sensors-logo

Journal Browser

Journal Browser

Sensing Human Movement through Wearables

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

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 14527

Special Issue Editors

School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, QLD, Australia
Interests: trauma-informed movement and physical activity; embedding Aboriginal and Torres Strait Islander ways of knowing, being and doing; wearable technology in the assessment of human movement; strategic leadership; child development and inclusive sport and exercise systems; engaging diverse peoples in education
Prof. Dr. Jim Lee
E-Mail Website
Guest Editor
Sabel Labs, Brisbane, QLD, Australia
Interests: Biomechancis
Dr. Sam Gleadhill
E-Mail Website
Guest Editor
National Institute of Fitness and Sports in Kanoya, Kanoya, Kagoshima 891-2393, Japan
Interests: Biomechancis
Dr. Charlene Willis
E-Mail Website
Guest Editor
School of PAM (Anatomy Physiology Biology), Griffith University, Nathan, QLD 4111, Australia
Interests: Anatomy and STEM

Special Issue Information

Dear Colleagues,

The development of the innovative sensors measuring human movement has revolutionised sport science and exercise programming. Athletes are now better prepared to take on the challenges of sport performance in part due to the richness of data collected through sensors. Sport coaches are also more informed about the requirements of their athletes’ sport performance so that their training programs and testing procedures now more accurately reflect human performance within a sport specific context. It is also clear that the increasing applications of sensors has led to considerable improvements in human performance in areas such as defence science as well as workplace health and wellbeing. The portability of sesnors has made it possible to measure performance within task specific environments so that results more accurately reflect human performance under combat situations (defence science) or other occupational environments (workplace health and safety). Furthermore, the use of sensors has revolutionised learning environments whereby human movemement can be assessed using sensors with the results integrated into educational programs and curriculum. This emerging area of sensor research employs sport science techniques within an primary or secondary educational context to better build the aspirations, understandings and knowledges of the next generation of scientists and researchers.

Dr. Keane W. Wheeler
Prof. Dr. James Lee
Dr. Sam Gleadhill
Dr. Charlene Willis
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

  • Motion
  • Monitoring
  • Wearable
  • Exercise
  • Sport
  • Human activity
  • Technologies
  • Health
  • Science
  • STEM

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 4267 KiB  
Article
ArtiLock: Smartphone User Identification Based on Physiological and Behavioral Features of Monosyllable Articulation
Sensors 2023, 23(3), 1667; https://doi.org/10.3390/s23031667 - 02 Feb 2023
Viewed by 1044
Abstract
Although voice authentication is generally secure, voiceprint-based authentication methods have the drawback of being affected by environmental noise, long passphrases, and large registered samples. Therefore, we present a breakthrough idea for smartphone user authentication by analyzing articulation and integrating the physiology and behavior [...] Read more.
Although voice authentication is generally secure, voiceprint-based authentication methods have the drawback of being affected by environmental noise, long passphrases, and large registered samples. Therefore, we present a breakthrough idea for smartphone user authentication by analyzing articulation and integrating the physiology and behavior of the vocal tract, tongue position, and lip movement to expose the uniqueness of individuals while making utterances. The key idea is to leverage the smartphone speaker and microphone to simultaneously transmit and receive speech and ultrasonic signals, construct identity-related features, and determine whether a single utterance is a legitimate user or an attacker. Physiological authentication methods prevent other users from copying or reproducing passwords. Compared to other types of behavioral authentication, the system is more accurately able to recognize the user’s identity and adapt accordingly to environmental variations. The proposed system requires a smaller number of samples because single utterances are utilized, resulting in a user-friendly system that resists mimicry attacks with an average accuracy of 99% and an equal error rate of 0.5% under the three different surroundings. Full article
(This article belongs to the Special Issue Sensing Human Movement through Wearables)
Show Figures

