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17 pages, 1179 KiB

Open AccessArticle

Discovering Homogeneous Groups from Geo-Tagged Videos

by Xuejing Di, Dong June Lew and Kwang Woo Nam

Sensors 2023, 23(9), 4443; https://doi.org/10.3390/s23094443 - 01 May 2023

Viewed by 1289

The popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in applications such [...] Read more.

The popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in applications such as how groups of videographers behave and in future-movement prediction. In this paper, first we propose algorithms to discover homogeneous groups from geo-tagged videos with view directions. Second, we extend the density clustering algorithm to support fields-of-view (FoVs) in the geo-tagged videos and propose an optimization model based on a two-level grid-based index. We show the efficiency and effectiveness of the proposed homogeneous-pattern-discovery approach through experimental evaluation on real and synthetic datasets. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing)

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16 pages, 1613 KiB

Open AccessArticle

Validity of a Smartphone Application in Calculating Measures of Heart Rate Variability

by Andreas T. Himariotis, Kyle F. Coffey, Sabrina E. Noel and David J. Cornell

Sensors 2022, 22(24), 9883; https://doi.org/10.3390/s22249883 - 15 Dec 2022

Cited by 3 | Viewed by 1883

Abstract

The purpose of the current study was to determine the concurrent validity of the Elite HRV smartphone application when calculating heart rate variability (HRV) metrics in reference to an independent software criterion. A total of 5 minutes of R–R interval and natural log [...] Read more.

The purpose of the current study was to determine the concurrent validity of the Elite HRV smartphone application when calculating heart rate variability (HRV) metrics in reference to an independent software criterion. A total of 5 minutes of R–R interval and natural log of root mean square of the successive differences (lnRMSSD) resting HRV data were simultaneously collected using two Polar H10 heart rate monitors (HRMs) in both the seated and supine positions from 22 participants (14 males, 8 females). One H10 HRM was paired with a Polar V800 watch and one with the Elite HRV application. When no artifact correction was applied, significant, but small, differences in the lnRMSSD data were observed between the software in the seated position (p = 0.022), and trivial and nonstatistically significant differences were observed in the supine position (p = 0.087). However, significant differences (p > 0.05) in the lnRMSSD data were no longer identifiable in either the seated or the supine positions when applying Very Low, Low, or Automatic artifact-correction filters. Additionally, excellent agreements (ICC_3,1 = 0.938 − 0.998) and very strong to near-perfect (r = 0.889 − 0.997) relationships were observed throughout all correction levels. The Elite HRV smartphone application is a valid tool for calculating resting lnRMSSD HRV metrics. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing)

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16 pages, 2853 KiB

Open AccessArticle

Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario Monitored through EEG and ECG

by Vasileios Aspiotis, Andreas Miltiadous, Konstantinos Kalafatakis, Katerina D. Tzimourta, Nikolaos Giannakeas, Markos G. Tsipouras, Dimitrios Peschos, Euripidis Glavas and Alexandros T. Tzallas

Sensors 2022, 22(15), 5792; https://doi.org/10.3390/s22155792 - 03 Aug 2022

Cited by 15 | Viewed by 3898

Abstract

Over the last decade, virtual reality (VR) has become an increasingly accessible commodity. Head-mounted display (HMD) immersive technologies allow researchers to simulate experimental scenarios that would be unfeasible or risky in real life. An example is extreme heights exposure simulations, which can be [...] Read more.

