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Recent Trends of Wearable Sensors for Biomechanics Analysis and Physiological State Function Assessment

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 5120

Special Issue Editors

Dr. Alessandro Mengarelli

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy
Interests: biomechanics; movement analysis; advanced signal processing; biomechanical modelling and control; pattern recognition; timeseries analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Federica Verdini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
Interests: biomechanics; movement analysis; advanced signal processing; biomechanical modeling and control
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Tigrini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy
Interests: neuromuscolar control; signal processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the use of smart devices in research and clinical applications has dramatically increased. In particular, wearable sensors are an object of growing attention in the scientific community since they allow for long-term recording of a wide range of biological signals; in addition, they have a high degree of user acceptance. Moreover, wearable devices favour the development of measurement protocols and procedures focused on daily living activities, with current efforts being diverted towards the remote monitoring of physical activities, the detection of potentially dangerous domestic events, e.g. falling, and the development of remote care frameworks. On the other hand, wearable devices can have many different sensors and measurement technologies, with the possibility of recording a large set of biosignals related to the subject’s movement and physiological functions. However, the spread of this technology and the increasing number of potentially available raw data require novel processing and mining techniques for extracting reliable information necessary for the different fields where wearable sensors and smart devices find their applications, which range from biomechanical analysis and physiological state assessment to rehabilitative technologies. This Special Issue aims to collect articles dealing with novel applications of wearable sensors and advances in signals acquisition and processing for fields related to human movement and physical monitoring.

Dr. Alessandro Mengarelli
Dr. Federica Verdini
Dr. Andrea Tigrini
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

  • wearable sensors
  • biosignals
  • long-term monitoring
  • activities of daily living
  • inertial sensing
  • data processing
  • signal processing
  • pattern recognition

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Published Papers (3 papers)

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Research

15 pages, 2040 KiB  
Article
Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study
by Yang Zhao, Lisha Yu, Xiaomao Fan, Marco Y. C. Pang, Kwok-Leung Tsui and Hailiang Wang
Sensors 2023, 23(18), 8008; https://doi.org/10.3390/s23188008 - 21 Sep 2023
Cited by 2 | Viewed by 1657
Abstract
Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. [...] Read more.
Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users’ multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable. Full article
(This article belongs to the Special Issue Recent Trends of Wearable Sensors for Biomechanics Analysis and Physiological State Function Assessment)
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27 pages, 6369 KiB  
Article
Drivers’ Mental Engagement Analysis Using Multi-Sensor Fusion Approaches Based on Deep Convolutional Neural Networks
by Taraneh Aminosharieh Najafi, Antonio Affanni, Roberto Rinaldo and Pamela Zontone
Sensors 2023, 23(17), 7346; https://doi.org/10.3390/s23177346 - 23 Aug 2023
Cited by 2 | Viewed by 1247
Abstract
In this paper, we present a comprehensive assessment of individuals’ mental engagement states during manual and autonomous driving scenarios using a driving simulator. Our study employed two sensor fusion approaches, combining the data and features of multimodal signals. Participants in our experiment were [...] Read more.
In this paper, we present a comprehensive assessment of individuals’ mental engagement states during manual and autonomous driving scenarios using a driving simulator. Our study employed two sensor fusion approaches, combining the data and features of multimodal signals. Participants in our experiment were equipped with Electroencephalogram (EEG), Skin Potential Response (SPR), and Electrocardiogram (ECG) sensors, allowing us to collect their corresponding physiological signals. To facilitate the real-time recording and synchronization of these signals, we developed a custom-designed Graphical User Interface (GUI). The recorded signals were pre-processed to eliminate noise and artifacts. Subsequently, the cleaned data were segmented into 3 s windows and labeled according to the drivers’ high or low mental engagement states during manual and autonomous driving. To implement sensor fusion approaches, we utilized two different architectures based on deep Convolutional Neural Networks (ConvNets), specifically utilizing the Braindecode Deep4 ConvNet model. The first architecture consisted of four convolutional layers followed by a dense layer. This model processed the synchronized experimental data as a 2D array input. We also proposed a novel second architecture comprising three branches of the same ConvNet model, each with four convolutional layers, followed by a concatenation layer for integrating the ConvNet branches, and finally, two dense layers. This model received the experimental data from each sensor as a separate 2D array input for each ConvNet branch. Both architectures were evaluated using a Leave-One-Subject-Out (LOSO) cross-validation approach. For both cases, we compared the results obtained when using only EEG signals with the results obtained by adding SPR and ECG signals. In particular, the second fusion approach, using all sensor signals, achieved the highest accuracy score, reaching 82.0%. This outcome demonstrates that our proposed architecture, particularly when integrating EEG, SPR, and ECG signals at the feature level, can effectively discern the mental engagement of drivers. Full article
(This article belongs to the Special Issue Recent Trends of Wearable Sensors for Biomechanics Analysis and Physiological State Function Assessment)
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13 pages, 4577 KiB  
Article
Does Smartphone Use Affect a Subsequent Swimming Training Session? Preliminary Results in Amateur Triathletes
by Claudio Quagliarotti, Vittorio Coloretti, Emanuele Dello Stritto, Sarah Cuccurullo, Jessica Acalai, Romuald Lepers, Silvia Fantozzi, Matteo Cortesi and Maria Francesca Piacentini
Sensors 2023, 23(13), 5837; https://doi.org/10.3390/s23135837 - 23 Jun 2023
Cited by 1 | Viewed by 1506
Abstract
To date, the literature has failed to individuate a clear motivation for the performance decrement after a mental fatigue-inducing task. This study aimed to evaluate biomechanical and perceptual variables during a swimming training session in different mental fatigue states. Seven amateur triathletes watched [...] Read more.
To date, the literature has failed to individuate a clear motivation for the performance decrement after a mental fatigue-inducing task. This study aimed to evaluate biomechanical and perceptual variables during a swimming training session in different mental fatigue states. Seven amateur triathletes watched a documentary, utilized a smartphone, or performed an AX-CPT for 45 min randomly on three different days. After, they performed a 15-min warm-up followed by 6 × 200 m at constant pre-set speed plus one 200 m at maximal effort. The mental fatigue status was assessed by the visual analog scale (VAS) and short-Stroop task results before, post-mental task, and post-swimming session. The biomechanical and motor coordination variables during swimming were assessed using five IMU sensors and video analysis. The heart rate and rate of perceived exertion were monitored during the task. No differences in biomechanical and perceptual variables were found between and within conditions. Higher mental fatigue was found only in the AX-CPT condition at post task by VAS. In this preliminary study, no changes in swimming biomechanics were highlighted by mental fatigue, but the warm-up performed may have counteracted its negative effects. Further studies are recommended. Full article
(This article belongs to the Special Issue Recent Trends of Wearable Sensors for Biomechanics Analysis and Physiological State Function Assessment)
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