Figure 1

17 pages, 40392 KiB  
Article
Catch Recognition in Automated American Football Training Using Machine Learning
Sensors 2023, 23(2), 840; https://doi.org/10.3390/s23020840 - 11 Jan 2023
Viewed by 1633
Abstract
In order to train receivers in American football in a targeted and individual manner, the strengths and weaknesses of the athletes must be evaluated precisely. As human resources are limited, it is beneficial to do it in an automated way. Automated passing machines [...] Read more.
In order to train receivers in American football in a targeted and individual manner, the strengths and weaknesses of the athletes must be evaluated precisely. As human resources are limited, it is beneficial to do it in an automated way. Automated passing machines are already given, therefore the motivation is to design a computer-based system that records and automatically evaluates the athlete’s catch attempts. The most fundamental evaluation would be whether the athlete has caught the pass successfully or not. An experiment was carried out to gain data about catch attempts that potentially contain information about the outcome of such. The experiment used a fully automated passing machine which can release passes on command. After a pass was released, an audio and a video sequence of the specific catch attempt was recorded. For this purpose, an audio-visual recording system was developed which was integrated into the passing machine. This system is used to create an audio and video dataset in the amount of 2276 recorded catch attempts. A Convolutional Neural Network (CNN) is used for feature extraction with downstream Long Short-Term Memory (LSTM) to classify the video data. Classification of the audio data is performed using a one-dimensional CNN. With the chosen neural network architecture, an accuracy of 92.19% was achieved in detecting whether a pass had been caught or not. The feasibility for automatic classification of catch attempts during automated catch training is confirmed with this result. Full article
(This article belongs to the Special Issue Sensing Human Movement through Wearables)
Show Figures

Figure 1

20 pages, 782 KiB  
Article
Acoustic Sensing Based on Online Handwritten Signature Verification
Sensors 2022, 22(23), 9343; https://doi.org/10.3390/s22239343 - 30 Nov 2022
Cited by 2 | Viewed by 1484
Abstract
Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, [...] Read more.
Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online signature verification system, it provides handy and safe paper-based signature verification services. The prime notion is to utilize the acoustic signals that are bounced back via a pen tip to depict a user’s signing pattern. We designed the signal modulation stratagem carefully to guarantee high performance, developed a distance measurement algorithm based on phase shift, and trained a verification model. In comparison with the traditional signature verification scheme, SilentSign allows users to sign more conveniently as well as invisibly. To evaluate SilentSign in various settings, we conducted comprehensive experiments with 35 participants. Our results reveal that SilentSign can attain 98.2% AUC and 1.25% EER. We note that a shorter conference version of this paper was presented in Percom (2019). Our initial conference paper did not finish the complete experiment. This manuscript has been revised and provided additional experiments to the conference proceedings; for example, by including System Robustness, Computational Overhead, etc. Full article
(This article belongs to the Special Issue Sensing Human Movement through Wearables)
Show Figures

Figure 1

20 pages, 3487 KiB  
Article
Co-Operative Design of a Coach Dashboard for Training Monitoring and Feedback
Sensors 2022, 22(23), 9073; https://doi.org/10.3390/s22239073 - 23 Nov 2022
Viewed by 2113
Abstract
Athlete development depends on many factors that need to be balanced by the coach. The amount of data collected grows with the development of sensor technology. To make data-informed decisions for training prescription of their athletes, coaches could be supported by feedback through [...] Read more.
Athlete development depends on many factors that need to be balanced by the coach. The amount of data collected grows with the development of sensor technology. To make data-informed decisions for training prescription of their athletes, coaches could be supported by feedback through a coach dashboard. The aim of this paper is to describe the design of a coach dashboard based on scientific knowledge, user requirements, and (sensor) data to support decision making of coaches for athlete development in cyclic sports. The design process involved collaboration with coaches, embedded scientists, researchers, and IT professionals. A classic design thinking process was used to structure the research activities in five phases: empathise, define, ideate, prototype, and test phases. To understand the user requirements of coaches, a survey (n = 38), interviews (n = 8) and focus-group sessions (n = 4) were held. Design principles were adopted into mock-ups, prototypes, and the final coach dashboard. Designing a coach dashboard using the co-operative research design helped to gain deep insights into the specific user requirements of coaches in their daily training practice. Integrating these requirements, scientific knowledge, and functionalities in the final coach dashboard allows the coach to make data-informed decisions on training prescription and optimise athlete development. Full article
(This article belongs to the Special Issue Sensing Human Movement through Wearables)
Show Figures

