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Advances in Design and Integration of Wearable Sensors for Ergonomics

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 75039

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Special Issue Editors

Prof. Dr. Nicola Francesco Lopomo

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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

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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

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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

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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,

We all know how ergonomics can contribute to maximize human wellbeing and the overall efficiency of a working system by integrating different approaches to fully understand 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 situation but - thanks to proactive methods – it is able to provide instruments to design, virtually assess, and identify optimal solutions in advance. Furthermore, in the frame of the actual complex working activities, ergonomics can provide a global and multi-parametric perspective which surpasses the individually-applied standard approaches. Indeed, the first goal is to measure the ergonomics of man-machine-environment systems so to have information for driving developments.
Within this framework, the latest advances in wearable technologies are allowing to ecologically collect 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 face expression tracking device, smart sensors for temperature, hearth-rate, breathing, EEG and electrodermal activity and muscular activation analysis are offering a wide perspective for novel solutions. All these approaches are providing new opportunities to improve our actual knowledge of the individual wellbeing and the working context by integrating a plethora of valuable information, which can be analyzed also through novel techniques, including biomechanical modelling, 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 spanning topics across the design of novel sensors or wearable technologies and the development of any novel methodology aiming to integrate quantitative physiological and environmental information for those that are the main goals of the ergonomics.

Prof. Dr. Nicola Francesco Lopomo
Dr. Carlo Emilio 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 for ergonomics
Activity monitorning devices and systems
Novel methods and systems for integrated ergonomic assessment
Sensors for 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 environment
Ubiquitous input devices
Wearable fashion

Published Papers (12 papers)

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Research

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14 pages, 13886 KiB

Open AccessArticle

Development of an Integrated Virtual Reality System with Wearable Sensors for Ergonomic Evaluation of Human–Robot Cooperative Workplaces

by Teodorico Caporaso, Stanislao Grazioso and Giuseppe Di Gironimo

Sensors 2022, 22(6), 2413; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062413 - 21 Mar 2022

Cited by 13 | Viewed by 3395

Abstract

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. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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12 pages, 2457 KiB

Open AccessArticle

Evaluation of a New Simplified Inertial Sensor Method against Electrogoniometer for Measuring Wrist Motion in Occupational Studies

by Karnica Manivasagam and Liyun Yang

Sensors 2022, 22(4), 1690; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041690 - 21 Feb 2022

Cited by 11 | Viewed by 2620

Abstract

Wrist velocity is an important risk factor for work-related musculoskeletal disorders in the elbow/hand, which is also difficult to assess by observation or self-reports. This study aimed to evaluate a new convenient and low-cost inertial measurement unit (IMU)-based method using gyroscope signals against an electrogoniometer for measuring wrist flexion velocity. Twelve participants performed standard wrist movements and simulated work tasks while equipped with both systems. Two computational algorithms for the IMU-based system, i.e., IMU_norm and IMU_flex, were used. For wrist flexion/extension, the mean absolute errors (MAEs) of median wrist flexion velocity compared to the goniometer were <10.1°/s for IMU_norm and <4.1°/s for IMU_flex. During wrist deviation and pronation/supination, all methods showed errors, where the IMU_norm method had the largest overestimations. For simulated work tasks, the IMU_flex method had small bias and better accuracy than the IMU_norm method compared to the goniometer, with the MAEs of median wrist flexion velocity <5.8°/s. The results suggest that the IMU-based method can be considered as a convenient method to assess wrist motion for occupational studies or ergonomic evaluations for the design of workstations and tools by both researchers and practitioners, and the IMU_flex method is preferred. Future studies need to examine algorithms to further improve the accuracy of the IMU-based method in tasks of larger variations, as well as easy calibration procedures. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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21 pages, 3968 KiB

Open AccessArticle

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

by Luca Ascari, Anna Marchenkova, Andrea Bellotti, Stefano Lai, Lucia Moro, Konstantin Koshmak, Alice Mantoan, Michele Barsotti, Raffaello Brondi, Giovanni Avveduto, Davide Sechi, Alberto Compagno, Pietro Avanzini, Jonas Ambeck-Madsen and Giovanni Vecchiato

Sensors 2021, 21(24), 8167; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248167 - 07 Dec 2021

