rehabilitation with Smart Glasses: Methodology, Design, and Results of a Preliminary Pilot
by Silvia Imbesi and Mattia Corzani
Sensors 2023, 23(2), 874; https://doi.org/10.3390/s23020874 - 12 Jan 2023
Cited by 2 | Viewed by 1564
Abstract
Recent advances in mobile technology have shown that augmented unisensory feedback can be leveraged to improve gait using wearable systems, but less is known about the possible benefits and usability of multisensory (i.e., multimodal) feedback. This paper introduces the preliminary results of an [...] Read more.
Recent advances in mobile technology have shown that augmented unisensory feedback can be leveraged to improve gait using wearable systems, but less is known about the possible benefits and usability of multisensory (i.e., multimodal) feedback. This paper introduces the preliminary results of an innovative research project aiming to develop an mHealth system including Android smart glasses, and providing multisensory cues for gait rehabilitation of people affected by Parkinson’s disease in and out of the medical context. In particular, the paper describes a preliminary pilot focusing on the design of visual, auditory, and haptic cues, and testing the design methodologies to be used in further developments of the project. Considered research questions were: Which kinds of images, sounds, and vibrations mostly influence gait speed, stride length, and cadence? Which are the ones stressing the user the least? Which ones induce the most immediate reaction? Thus, in this starting part of the research project, different typologies of sensory cues were designed, tested, and evaluated considering quantitative and qualitative parameters to properly answer the research questions. Full article
(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics 2022-2023)
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