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

Deadline for manuscript submissions: 25 May 2025 | Viewed by 13346

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Special Issue Editor

Prof. Dr. Iain D. Murray

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Guest Editor

Faculty of Science and Engineering, Curtin University, Perth 6845, Australia
Interests: assistive technology; vision impairment; embedded system; steaching and learning

Special Issue Information

Dear Colleagues,

Recently, we have witnessed a surge of consumer-level smart sensors and wearables that focus on monitoring human movement However, in many cases, there is a lack of academic research and validation on the methods used by these devices. This special issue aims to put together original research and review articles on recent advances in sensors for human movement recognition and analysis. Potential applications are:

Assistive technology
Rehabilitation
Sport Science
Smart PPE equipment

Please note that in accordance with MDPI sensor’s scope, full experimental details must be provided so that the results can be reproduced. Additionally, any manuscript that includes data from human participants will require ethics approval from relevant authorizing bodies. There is a special interest in cross-discipline research where engineers have been collaborating with health science clinicians or researchers that can provide feedback on the clinical validity of the measurements.

Prof. Dr. Iain D. Murray
Guest Editor

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Published Papers (7 papers)

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Research

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18 pages, 2362 KiB

Open AccessArticle

Exploring the Impact of the NULL Class on In-the-Wild Human Activity Recognition

by Josh Cherian, Samantha Ray, Paul Taele, Jung In Koh and Tracy Hammond

Sensors 2024, 24(12), 3898; https://doi.org/10.3390/s24123898 - 16 Jun 2024

Viewed by 686

Abstract

Monitoring activities of daily living (ADLs) plays an important role in measuring and responding to a person’s ability to manage their basic physical needs. Effective recognition systems for monitoring ADLs must successfully recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges associated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of preprocessing techniques to increase recall and postprocessing techniques to increase precision can result in more desirable models for tasks such as ADL monitoring. In a user-independent evaluation using in-the-wild data, these techniques resulted in a model that achieved an event-based F1-score of over 0.9 for brushing teeth, combing hair, walking, and washing hands. This work tackles fundamental challenges in machine learning that will need to be addressed in order for these systems to be deployed and reliably work in the real world. Full article

(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

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13 pages, 1812 KiB

Open AccessArticle

Optimizing Epoch Length and Activity Count Threshold Parameters in Accelerometry: Enhancing Upper Extremity Use Quantification in Cerebral Palsy

by Isabelle Poitras, Léandre Gagné-Pelletier, Jade Clouâtre, Véronique H. Flamand, Alexandre Campeau-Lecours and Catherine Mercier

Sensors 2024, 24(4), 1100; https://doi.org/10.3390/s24041100 - 8 Feb 2024

Cited by 1 | Viewed by 992

Abstract

Various accelerometry protocols have been used to quantify upper extremity (UE) activity, encompassing diverse epoch lengths and thresholding methods. However, there is no consensus on the most effective approach. The aim of this study was to delineate the optimal parameters for analyzing accelerometry data to quantify UE use in individuals with unilateral cerebral palsy (CP). Methods: A group of adults with CP (n = 15) participated in six activities of daily living, while a group of children with CP (n = 14) underwent the Assisting Hand Assessment. Both groups performed the activities while wearing ActiGraph GT9X-BT devices on each wrist, with concurrent video recording. Use ratio (UR) derived from accelerometry and video analysis and accelerometer data were compared for different epoch lengths (1, 1.5, and 2 s) and activity count (AC) thresholds (between 2 and 150). Results: In adults, results are comparable across epoch lengths, with the best AC thresholds being ≥ 100. In children, results are similar across epoch lengths of 1 and 1.5 (optimal AC threshold = 50), while the optimal threshold is higher with an epoch length of 2 (AC = 75). Conclusions: The combination of epoch length and AC thresholds should be chosen carefully as both influence the validity of the quantification of UE use. Full article