Figure 1

13 pages, 692 KiB  
Article
Optimizing Reaction Time in Relation to Manual and Foot Laterality in Children Using the Fitlight Technological Systems
Sensors 2022, 22(22), 8785; https://doi.org/10.3390/s22228785 - 14 Nov 2022
Cited by 9 | Viewed by 1704
Abstract
The purpose of the study was to design and implement, in the physical and sports education process and in the motor evaluation process, a program of exercises and specific tests to optimize reaction time by using the Fitlight technological systems in relation to [...] Read more.
The purpose of the study was to design and implement, in the physical and sports education process and in the motor evaluation process, a program of exercises and specific tests to optimize reaction time by using the Fitlight technological systems in relation to the manual and foot laterality of the pupils and identification of gender differences regarding the development of reaction speed. The study included 231 pupils, between 10 and 11 years old, who were divided into two groups according to gender, as follows: the male sample included 109 (97.32%) subjects, and the female sample included 103 (94.45%) participants. All subjects were identified with right manual and foot laterality. Both samples performed a specific exercise program to optimize reaction time in relation to manual and foot laterality by using Fitlight technologies. In the study, four tests were applied in order to evaluate reaction times using Fitlight, two in relation to the manual laterality and two with foot laterality, and the results were statistically processed with IBM SPPS Statistic 24 (IBM Corp., Armonk, NY, USA). Through the comparative analysis of the samples and the progress aimed at optimizing the reaction time specific to our study, it was found that the female sample recorded greater progress at the level of manual laterality, both for the right hand and for the left one, while the sample of boys recorded significant progress in terms of improving reaction time at the level of right and left foot laterality. At the foot laterality level, the results for the executions with the right foot were better in the simple test with four Fitlight spotlights in a line, and for the complex test, with eight Fitlight spotlights in a square, the results were better in the executions with the left foot. This reveals the fact that the greater the execution complexity, the better the motor prevalence on the left side. Full article
(This article belongs to the Special Issue Sensing Human Movement through Wearables)
Show Figures

Figure 1

16 pages, 2646 KiB  
Article
Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors
Sensors 2022, 22(10), 3865; https://doi.org/10.3390/s22103865 - 19 May 2022
Cited by 2 | Viewed by 1441
Abstract
The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost IMU [...] Read more.
The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost IMU sensor. Each subject was observed using three sensors at the shoe, shin, and thigh. We show that existing general-purpose architectures outperform classical feature-based approaches and propose a novel architecture tailored for this task. The proposed architecture is based on a semi-supervised variational auto-encoder structure with innovated decoder in the form of a dense layer with a sinusoidal activation function. The proposed architecture achieved the lowest average error on the test data. Analysis of sensor placement reveals that the best location for the sensor is the shoe. Significant accuracy gain was observed when all three sensors were available. All data acquired in this experiment and the code of the estimation methods are available for download. Full article
(This article belongs to the Special Issue Sensing Human Movement through Wearables)
Show Figures

Figure 1

18 pages, 33439 KiB  
Article
Human Activity Recognition Based on Residual Network and BiLSTM
Sensors 2022, 22(2), 635; https://doi.org/10.3390/s22020635 - 14 Jan 2022
Cited by 58 | Viewed by 4079
Abstract
Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity [...] Read more.
Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters. Full article
(This article belongs to the Special Issue Sensing Human Movement through Wearables)
Show Figures

Figure 1

Back to TopTop