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Biomedical Sensors for Diagnosis and Rehabilitation

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 8613

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Special Issue Editor

Dr. Cecilia García Cena

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Guest Editor

Department of Electronics, Automation and Computer Science, Universidad Politécnica de Madrid, Madrid, Spain
Interests: bioinspired robotics; rehabilitation robots; dynamical control of robots; underwater robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to data provided by the World Health Organization, by 2030, 1 of 6 people in world will be aged 60 years or over. From a biological point of view, aging is a natural process where cellules become damaged over time, increasing the risk of suffering several diseases. After acute trauma, rehabilitation therapy aims to recover those lost capabilities and restore the autonomy of patients.

Cost-effective technologies that assist researchers and medical practitioners are both demanded and needed. Biosensors arise as key elements in the process of diagnosis, prognosis, and eventually rehabilitation therapies. The medical society demands safe and cheaper means of performing their work in order to guarantee the sustainability of the entire healthcare system.

In general, biosensors' applications are for screening infections and early detection, chronic disease treatment, health management, and well-being surveillance. Moreover, some of them play an important role in the rehabilitation process. Physiological monitoring via biosensors could help in both diagnosis and ongoing treatment of a huge number of individuals with neurological, cardiovascular, and pulmonary diseases, among others.

Prof. Dr. Cecilia Garcia
Guest Editor

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Keywords

physiological monitoring
biosensors
rehabilitation
electronics development
software development
communication
human–machine interaction

Related Special Issue

Biomedical Sensors for Diagnosis and Rehabilitation-2nd Edition in Sensors

Published Papers (7 papers)

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Research

14 pages, 14934 KiB

Open AccessArticle

Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases

by Leonardo Ariel Cano, Ana Lía Albarracín, Alvaro Gabriel Pizá, Cecilia Elisabet García-Cena, Eduardo Fernández-Jover and Fernando Daniel Farfán

Sensors 2024, 24(4), 1089; https://doi.org/10.3390/s24041089 - 07 Feb 2024

Viewed by 835

Abstract

Neurodegenerative diseases (NDs), such as Alzheimer’s, Parkinson’s, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor symptoms. However, when these diagnoses are made, the subjects are often in advanced stages where neuromuscular alterations are frequently irreversible. In this context, we propose a methodology to evaluate the cognitive workload (CWL) of motor tasks involving decision-making processes. CWL is a concept widely used to address the balance between task demand and the subject’s available resources to complete that task. In this study, multiple models for motor planning during a motor decision-making task were developed by recording EEG and EMG signals in

n = 17

healthy volunteers (9 males, 8 females, age

28.66 \pm 8.8

years). In the proposed test, volunteers have to make decisions about which hand should be moved based on the onset of a visual stimulus. We computed functional connectivity between the cortex and muscles, as well as among muscles using both corticomuscular and intermuscular coherence. Despite three models being generated, just one of them had strong performance. The results showed two types of motor decision-making processes depending on the hand to move. Moreover, the central processing of decision-making for the left hand movement can be accurately estimated using behavioral measures such as planning time combined with peripheral recordings like EMG signals. The models provided in this study could be considered as a methodological foundation to detect neuromuscular alterations in asymptomatic patients, as well as to monitor the process of a degenerative disease. Full article

(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation)

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15 pages, 1620 KiB

Open AccessArticle

Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning

by Fernando Villalba-Meneses, Cesar Guevara, Alejandro B. Lojan, Mario G. Gualsaqui, Isaac Arias-Serrano, Paolo A. Velásquez-López, Diego Almeida-Galárraga, Andrés Tirado-Espín, Javier Marín and José J. Marín