(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

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16 pages, 1827 KiB

Open AccessArticle

Performance Analysis of the Spanish Men’s Top and Second Professional Football Division Teams during Eight Consecutive Seasons

by Ibai Errekagorri, Roberto López del Campo, Ricardo Resta and Julen Castellano

Sensors 2023, 23(22), 9115; https://doi.org/10.3390/s23229115 - 11 Nov 2023

Cited by 2 | Viewed by 1744

Abstract

The present study aimed to analyse the performance of the Spanish men’s top (LaLiga1) and second (LaLiga2) professional football division teams for eight consecutive seasons (from 2011–2012 to 2018–2019). The variables recorded were Passes, Successful Passes, Crosses, Shots, Goals, Corners, Fouls, Width, Length, Height, distance from the goalkeeper to the nearest defender (GkDef) and total distance covered (TD). The main results were that (1) LaLiga1 teams showed lower values of Length from 2013–2014, and lower values of GkDef and TD from 2014–2015; (2) LaLiga2 teams showed fewer Passes and lower values of GkDef and TD from 2014–2015, and fewer Goals and lower values of Length from 2015–2016; and (3) LaLiga1 teams showed more Passes, Successful Passes, Shots and Goals and higher values of TD compared to LaLiga2 teams during the eight-season period. This study concludes that LaLiga1 teams showed fewer final offensive actions, LaLiga2 teams showed fewer Passes and Goals and the teams of both leagues played in a space with greater density (meters by player), covering less distance as the seasons passed. The information provided in this study makes it possible to have reference values that have characterised the performance of the teams. Full article

(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

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27 pages, 496 KiB

Open AccessArticle

Measures of Maximal Tactile Pressures during a Sustained Grasp Task Using a TactArray Device Have Satisfactory Reliability and Concurrent Validity in People with Stroke

by Urvashy Gopaul, Derek Laver, Leeanne Carey, Thomas Matyas, Paulette van Vliet and Robin Callister

Sensors 2023, 23(6), 3291; https://doi.org/10.3390/s23063291 - 20 Mar 2023

Cited by 1 | Viewed by 2143

Abstract

Sensor-based devices can record pressure or force over time during grasping and therefore offer a more comprehensive approach to quantifying grip strength during sustained contractions. The objectives of this study were to investigate the reliability and concurrent validity of measures of maximal tactile pressures and forces during a sustained grasp task using a TactArray device in people with stroke. Participants with stroke (n = 11) performed three trials of sustained maximal grasp over 8 s. Both hands were tested in within- and between-day sessions, with and without vision. Measures of maximal tactile pressures and forces were measured for the complete (8 s) grasp duration and plateau phase (5 s). Tactile measures are reported using the highest value among three trials, the mean of two trials, and the mean of three trials. Reliability was determined using changes in mean, coefficients of variation, and intraclass correlation coefficients (ICCs). Pearson correlation coefficients were used to evaluate concurrent validity. This study found that measures of reliability assessed by changes in means were good, coefficients of variation were good to acceptable, and ICCs were very good for maximal tactile pressures using the average pressure of the mean of three trials over 8 s in the affected hand with and without vision for within-day sessions and without vision for between-day sessions. In the less affected hand, changes in mean were very good, coefficients of variations were acceptable, and ICCs were good to very good for maximal tactile pressures using the average pressure of the mean of three trials over 8 s and 5 s, respectively, in between-day sessions with and without vision. Maximal tactile pressures had moderate correlations with grip strength. The TactArray device demonstrates satisfactory reliability and concurrent validity for measures of maximal tactile pressures in people with stroke. Full article

(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

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17 pages, 4085 KiB

Open AccessArticle

Dynamic Edge Convolutional Neural Network for Skeleton-Based Human Action Recognition

