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Dear Colleagues,

The use of wearable sensors in measuring force and motions of human structures can potentially bring benefits to healthcare, sport, and well-being. Examples of wearable sensors for biomechanical measurements include accelerometers, gyroscopes, magnetometers, ultrasound, and optical, nanomaterial-based, EMG, and force sensors.

This Special Issue focuses on applications of wearable sensors in the following three areas:

Rehabilitation and gerontology.
Sport performance and injury prevention.
Risk assessment at work.

Papers that look into the developments, uses, and/or outcome measurements of wearable sensors in the above three areas are welcomed. Original research and review papers in these areas are encouraged.

Dr. Winson Lee
Guest Editor

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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.

Related Special Issue

Published Papers (2 papers)

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Research

19 pages, 1995 KiB

Open AccessArticle

Biomechanical Effects of Using a Passive Exoskeleton for the Upper Limb in Industrial Manufacturing Activities: A Pilot Study

by Armando Coccia, Edda Maria Capodaglio, Federica Amitrano, Vittorio Gabba, Monica Panigazzi, Gaetano Pagano and Giovanni D’Addio

Sensors 2024, 24(5), 1445; https://doi.org/10.3390/s24051445 - 23 Feb 2024

Cited by 1 | Viewed by 901

Abstract

This study investigates the biomechanical impact of a passive Arm-Support Exoskeleton (ASE) on workers in wool textile processing. Eight workers, equipped with surface electrodes for electromyography (EMG) recording, performed three industrial tasks, with and without the exoskeleton. All tasks were performed in an upright stance involving repetitive upper limbs actions and overhead work, each presenting different physical demands in terms of cycle duration, load handling and percentage of cycle time with shoulder flexion over 80°. The use of ASE consistently lowered muscle activity in the anterior and medial deltoid compared to the free condition (reduction in signal Root Mean Square (RMS)

- 21.6 %

and

- 13.6 %

, respectively), while no difference was found for the Erector Spinae Longissimus (ESL) muscle. All workers reported complete satisfaction with the ASE effectiveness as rated on Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST), and 62% of the subjects rated the usability score as very high (>80 System Usability Scale (SUS)). The reduction in shoulder flexor muscle activity during the performance of industrial tasks is not correlated to the level of ergonomic risk involved. This preliminary study affirms the potential adoption of ASE as support for repetitive activities in wool textile processing, emphasizing its efficacy in reducing shoulder muscle activity. Positive worker acceptance and intention to use ASE supports its broader adoption as a preventive tool in the occupational sector. Full article

(This article belongs to the Special Issue Wearable Sensors for Biomechanics Applications—2nd Edition)

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17 pages, 4015 KiB

Open AccessArticle

Floating Epoch Length Improves the Accuracy of Accelerometry-Based Estimation of Coincident Oxygen Consumption

by Henri Vähä-Ypyä, Pauliina Husu, Tommi Vasankari and Harri Sievänen

Sensors 2024, 24(1), 76; https://doi.org/10.3390/s24010076 - 22 Dec 2023

Viewed by 563

Abstract

Estimation of oxygen consumption (VO₂) from accelerometer data is typically based on prediction equations developed in laboratory settings using steadily paced and controlled test activities. These equations may not capture the temporary changes in VO₂ occurring in sporadic real-life physical activity. In this study, we introduced a novel floating epoch for accelerometer data analysis and hypothesized that an adaptive epoch length provides a more consistent estimation of VO₂ in irregular activity conditions than a 6 s constant epoch. Two different activity tests were conducted: a progressive constant-speed test (CS) performed on a track and a 6 min back-and-forth walk test including accelerations and decelerations (AC/DC) performed as fast as possible. Twenty-nine adults performed the CS test, and sixty-one performed the AC/DC test. The data were collected using hip-worn accelerometers and a portable metabolic gas analyzer. General linear models were employed to create the prediction models for VO₂ that were cross-validated using both data sets and epoch types as training and validation sets. The prediction equations based on the CS test or AC/DC test and 6 s epoch had excellent performance (R² = 89%) for the CS test but poor performance for the AC/DC test (31%). Only the VO₂ prediction equation based on the AC/DC test and the floating epoch had good performance (78%) for both tests. The overall accuracy of VO₂ prediction is compromised with the constant length epoch, whereas the prediction model based on irregular acceleration data analyzed with a floating epoch provided consistent performance for both activities. Full article

(This article belongs to the Special Issue Wearable Sensors for Biomechanics Applications—2nd Edition)

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