Cited by 4 | Viewed by 2539

Abstract

Nowadays, the growing interest in gathering physiological data and human behavior in everyday life scenarios is paralleled by an increase in wireless devices recording brain and body signals. However, the technical issues that characterize these solutions often limit the full brain-related assessments in real-life scenarios. Here we introduce the Biohub platform, a hardware/software (HW/SW) integrated wearable system for multistream synchronized acquisitions. This system consists of off-the-shelf hardware and state-of-art open-source software components, which are highly integrated into a high-tech low-cost solution, complete, yet easy to use outside conventional labs. It flexibly cooperates with several devices, regardless of the manufacturer, and overcomes the possibly limited resources of recording devices. The Biohub was validated through the characterization of the quality of (i) multistream synchronization, (ii) in-lab electroencephalographic (EEG) recordings compared with a medical-grade high-density device, and (iii) a Brain-Computer-Interface (BCI) in a real driving condition. Results show that this system can reliably acquire multiple data streams with high time accuracy and record standard quality EEG signals, becoming a valid device to be used for advanced ergonomics studies such as driving, telerehabilitation, and occupational safety. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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30 pages, 5138 KiB

Open AccessArticle

Forces: A Motion Capture-Based Ergonomic Method for the Today’s World

by Javier Marín and José J. Marín

Sensors 2021, 21(15), 5139; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155139 - 29 Jul 2021

Cited by 11 | Viewed by 4249

Abstract

Approximately three of every five workers are affected by musculoskeletal disorders, especially in production environments. In this regard, workstation ergonomic evaluations are especially beneficial for conducting preventive actions. Nevertheless, today’s context demonstrates that traditional ergonomic methods should lead to smart ergonomic methods. This document introduces the Forces ergonomic method, designed considering the possibilities of inertial motion capture technology and its applicability to evaluating actual workstations. This method calculates the joint risks for each posture and provides the total risk for the assessed workstation. In this calculation, Forces uses postural measurement and a kinetic estimation of all forces and torques that the joints support during movement. This paper details the method’s fundamentals to achieve structural validity, demonstrating that all parts that compose it are logical and well-founded. This method aims to aid prevention technicians in focusing on what matters: making decisions to improve workers’ health. Likewise, it aims to answer the current industry needs and reduce musculoskeletal disorders caused by repetitive tasks and lower the social, economic, and productivity losses that such disorders entail. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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16 pages, 1908 KiB

Open AccessArticle

A User Centered Methodology for the Design of Smart Apparel for Older Users

by Silvia Imbesi and Sofia Scataglini

Sensors 2021, 21(8), 2804; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082804 - 16 Apr 2021

Cited by 12 | Viewed by 3651

Abstract

Smart clothing plays a big role to foster innovation and to. boost health and well-being, improving the quality of the life of people, especially when addressed to niche users with particular needs related to their health. Designing smart apparel, in order to monitor physical and physiological functions in older users, is a crucial asset that user centered design is exploring, balancing needs expressed by the users with technological requirements related to the design process. In this paper, the authors describe a user centered methodology for the design of smart garments based on the evaluation of users’ acceptance of smart clothing. This comparison method can be considered as similar to a simplified version of the quality function deployment tool, and is used to evaluate the general response of each garment typology to different categories of requirements, determining the propensity of the older user to the utilization of the developed product. The suggested methodology aims at introducing in the design process a tool to evaluate and compare developed solutions, reducing complexity in design processes by providing a tool for the comparison of significant solutions, correlating quantitative and qualitative factors. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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16 pages, 2260 KiB

Open AccessArticle

by Leandro Donisi, Giuseppe Cesarelli, Armando Coccia, Monica Panigazzi, Edda Maria Capodaglio and Giovanni D’Addio

Sensors 2021, 21(8), 2593; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082593 - 07 Apr 2021

Cited by 34 | Viewed by 3895

Abstract

Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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17 pages, 6274 KiB

Open AccessArticle

A Modular Design for Distributed Measurement of Human–Robot Interaction Forces in Wearable Devices

by Keya Ghonasgi, Saad N. Yousaf, Paria Esmatloo and Ashish D. Deshpande

Sensors 2021, 21(4), 1445; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041445 - 19 Feb 2021