by Nusrat Tasnim and Joong-Hwan Baek

Sensors 2023, 23(2), 778; https://doi.org/10.3390/s23020778 - 10 Jan 2023

Cited by 15 | Viewed by 3874

Abstract

To provide accessible, intelligent, and efficient remote access such as the internet of things, rehabilitation, autonomous driving, virtual games, and healthcare, human action recognition (HAR) has gained much attention among computer vision researchers. Several methods have already been addressed to ensure effective and efficient action recognition based on different perspectives including data modalities, feature design, network configuration, and application domains. In this article, we design a new deep learning model by integrating criss-cross attention and edge convolution to extract discriminative features from the skeleton sequence for action recognition. The attention mechanism is applied in spatial and temporal directions to pursue the intra- and inter-frame relationships. Then, several edge convolutional layers are conducted to explore the geometric relationships among the neighboring joints in the human body. The proposed model is dynamically updated after each layer by recomputing the graph on the basis of k-nearest joints for learning local and global information in action sequences. We used publicly available benchmark skeleton datasets such as UTD-MHAD (University of Texas at Dallas multimodal human action dataset) and MSR-Action3D (Microsoft action 3D) to evaluate the proposed method. We also investigated the proposed method with different configurations of network architectures to assure effectiveness and robustness. The proposed method achieved average accuracies of 99.53% and 95.64% on the UTD-MHAD and MSR-Action3D datasets, respectively, outperforming state-of-the-art methods. Full article

(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

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23 pages, 11407 KiB

Open AccessArticle

Posture Monitoring and Correction Exercises for Workers in Hostile Environments Utilizing Non-Invasive Sensors: Algorithm Development and Validation

by Siavash Khaksar, Stefanie Pieters, Bita Borazjani, Joshua Hyde, Harrison Booker, Adil Khokhar, Iain Murray and Amity Campbell

Sensors 2022, 22(24), 9618; https://doi.org/10.3390/s22249618 - 8 Dec 2022

Cited by 2 | Viewed by 2391

Abstract

Personal protective equipment (PPE) is an essential key factor in standardizing safety within the workplace. Harsh working environments with long working hours can cause stress on the human body that may lead to musculoskeletal disorder (MSD). MSD refers to injuries that impact the muscles, nerves, joints, and many other human body areas. Most work-related MSD results from hazardous manual tasks involving repetitive, sustained force, or repetitive movements in awkward postures. This paper presents collaborative research from the School of Electrical Engineering and School of Allied Health at Curtin University. The main objective was to develop a framework for posture correction exercises for workers in hostile environments, utilizing inertial measurement units (IMU). The developed system uses IMUs to record the head, back, and pelvis movements of a healthy participant without MSD and determine the range of motion of each joint. A simulation was developed to analyze the participant’s posture to determine whether the posture present would pose an increased risk of MSD with limits to a range of movement set based on the literature. When compared to measurements made by a goniometer, the body movement recorded 94% accuracy and the wrist movement recorded 96% accuracy. Full article

(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

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Review

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12 pages, 580 KiB

Open AccessReview

Movement Sensing Opportunities for Monitoring Dynamic Cognitive States

by Tad T. Brunyé, James McIntyre, Gregory I. Hughes and Eric L. Miller

Sensors 2024, 24(23), 7530; https://doi.org/10.3390/s24237530 - 25 Nov 2024

Viewed by 244

Abstract

In occupational domains such as sports, healthcare, driving, and military, both individuals and small groups are expected to perform challenging tasks under adverse conditions that induce transient cognitive states such as stress, workload, and uncertainty. Wearable and standoff 6DOF sensing technologies are advancing rapidly, including increasingly miniaturized yet robust inertial measurement units (IMUs) and portable marker-less infrared optical motion tracking. These sensing technologies may offer opportunities to track overt physical behavior and classify cognitive states relevant to human performance in diverse human–machine domains. We describe progress in research attempting to distinguish cognitive states by tracking movement behavior in both individuals and small groups, examining potential applications in sports, healthcare, driving, and the military. In the context of military training and operations, there are no generally accepted methods for classifying transient mental states such as uncertainty from movement-related data, despite its importance for shaping decision-making and behavior. To fill this gap, an example data set is presented including optical motion capture of rifle trajectories during a dynamic marksmanship task that elicits variable uncertainty; using machine learning, we demonstrate that features of weapon trajectories capturing the complexity of motion are valuable for classifying low versus high uncertainty states. We argue that leveraging metrics of human movement behavior reveals opportunities to complement relatively costly and less portable neurophysiological sensing technologies and enables domain-specific human–machine interfaces to support a wide range of cognitive functions. Full article

(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

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