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Human Signal Processing Based on Wearable Non-Invasive Device: 2nd Edition
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Human Signal Processing Based on Wearable Non-Invasive Device: 2nd Edition

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 6640

Special Issue Editors

Prof. Dr. Wing-Kuen Ling

E-Mail Website
Guest Editor
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: biomedical signal denoising; machine learning with applications in biomedical signal classification and regression; nonlinear dynamics with applications in EEG and ECG modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Steve Ling

E-Mail Website
Guest Editor
Department of Electrical and Data Engineering, University of Technology Sydney, Sydney 00099F, Australia
Interests: neural networks; medical imaging; BCI applications; non-invasive bioinstrumentation
Special Issues, Collections and Topics in MDPI journals
Dr. Bonnie Law

E-Mail Website
Guest Editor
Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: source camera identification; image forensics; biomedical image/signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The human body creates many different types of signals, which can be recorded in the form of photoplethysmograms, electrocardiograms, electromyograms, electroencephalograms and electrooculograms. These human signals play an important role in the diagnosis of disease. However, the workloads of medical personnel for interpreting these signals are colossal. In order to address this issue, automatic human signal processing is required. To process these human signals, signal denoising, feature extraction and classification or regression are usually required. To perform denoising, time frequency analysis approaches such as wavelet transform approaches, empirical mode decomposition approaches and singular spectrum analysis approaches are employed. To perform feature extraction, statistical approaches are employed. To perform classification or regression, neural networks or tree-based systems are employed. This Special Issue mainly focuses on proposing new methods for carrying out human signal processing and exploring new applications using human signal processing techniques.

Prof. Dr. Wing-Kuen Ling
Dr. Steve Ling
Dr. Bonnie Law
Guest Editors

Manuscript Submission Information

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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

  • photoplethysmograms
  • electrocardiograms
  • electromyograms
  • electroencephalograms
  • electrooculograms
  • denoising
  • feature extraction
  • classification
  • regression

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Related Special Issue

Published Papers (5 papers)

