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Advances and Application of Human Movement Sensors

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 29295

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Dr. Kevin Bell

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Guest Editor

Department of Orthopaedic Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
Interests: spine; low back pain; cervical spine; rehabilitation; remote monitoring; telemedicine; robotics

Special Issue Information

Dear Colleagues,

The need or desire to measure of human movement is relevant across a wide range of fields, from healthcare to movie animation. The most common approach to measuring human movement is optical motion tracking, which usually requires an array of cameras mounted rigidly within a controlled laboratory environment, which are utilized to capture the three-dimensional movement of markers or fixtures adhered to the body’s segments. Although this approach produces accurate results and is currently the gold standard in most fields, the artificial laboratory conditions can cause unknown experimental artifacts and biases.

Therefore, this Special Issue focuses on recent advancements in sensing technologies that have made it possible to accurately and reliably measure human movement outside of the laboratory setting and the resulting applications. I invite the community, across a broad range of fields and applications, to submit ground-breaking papers that will forge the future of human movement monitoring.

Dr. Kevin Bell
Guest Editor

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.

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Keywords

human movement
kinematics
wearable
implantable
ambulatory monitoring
remote monitoring
flexible sensors
mobile healthcare
telehealth

Published Papers (6 papers)

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Research

19 pages, 4299 KiB

Open AccessArticle

HARTH: A Human Activity Recognition Dataset for Machine Learning

by Aleksej Logacjov, Kerstin Bach, Atle Kongsvold, Hilde Bremseth Bårdstu and Paul Jarle Mork

Sensors 2021, 21(23), 7853; https://doi.org/10.3390/s21237853 - 25 Nov 2021

Cited by 39 | Viewed by 9129

Abstract

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa

= 0.96

). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of

0.81

(standard deviation:

\pm 0.18

), recall of

0.85 \pm 0.13

, and precision of

0.79 \pm 0.22

in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living. Full article

(This article belongs to the Special Issue Advances and Application of Human Movement Sensors)

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16 pages, 3509 KiB

Open AccessArticle

The WGD—A Dataset of Assembly Line Working Gestures for Ergonomic Analysis and Work-Related Injuries Prevention

by Christian Tamantini, Francesca Cordella, Clemente Lauretti and Loredana Zollo

Sensors 2021, 21(22), 7600; https://doi.org/10.3390/s21227600 - 16 Nov 2021

Cited by 13 | Viewed by 2630

Abstract

This paper wants to stress the importance of human movement monitoring to prevent musculoskeletal disorders by proposing the WGD—Working Gesture Dataset, a publicly available dataset of assembly line working gestures that aims to be used for worker’s kinematic analysis. It contains kinematic data acquired from healthy subjects performing assembly line working activities using an optoelectronic motion capture system. The acquired data were used to extract quantitative indicators to assess how the working tasks were performed and to detect useful information to estimate the exposure to the factors that may contribute to the onset of musculoskeletal disorders. The obtained results demonstrate that the proposed indicators can be exploited to early detect incorrect gestures and postures and, consequently to prevent work-related disorders. The approach is general and independent on the adopted motion analysis system. It wants to provide indications for safely performing working activities. For example, the proposed WGD can also be used to evaluate the kinematics of workers in real working environments thanks to the adoption of unobtrusive measuring systems, such as wearable sensors through the extracted indicators and thresholds. Full article

(This article belongs to the Special Issue Advances and Application of Human Movement Sensors)

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13 pages, 8367 KiB

Open AccessCommunication

Novel Conformal Skin Patch with Embedded Thin-Film Electrodes for Early Detection of Extravasation

by Ruiqi Lim, Ramona B. Damalerio, Choon Looi Bong, Swee Kim Tan and Ming-Yuan Cheng

Sensors 2021, 21(10), 3429; https://doi.org/10.3390/s21103429 - 14 May 2021

Cited by 5 | Viewed by 6738

Abstract

Extravasation is a complication of intravenous (IV) cannulation in which vesicant drugs leak from a vein into the surrounding subcutaneous tissue. The severity of extravasation depends on the type, concentration, and volume of drugs that accumulate in the subcutaneous tissue. Rapid detection of extravasation can facilitate prompt medical intervention, minimizing tissue damage, and preventing adverse events. In this study, we present two portable sensor patches, namely gold- and carbon-based sensing patches, for early detection of extravasation. The gold-based sensor patch detected extravasated fluid of volume as low as 2 mL in in vivo animal models and human clinical trials; the patch exhibited a resistance change of 41%. The carbon-based sensor patch exhibited a resistance change of 51% for 2 mL of extravasated fluid, and fabrication throughput and cost-effectiveness are superior for this patch compared with the gold-based sensing patch. Full article