Over the last decade, virtual reality (VR) has become an increasingly accessible commodity. Head-mounted display (HMD) immersive technologies allow researchers to simulate experimental scenarios that would be unfeasible or risky in real life. An example is extreme heights exposure simulations, which can be utilized in research on stress system mobilization. Until recently, electroencephalography (EEG)-related research was focused on mental stress prompted by social or mathematical challenges, with only a few studies employing HMD VR techniques to induce stress. In this study, we combine a state-of-the-art EEG wearable device and an electrocardiography (ECG) sensor with a VR headset to provoke stress in a high-altitude scenarios while monitoring EEG and ECG biomarkers in real time. A robust pipeline for signal clearing is implemented to preprocess the noise-infiltrated (due to movement) EEG data. Statistical and correlation analysis is employed to explore the relationship between these biomarkers with stress. The participant pool is divided into two groups based on their heart rate increase, where statistically important EEG biomarker differences emerged between them. Finally, the occipital-region band power changes and occipital asymmetry alterations were found to be associated with height-related stress and brain activation in beta and gamma bands, which correlates with the results of the self-reported Perceived Stress Scale questionnaire. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing)

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19 pages, 3426 KiB

Open AccessArticle

Respiratory Inductance Plethysmography to Assess Fatigability during Repetitive Work

by Luís Silva, Mariana Dias, Duarte Folgado, Maria Nunes, Praneeth Namburi, Brian Anthony, Diogo Carvalho, Miguel Carvalho, Elazer Edelman and Hugo Gamboa

Sensors 2022, 22(11), 4247; https://doi.org/10.3390/s22114247 - 02 Jun 2022

Cited by 3 | Viewed by 2643

Abstract

Cumulative fatigue during repetitive work is associated with occupational risk and productivity reduction. Usually, subjective measures or muscle activity are used for a cumulative evaluation; however, Industry 4.0 wearables allow overcoming the challenges observed in those methods. Thus, the aim of this study [...] Read more.

Cumulative fatigue during repetitive work is associated with occupational risk and productivity reduction. Usually, subjective measures or muscle activity are used for a cumulative evaluation; however, Industry 4.0 wearables allow overcoming the challenges observed in those methods. Thus, the aim of this study is to analyze alterations in respiratory inductance plethysmography (RIP) to measure the asynchrony between thorax and abdomen walls during repetitive work and its relationship with local fatigue. A total of 22 healthy participants (age: 27.0 ± 8.3 yrs; height: 1.72 ± 0.09 m; mass: 63.4 ± 12.9 kg) were recruited to perform a task that includes grabbing, moving, and placing a box in an upper and lower shelf. This task was repeated for 10 min in three trials with a fatigue protocol between them. Significant main effects were found from Baseline trial to the Fatigue trials (p < 0.001) for both RIP correlation and phase synchrony. Similar results were found for the activation amplitude of agonist muscle (p < 0.001), and to the muscle acting mainly as a joint stabilizer (p < 0.001). The latter showed a significant effect in predicting both RIP correlation and phase synchronization. Both RIP correlation and phase synchronization can be used for an overall fatigue assessment during repetitive work. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing)

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14 pages, 526 KiB

Open AccessArticle

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

by Rui Varandas, Rodrigo Lima, Sergi Bermúdez I Badia, Hugo Silva and Hugo Gamboa

Sensors 2022, 22(11), 4010; https://doi.org/10.3390/s22114010 - 25 May 2022

Cited by 4 | Viewed by 2921

Abstract

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact [...] Read more.

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing)

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16 pages, 30706 KiB

Open AccessArticle

Exploiting Resistive Matrix Technology to Build a Stretchable Sensorised Sock for Gait Analysis in Daily Life

by Nicola Carbonaro, Lucia Arcarisi, Carlotta Marinai, Marco Laurino, Francesco Di Rienzo, Carlo Vallati and Alessandro Tognetti

Sensors 2022, 22(5), 1761; https://doi.org/10.3390/s22051761 - 24 Feb 2022

Cited by 4 | Viewed by 2503

Abstract

We describe the development and preliminary evaluation of an innovative low-cost wearable device for gait analysis. We have developed a sensorized sock equipped with 32 piezoresistive textile-based sensors integrated in the heel and metatarsal areas for the detection of signals associated with the [...] Read more.