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Research

16 pages, 3813 KB  
Article
Usability Evaluation and Perceived Performance of the MoonWalking® Insole in Safety Footwear
by Pedro Castro-Martins, Arcelina Marques, Luís Pinto-Coelho and Mário Vaz
Sensors 2026, 26(9), 2668; https://doi.org/10.3390/s26092668 - 25 Apr 2026
Viewed by 765
Abstract
Prolonged standing and repetitive lifting are routine occupational stressors that elevate plantar pressures across workers. In those with diabetes, these demands represent additional risk factors for diabetic foot pathology, highlighting the need for ergonomic interventions beyond standard safety footwear. This study evaluated the [...] Read more.
Prolonged standing and repetitive lifting are routine occupational stressors that elevate plantar pressures across workers. In those with diabetes, these demands represent additional risk factors for diabetic foot pathology, highlighting the need for ergonomic interventions beyond standard safety footwear. This study evaluated the perceived ergonomic performance of the MoonWalking® insole, a novel adaptive pneumatic system designed for real-time pressure stabilization and offloading when integrated into safety footwear. A comparative experimental protocol tested two conditions: safety footwear with the manufacturer’s original insole and the same footwear with the MoonWalking prototype. Twenty participants assessed perceived comfort using a VAS and binary ergonomic questionnaires. The results showed statistically significant improvements in perceived cushioning, foot fit, and overall comfort when using the MoonWalking insole. Participants consistently identified pressure-stabilizing and offloading functions across all plantar regions, indicating that adaptive pressure control was clearly perceptible. No pain or movement restrictions were reported. Although perceived fatigue did not reach statistical significance, a decreasing trend was observed. A slight reduction in intention to reuse the footwear occurred with the prototype, possibly due to its increased weight. These findings provide evidence that integrating an adaptive pneumatic insole into safety footwear may improve plantar pressure redistribution and user comfort. Full article
(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-Invasive Device: 2nd Edition)
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16 pages, 1348 KB  
Article
Kinematic Parameters of Normal Hand-to-Mouth Movement in Pediatric Populations: Adaptation of the “Rab Hand-to-Mouth Protocol”
by Álvaro Pérez-Somarriba Moreno, Rosa María Ortiz-Gutiérrez, Patricia Martín-Casas, Iñigo Monzón Tobalina, Paula Arias Martínez, Ignacio Martínez Caballero, Angélica Guerrero-Blázquez and María José Díaz-Arribas
Sensors 2026, 26(9), 2625; https://doi.org/10.3390/s26092625 - 23 Apr 2026
Viewed by 725
Abstract
Optoelectronic motion capture systems provide objective and high-resolution measurements of upper limb kinematics. The hand-to-mouth movement is closely related to motor development in children. The “Rab Hand-to-Mouth protocol” (BTS Bioengineering) is widely used; however, its seated configuration constrains elbow posture and may limit [...] Read more.
Optoelectronic motion capture systems provide objective and high-resolution measurements of upper limb kinematics. The hand-to-mouth movement is closely related to motor development in children. The “Rab Hand-to-Mouth protocol” (BTS Bioengineering) is widely used; however, its seated configuration constrains elbow posture and may limit the ecological validity of the movement. In this study, we propose a methodological adaptation of the protocol in a standing position to allow a more physiological elbow configuration and to increase the dynamic range of elbow and shoulder motion. The objective was to characterize kinematic patterns of the hand-to-mouth movement in typically developing children aged 4 to 9 years using this adapted setup. This study was designed as a descriptive analysis and does not aim to provide formal validation of the standing protocol against the original seated configuration. An observational study that included 40 children was conducted. Motion data were acquired using eight optoelectronic cameras (sampling frequency: 250 Hz) and 17 reflective markers placed on the trunk and upper limbs. Kinematic patterns and spatiotemporal parameters were computed using dedicated motion analysis software. No significant differences were observed between dominant and non-dominant limbs in spatiotemporal parameters, whereas kinematic differences were minimal and limited to trunk rotation, as identified by Statistical Parametric Mapping (SPM). Some isolated statistically significant associations with age were identified in specific spatiotemporal variables; however, these variables showed low coefficients of determination (R2), indicating limited explanatory power of age. Overall, kinematic parameters did not exhibit consistent age-related patterns. These findings provide preliminary descriptive data for hand-to-mouth kinematics in a standing condition, which may contribute to the future development of assessment protocols. However, the limited sample size and the absence of pathological populations restrict the direct generalization of these findings. Future studies should evaluate the applicability of this approach in clinical cohorts and explore its integration into sensor-based and data-driven models for movement analysis. Full article
(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-Invasive Device: 2nd Edition)
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16 pages, 3335 KB  
Article
A Robust mmWave Radar Framework for Accurate People Counting and Motion Classification
by Nuobei Zhang, Haoxuan Li, Adnan Zahid, Yue Tian and Wenda Li
Sensors 2026, 26(4), 1289; https://doi.org/10.3390/s26041289 - 16 Feb 2026
Viewed by 1401
Abstract
People counting and occupancy monitoring play a vital role in applications such as intelligent building management, safety control, and resource optimization in future smart cities. Conventional camera and infrared-based methods often suffer from privacy risks, lighting dependency, and limited robustness in complex indoor [...] Read more.