(This article belongs to the Special Issue Advances and Application of Human Movement Sensors)

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15 pages, 1637 KiB

Open AccessArticle

Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint–Node Plots

by Posen Lee, Tai-Been Chen, Chi-Yuan Wang, Shih-Yen Hsu and Chin-Hsuan Liu

Sensors 2021, 21(9), 3212; https://doi.org/10.3390/s21093212 - 5 May 2021

Cited by 4 | Viewed by 2308

Abstract

Postural control decreases with aging. Thus, an efficient and accurate method of detecting postural control is needed. We enrolled 35 elderly adults (aged 82.06 ± 8.74 years) and 20 healthy young adults (aged 21.60 ± 0.60 years) who performed standing tasks for 40 s, performed six times. The coordinates of 15 joint nodes were captured using a Kinect device (30 Hz). We plotted joint positions into a single 2D figure (named a joint–node plot, JNP) once per second for up to 40 s. A total of 15 methods combining deep and machine learning for postural control classification were investigated. The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and kappa values of the selected methods were assessed. The highest PPV, NPV, accuracy, sensitivity, specificity, and kappa values were higher than 0.9 in validation testing. The presented method using JNPs demonstrated strong performance in detecting the postural control ability of young and elderly adults. Full article

(This article belongs to the Special Issue Advances and Application of Human Movement Sensors)

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10 pages, 3395 KiB

Open AccessArticle

A Hand-Worn Inertial Measurement Unit for Detection of Bat–Ball Impact during Baseball Hitting

by Niroshan G. Punchihewa, Hideki Arakawa, Etsuo Chosa and Go Yamako

Sensors 2021, 21(9), 3002; https://doi.org/10.3390/s21093002 - 25 Apr 2021

Cited by 2 | Viewed by 3136

Abstract

Swinging a baseball bat at a pitched ball takes less than half of a second. A hitter uses his lower extremities to generate power, and coordination of the swing motion gradually transfers power through the trunk to the upper extremities during bat–ball impact. The most important instant of the baseball swing is at the bat–ball impact, after which the direction, speed, height, and distance of the hit ball determines whether runs can be scored. Thus, analyzing the biomechanical parameters at the bat–ball impact is useful for evaluating player performance. Different motion-capture systems use different methods to identify bat–ball impact. However, the level of accuracy to detect bat–ball impact is not well documented. The study aim was to examine the required accuracy to detect bat–ball impact timing. The results revealed that ±2 ms accuracy is required to report trunk and hand kinematics, especially for higher-order time-derivatives. Here, we propose a new method using a hand-worn inertial measurement unit to accurately detect bat–ball impact timing. The results of this study will be beneficial for analyzing the kinematics of baseball hitting under real-game conditions. Full article

(This article belongs to the Special Issue Advances and Application of Human Movement Sensors)

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16 pages, 973 KiB

Open AccessArticle

A Portable System for Remote Rehabilitation Following a Total Knee Replacement: A Pilot Randomized Controlled Clinical Study

by Kevin M. Bell, Chukwudi Onyeukwu, Clair N. Smith, Adrianna Oh, Annette Devito Dabbs, Sara R. Piva, Adam J. Popchak, Andrew D. Lynch, James J. Irrgang and Michael P. McClincy

Sensors 2020, 20(21), 6118; https://doi.org/10.3390/s20216118 - 27 Oct 2020

Cited by 18 | Viewed by 4167

Abstract

Rehabilitation has been shown to improve functional outcomes following total knee replacement (TKR). However, its delivery and associated costs are highly variable. The authors have developed and previously validated the accuracy of a remote (wearable) rehabilitation monitoring platform (interACTION). The present study’s objective was to assess the feasibility of utilizing interACTION for the remote management of rehabilitation after TKR and to determine a preliminary estimate of the effects of the interACTION system on the value of rehabilitation. Specifically, we tested post-operative outpatient rehabilitation supplemented with interACTION (n = 13) by comparing it to a standard post-operative outpatient rehabilitation program (n = 12) using a randomized design. Attrition rates were relatively low and not significantly different between groups, indicating that participants found both interventions acceptable. A small (not statistically significant) decrease in the number of physical therapy visits was observed in the interACTION Group, therefore no significant difference in total cost could be observed. All patients and physical therapists in the interACTION Group indicated that they would use the system again in the future. Therefore, the next steps are to address the concerns identified in this pilot study and to expand the platform to include behavioral change strategies prior to conducting a full-scale randomized controlled trial. Trial registration: ClinicalTrials.gov NCT02646761 “interACTION: A Portable Joint Function Monitoring and Training System for Remote Rehabilitation Following TKA” 6 January 2016. Full article

(This article belongs to the Special Issue Advances and Application of Human Movement Sensors)

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