We describe the development and preliminary evaluation of an innovative low-cost wearable device for gait analysis. We have developed a sensorized sock equipped with 32 piezoresistive textile-based sensors integrated in the heel and metatarsal areas for the detection of signals associated with the contact pressures generated during walking phases. To build the sock, we applied a sensing patch on a commercially available sock. The sensing patch is a stretchable circuit based on the resistive matrix method, in which conductive stripes, based on conductive inks, are coupled with piezoresistive fabrics to form sensing elements. In our sensorized sock, we introduced many relevant improvements to overcome the limitations of the classical resistive matrix method. We preliminary evaluated the sensorized sock on five healthy subjects by performing a total of 80 walking tasks at different speeds for a known distance. Comparison of step count and step-to-step frequency versus reference measurements showed a high correlation between the estimated measure and the real one. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing)

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16 pages, 2067 KiB

Open AccessArticle

Towards a Contactless Stress Classification Using Thermal Imaging

by Federica Gioia, Alberto Greco, Alejandro Luis Callara and Enzo Pasquale Scilingo

Sensors 2022, 22(3), 976; https://doi.org/10.3390/s22030976 - 27 Jan 2022

Cited by 15 | Viewed by 2830

Abstract

Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates the contribution given by thermal imaging [...] Read more.

Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates the contribution given by thermal imaging for an effective automatic stress detection with the perspective of a contactless stress recognition system. To this aim, we recorded both ANS correlates (cardiac, electrodermal, and respiratory activity) and thermal images from 25 volunteers under acute stress induced by the Stroop test. We conducted a statistical analysis on the features extracted from each signal, and we implemented subject-independent classifications based on the support vector machine model with an embedded recursive feature elimination algorithm. Particularly, we trained three classifiers using different feature sets: the full set of features, only those derived from the peripheral autonomic correlates, and only those derived from the thermal images. Classification accuracy and feature selection results confirmed the relevant contribution provided by the thermal features in the acute stress detection task. Indeed, a combination of ANS correlates and thermal features achieved 97.37% of accuracy. Moreover, using only thermal features we could still successfully detect stress with an accuracy of 86.84% in a contact-free manner. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing)

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10 pages, 2226 KiB

Open AccessCommunication

Poor Motor Coordination Elicits Altered Lower Limb Biomechanics in Young Football (Soccer) Players: Implications for Injury Prevention through Wearable Sensors

by Stefano Di Paolo, Stefano Zaffagnini, Nicola Pizza, Alberto Grassi and Laura Bragonzoni

Sensors 2021, 21(13), 4371; https://doi.org/10.3390/s21134371 - 25 Jun 2021

Cited by 14 | Viewed by 3882

Abstract

Motor coordination and lower limb biomechanics are crucial aspects of anterior cruciate ligament (ACL) injury prevention strategies in football. These two aspects have never been assessed together in real scenarios in the young population. The present study aimed to investigate the influence of [...] Read more.

Motor coordination and lower limb biomechanics are crucial aspects of anterior cruciate ligament (ACL) injury prevention strategies in football. These two aspects have never been assessed together in real scenarios in the young population. The present study aimed to investigate the influence of motor coordination on lower limb biomechanics in young footballers during an on-the-pitch training. Eighteen juvenile football players (10 y ± 2 m) were enrolled. Each player performed a training drill with sport-specific movements (vertical jump, agility ladders, change of direction) and the Harre circuit test (HCT) to evaluate players’ motor coordination. Wearable inertial sensors (MTw Awinda, Xsens) were used to assess lower limb joint angles and accelerations. Based on the results of the HCT, players were divided into poorly coordinated (PC) and well-coordinated (WC) on the basis of the literature benchmark. The PC group showed a stiffer hip biomechanics strategy (up to 40% lower flexion angle, ES = 2.0) and higher internal-external hip rotation and knee valgus (p < 0.05). Significant biomechanical limb asymmetries were found only in the PC group for the knee joint (31–39% difference between dominant and non-dominant limb, ES 1.6–2.3). Poor motor coordination elicited altered hip and knee biomechanics during sport-specific dynamic movements. The monitoring of motor coordination and on-field biomechanics might enhance the targeted trainings for ACL injury prevention. Full article

(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing)

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