Cited by 12 | Viewed by 3237

Abstract

Measurement of interaction forces distributed across the attachment interface in wearable devices is critical for understanding ergonomic physical human–robot interaction (pHRI). The main challenges in sensorization of pHRI interfaces are (i) capturing the fine nature of force transmission from compliant human tissue onto rigid surfaces in the wearable device and (ii) utilizing a low-cost and easily implementable design that can be adapted for a variety of human interfaces. This paper addresses both challenges and presents a modular sensing panel that uses force-sensing resistors (FSRs) combined with robust electrical and mechanical integration principles that result in a reliable solution for distributed load measurement. The design is demonstrated through an upper-arm cuff, which uses 24 sensing panels, in conjunction with the Harmony exoskeleton. Validation of the design with controlled loading of the sensorized cuff proves the viability of FSRs in an interface sensing solution. Preliminary experiments with a human subject highlight the value of distributed interface force measurement in recognizing the factors that influence ergonomic pHRI and elucidating their effects. The modular design and low cost of the sensing panel lend themselves to extension of this approach for studying ergonomics in a variety of wearable applications with the goal of achieving safe, comfortable, and effective human–robot interaction. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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25 pages, 6532 KiB

Open AccessArticle

A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling

by Emily S. Matijevich, Peter Volgyesi and Karl E. Zelik

Sensors 2021, 21(2), 340; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020340 - 06 Jan 2021

Cited by 23 | Viewed by 5974

Abstract

(1) Background: Low back disorders are a leading cause of missed work and physical disability in manual material handling due to repetitive lumbar loading and overexertion. Ergonomic assessments are often performed to understand and mitigate the risk of musculoskeletal overexertion injuries. Wearable sensor solutions for monitoring low back loading have the potential to improve the quality, quantity, and efficiency of ergonomic assessments and to expand opportunities for the personalized, continuous monitoring of overexertion injury risk. However, existing wearable solutions using a single inertial measurement unit (IMU) are limited in how accurately they can estimate back loading when objects of varying mass are handled, and alternative solutions in the scientific literature require so many distributed sensors that they are impractical for widespread workplace implementation. We therefore explored new ways to accurately monitor low back loading using a small number of wearable sensors. (2) Methods: We synchronously collected data from laboratory instrumentation and wearable sensors to analyze 10 individuals each performing about 400 different material handling tasks. We explored dozens of candidate solutions that used IMUs on various body locations and/or pressure insoles. (3) Results: We found that the two key sensors for accurately monitoring low back loading are a trunk IMU and pressure insoles. Using signals from these two sensors together with a Gradient Boosted Decision Tree algorithm has the potential to provide a practical (relatively few sensors), accurate (up to r² = 0.89), and automated way (using wearables) to monitor time series lumbar moments across a broad range of material handling tasks. The trunk IMU could be replaced by thigh IMUs, or a pelvis IMU, without sacrificing much accuracy, but there was no practical substitute for the pressure insoles. The key to realizing accurate lumbar load estimates with this approach in the real world will be optimizing force estimates from pressure insoles. (4) Conclusions: Here, we present a promising wearable solution for the practical, automated, and accurate monitoring of low back loading during manual material handling. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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18 pages, 2106 KiB

Open AccessArticle

An Online Method to Detect and Locate an External Load on the Human Body with Applications in Ergonomics Assessment

by Marta Lorenzini, Wansoo Kim, Elena De Momi and Arash Ajoudani

Sensors 2020, 20(16), 4471; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164471 - 10 Aug 2020

Cited by 5 | Viewed by 4023

Abstract

In this work, we propose an online method to detect and approximately locate an external load induced on the body of a person interacting with the environment. The method is based on a torque equilibrium condition on the human sagittal plane, which takes into account a reduced-complexity model of the whole-body centre of pressure (CoP) along with the measured one, and the vertical component of the ground reaction forces (vGRFs). The latter is combined with a statistical analysis approach to improve the localisation accuracy, (which is subject to uncertainties) to the extent of the industrial applications we target. The proposed technique eliminates the assumption of known contact position of an external load on the human limbs, allowing a more flexible online body-state tracking. The accuracy of the proposed method is first evaluated via a simulation study in which various contact points on different body postures are considered. Next, experiments on human subjects with three different contact locations applied to the human body are presented, revealing the validity of the proposed methodology. Lastly, its benefit in the estimation of human dynamic states is demonstrated. These results add another layer to the online human ergonomics assessment framework developed in our laboratory, extending it to more realistic and varying interaction conditions. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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Review

