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Advanced Applications in Wearable Biosensors
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Advanced Applications in Wearable Biosensors

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 14349

Special Issue Editor

Dr. Daniel T.P. Fong

E-Mail Website1 Website2
Guest Editor
National Centre for Sport and Exercise Medicine, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK
Interests: ankle sprain; sports injury; orthopaedics; gait; sports medicine; sports biomechanics; clinical biomechanics; motion sensing; biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable sensors have been used in human motion analysis and monitoring since the 1990s. They are highly transportable and do not require stationary units like receivers and cameras and therefore can be used outside the traditional laboratories or clinics equipped with force plates, motion capture systems with multiple cameras, and a lot of other equipment. These devices allow the measurement and monitoring of human motion and a variety of biological and biomedical signals to be conducted anywhere and can further allow practical applications in training, telemedicine, and rehabilitation monitoring.

The common types of wearable sensors used in human movement analysis and biomedical signal monitoring are accelerometers, gyroscopes, inertial measurement units, micro-electromechanical systems (MEMS), optical sensors, electrodes, force or pressure sensors, foot switch, stretch sensors, temperature sensors, global position system (GPS) sensors, etc.

Some of these sensors can also harvest and store energy generated by human movement. These sensors allow the measurement, monitoring, and analysis of biological, biomedical, and biomechanical signals to quantify the kinematics of the movement, position of the users, muscle activity, reaction and fatigue, gait event identification, breathing rhythm monitoring, exercise intensity monitoring, concussion impact monitoring, as well as biological vital sign monitoring.

This Special Issue seeks papers related to the development and use of wearable sensors for human gait and motion analysis. We accept original, technical or review papers on (but not limited to) the following topics:

  • Concussion impact monitoring
  • Applications in musculoskeletal system
  • Biological vital signs monitoring
  • Sports biomechanics analysis
  • Applications in orthopaedics
  • Markerless motion analysis
  • Movement analysis in individual or team sports
  • Telemedicine
  • Muscle fatigue monitoring
  • Practical applications in training and rehabilitation
  • Validation against traditional motion capture system

You may choose our Joint Special Issue in Biosensors.

Dr. Daniel T.P. Fong
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.

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 sensor
  • Accelerometer
  • Gyroscope
  • Biomechanics
  • Gait
  • Kinematics
  • Motion analysis

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Published Papers (3 papers)

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Research

10 pages, 19249 KiB  
Article
Compression Garments Reduce Soft Tissue Vibrations and Muscle Activations during Drop Jumps: An Accelerometry Evaluation
by Liqin Deng, Yang Yang, Chenhao Yang, Ying Fang, Xini Zhang, Li Liu and Weijie Fu
Sensors 2021, 21(16), 5644; https://doi.org/10.3390/s21165644 - 21 Aug 2021
Cited by 7 | Viewed by 3981
Abstract
Objectives: To explore the effects of wearing compression garments on joint mechanics, soft tissue vibration and muscle activities during drop jumps. Methods: Twelve healthy male athletes were recruited to execute drop jumps from heights of 30, 45 and 60 cm whilst wearing compression [...] Read more.
Objectives: To explore the effects of wearing compression garments on joint mechanics, soft tissue vibration and muscle activities during drop jumps. Methods: Twelve healthy male athletes were recruited to execute drop jumps from heights of 30, 45 and 60 cm whilst wearing compression shorts (CS) and control shorts (CON). Sagittal plane kinematics, ground reaction forces, accelerations of the quadriceps femoris (QF), hamstrings (HM) and shoe heel-cup, and electromyography images of the rectus femoris (RF) and biceps femoris (BF) were collected. Results: Compared with wearing CON, wearing CS significantly reduced the QF peak acceleration at 45 and 60 cm and the HM peak acceleration at 30 cm. Wearing CS significantly increased the damping coefficient for QF and HM at 60 cm compared with wearing CON. Moreover, the peak transmissibility when wearing CS was significantly lower than that when wearing CON for all soft tissue compartments and heights, except for QF at 30 cm. Wearing CS reduced the RF activity during the pre-, post-, and eccentric activations for all heights and concentric activations at 45 cm; it also reduced the BF activity during post- and eccentric activations at 30 and 60 cm, respectively. The hip and knee joint moments and power or jump height were unaffected by the garment type. Conclusion: Applying external compression can reduce soft tissue vibrations without compromising neuromuscular performance during strenuous physical activities that involve exposure to impact-induced vibrations. Full article
(This article belongs to the Special Issue Advanced Applications in Wearable Biosensors)
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20 pages, 4945 KiB  
Article
Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting
by Huaiyu Zhu, Yisheng Zhao, Yun Pan, Hanshuang Xie, Fan Wu and Ruohong Huan
Sensors 2021, 21(16), 5290; https://doi.org/10.3390/s21165290 - 5 Aug 2021
Cited by 14 | Viewed by 3001
Abstract
Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated [...] Read more.
Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis. Full article
(This article belongs to the Special Issue Advanced Applications in Wearable Biosensors)
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17 pages, 4025 KiB  
Article
Gait Trajectory and Gait Phase Prediction Based on an LSTM Network
by Binbin Su and Elena M. Gutierrez-Farewik
Sensors 2020, 20(24), 7127; https://doi.org/10.3390/s20247127 - 12 Dec 2020
Cited by 64 | Viewed by 6603
Abstract
Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an [...] Read more.
Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTM network to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases. Full article
(This article belongs to the Special Issue Advanced Applications in Wearable Biosensors)
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