People counting and occupancy monitoring play a vital role in applications such as intelligent building management, safety control, and resource optimization in future smart cities. Conventional camera and infrared-based methods often suffer from privacy risks, lighting dependency, and limited robustness in complex indoor environments. In this paper, we present a 60 GHz millimeter-wave (mmWave) radar-based occupancy monitoring system that enables accurate and privacy-preserving people counting. The proposed system leverages echo signals processed through Doppler and range spectrogram and analyzed by an enhanced ResNet-50 deep learning model to classify motion states and count individuals. Experimental results collected in a typical indoor environment demonstrate that the system achieves 95.45% accuracy across 6 classes of movements and 98.86% accuracy for people counting (0–3 persons). The method also shows strong adaptability under limited data and robustness to Gaussian blur interference, providing an efficient and reliable solution for intelligent indoor occupancy monitoring. Full article
(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-Invasive Device: 2nd Edition)
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16 pages, 1611 KB  
Article
An Explainable Machine Learning Approach to Explain the Effects of Training and Match Load on Ultra-Short-Term Heart Rate Variability in Semi-Professional Basketball Players
by Jorge Abruñedo-Lombardero, Alexis Padrón-Cabo, Daniel Vélez-Serrano, Alejandro Álvaro-Meca and Eliseo Iglesias-Soler
Sensors 2025, 25(22), 6928; https://doi.org/10.3390/s25226928 - 13 Nov 2025
Viewed by 1919
Abstract
Understanding how training and match load influence autonomic recovery is essential for optimizing athlete monitoring. This proof-of-concept study aimed to examine the impact of training and match load on next-day heart rate variability (HRV) and to explain how different load measures influenced the [...] Read more.
Understanding how training and match load influence autonomic recovery is essential for optimizing athlete monitoring. This proof-of-concept study aimed to examine the impact of training and match load on next-day heart rate variability (HRV) and to explain how different load measures influenced the internal response, using SHapley Additive Explanations (SHAP) to interpret machine learning models. Five semi-professional basketball players (23 ± 5 years; 191 ± 7 cm; 90 ± 11 kg) were monitored throughout a competitive season. HRV and load metrics were recorded daily. Differences in the natural logarithm of the root mean square of successive differences (LnRMSSD) across Non-Training, Training, and Match days were analyzed using linear mixed models. Additionally, a Gradient Boosting Machine model was developed to examine next-day HRV responses, with SHAP analysis providing both global and individual insights into feature importance. Next-morning LnRMSSD values were significantly lower on Match days compared to both Training and Non-Training days (p < 0.001). SHAP results identified rate of perceived exertion (RPE), days since last match, minutes played, and recent training load as the most influential variables associated with HRV changes. Pre-session heart rate and the root mean square of successive differences (RMSSD) values also demonstrated notable individual relevance. The ranking and magnitude of influential variables varied across players, highlighting the heterogeneity of physiological responses in team sports. While these findings are specific to this cohort, they illustrate the potential of explainable machine learning to enhance transparency and support individualized monitoring strategies. Importantly, they underscore the value of integrating both subjective and objective load measures to inform training decisions. Future research involving larger, multi-team samples is needed to validate the generalizability of these results. Full article
(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-Invasive Device: 2nd Edition)
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11 pages, 420 KB  
Article
Sensor-Agnostic, LSTM-Based Human Motion Prediction Using sEMG Data
by Bon Ho Koo, Ho Chit Siu and Lonnie G. Petersen
Sensors 2025, 25(17), 5474; https://doi.org/10.3390/s25175474 - 3 Sep 2025
Cited by 1 | Viewed by 1256
Abstract
The use of surface electromyography (sEMG) for conventional motion classification and prediction has had limitations due to sensor hardware differences. With the popularization of deep learning-based approaches to the application of motion prediction, this study explores the effects that different hardware sensor platforms [...] Read more.
The use of surface electromyography (sEMG) for conventional motion classification and prediction has had limitations due to sensor hardware differences. With the popularization of deep learning-based approaches to the application of motion prediction, this study explores the effects that different hardware sensor platforms have on the performance of a deep learning neural network trained to predict the one-degree-of-freedom (DoF) angular trajectory of a human. Two different sEMG sensor platforms were used to collect raw data from subjects conducting exercises, which was used to train a neural network designed to predict the future angular trajectory of the arm. The results show that the raw data originating from different sensor hardware with different configurations (including the communication method, data acquisition unit (DAQ) usage, electrode configuration, buffering method, preprocessing method, and experimental variables like the sampling frequency) produced bi-LSTM networks that performed similarly. This points to the hardware-agnostic nature of such deep learning networks. Full article
(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-Invasive Device: 2nd Edition)
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