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49 pages, 1374 KiB

Open AccessReview

The Cognitive-Emotional Design and Study of Architectural Space: A Scoping Review of Neuroarchitecture and Its Precursor Approaches

by Juan Luis Higuera-Trujillo, Carmen Llinares and Eduardo Macagno

Sensors 2021, 21(6), 2193; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062193 - 21 Mar 2021

Cited by 55 | Viewed by 25050

Abstract

Humans respond cognitively and emotionally to the built environment. The modern possibility of recording the neural activity of subjects during exposure to environmental situations, using neuroscientific techniques and virtual reality, provides a promising framework for future design and studies of the built environment. The discipline derived is termed “neuroarchitecture”. Given neuroarchitecture’s transdisciplinary nature, it progresses needs to be reviewed in a contextualised way, together with its precursor approaches. The present article presents a scoping review, which maps out the broad areas on which the new discipline is based. The limitations, controversies, benefits, impact on the professional sectors involved, and potential of neuroarchitecture and its precursors’ approaches are critically addressed. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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23 pages, 790 KiB

Open AccessReview

Towards a Functional Performance Validation Standard for Industrial Low-Back Exoskeletons: State of the Art Review

by Mattia Pesenti, Alberto Antonietti, Marta Gandolla and Alessandra Pedrocchi

Sensors 2021, 21(3), 808; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030808 - 26 Jan 2021

Cited by 39 | Viewed by 4156

Abstract

While the research interest for exoskeletons has been rising in the last decades, missing standards for their rigorous evaluation are potentially limiting their adoption in the industrial field. In this context, exoskeletons for worker support have the aim to reduce the physical effort required by humans, with dramatic social and economic impact. Indeed, exoskeletons can reduce the occurrence and the entity of work-related musculoskeletal disorders that often cause absence from work, resulting in an eventual productivity loss. This very urgent and multifaceted issue is starting to be acknowledged by researchers. This article provides a systematic review of the state of the art for functional performance evaluation of low-back exoskeletons for industrial workers. We report the state-of-the-art evaluation criteria and metrics used for such a purpose, highlighting the lack of a standard for this practice. Very few studies carried out a rigorous evaluation of the assistance provided by the device. To address also this topic, the article ends with a proposed framework for the functional validation of low-back exoskeletons for the industry, with the aim to pave the way for the definition of rigorous industrial standards. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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24 pages, 1263 KiB

Open AccessEditor’s ChoiceReview

Wearable Devices for Ergonomics: A Systematic Literature Review

by Elena Stefana, Filippo Marciano, Diana Rossi, Paola Cocca and Giuseppe Tomasoni

Sensors 2021, 21(3), 777; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030777 - 24 Jan 2021

Cited by 68 | Viewed by 9405

Abstract

Wearable devices are pervasive solutions for increasing work efficiency, improving workers’ well-being, and creating interactions between users and the environment anytime and anywhere. Although several studies on their use in various fields have been performed, there are no systematic reviews on their utilisation in ergonomics. Therefore, we conducted a systematic review to identify wearable devices proposed in the scientific literature for ergonomic purposes and analyse how they can support the improvement of ergonomic conditions. Twenty-eight papers were retrieved and analysed thanks to eleven comparison dimensions related to ergonomic factors, purposes, and criteria, populations, application and validation. The majority of the available devices are sensor systems composed of different types and numbers of sensors located in diverse body parts. These solutions also represent the technology most frequently employed for monitoring and reducing the risk of awkward postures. In addition, smartwatches, body-mounted smartphones, insole pressure systems, and vibrotactile feedback interfaces have been developed for evaluating and/or controlling physical loads or postures. The main results and the defined framework of analysis provide an overview of the state of the art of smart wearables in ergonomics, support the selection of the most suitable ones in industrial and non-industrial settings, and suggest future research directions. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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