Sensors 2024, 24(3), 831; https://doi.org/10.3390/s24030831 - 27 Jan 2024

Viewed by 1281

Abstract

Low back pain (LBP) is a highly common musculoskeletal condition and the leading cause of work absenteeism. This project aims to develop a medical test to help healthcare professionals decide on and assign physical treatment for patients with nonspecific LBP. The design uses machine learning (ML) models based on the classification of motion capture (MoCap) data obtained from the range of motion (ROM) exercises among healthy and clinically diagnosed patients with LBP from Imbabura–Ecuador. The following seven ML algorithms were tested for evaluation and comparison: logistic regression, decision tree, random forest, support vector machine (SVM), k-nearest neighbor (KNN), multilayer perceptron (MLP), and gradient boosting algorithms. All ML techniques obtained an accuracy above 80%, and three models (SVM, random forest, and MLP) obtained an accuracy of >90%. SVM was found to be the best-performing algorithm. This article aims to improve the applicability of inertial MoCap in healthcare by making use of precise spatiotemporal measurements with a data-driven treatment approach to improve the quality of life of people with chronic LBP. Full article

(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation)

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12 pages, 5125 KiB

Open AccessArticle

Remote Heart Rate Estimation Based on Transformer with Multi-Skip Connection Decoder: Method and Evaluation in the Wild

by Walaa Othman, Alexey Kashevnik, Ammar Ali, Nikolay Shilov and Dmitry Ryumin

Sensors 2024, 24(3), 775; https://doi.org/10.3390/s24030775 - 25 Jan 2024

Cited by 1 | Viewed by 679

Abstract

Heart rate is an essential vital sign to evaluate human health. Remote heart monitoring using cheaply available devices has become a necessity in the twenty-first century to prevent any unfortunate situation caused by the hectic pace of life. In this paper, we propose a new method based on the transformer architecture with a multi-skip connection biLSTM decoder to estimate heart rate remotely from videos. Our method is based on the skin color variation caused by the change in blood volume in its surface. The presented heart rate estimation framework consists of three main steps: (1) the segmentation of the facial region of interest (ROI) based on the landmarks obtained by 3DDFA; (2) the extraction of the spatial and global features; and (3) the estimation of the heart rate value from the obtained features based on the proposed method. This paper investigates which feature extractor performs better by captioning the change in skin color related to the heart rate as well as the optimal number of frames needed to achieve better accuracy. Experiments were conducted using two publicly available datasets (LGI-PPGI and Vision for Vitals) and our own in-the-wild dataset (12 videos collected by four drivers). The experiments showed that our approach achieved better results than the previously published methods, making it the new state of the art on these datasets. Full article

(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation)

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15 pages, 3090 KiB

Open AccessArticle

Fluctuations in Upper and Lower Body Movement during Walking in Normal Pressure Hydrocephalus and Parkinson’s Disease Assessed by Motion Capture with a Smartphone Application, TDPT-GT

by Chifumi Iseki, Shou Suzuki, Tadanori Fukami, Shigeki Yamada, Tatsuya Hayasaka, Toshiyuki Kondo, Masayuki Hoshi, Shigeo Ueda, Yoshiyuki Kobayashi, Masatsune Ishikawa, Shigenori Kanno, Kyoko Suzuki, Yukihiko Aoyagi and Yasuyuki Ohta

Sensors 2023, 23(22), 9263; https://doi.org/10.3390/s23229263 - 18 Nov 2023

Viewed by 1195

Abstract

We aimed to capture the fluctuations in the dynamics of body positions and find the characteristics of them in patients with idiopathic normal pressure hydrocephalus (iNPH) and Parkinson’s disease (PD). With the motion-capture application (TDPT-GT) generating 30 Hz coordinates at 27 points on the body, walking in a circle 1 m in diameter was recorded for 23 of iNPH, 23 of PD, and 92 controls. For 128 frames of calculated distances from the navel to the other points, after the Fourier transforms, the slopes (the representatives of fractality) were obtained from the graph plotting the power spectral density against the frequency in log–log coordinates. Differences in the average slopes were tested by one-way ANOVA and multiple comparisons between every two groups. A decrease in the absolute slope value indicates a departure from the 1/f noise characteristic observed in healthy variations. Significant differences in the patient groups and controls were found in all body positions, where patients always showed smaller absolute values. Our system could measure the whole body’s movement and temporal variations during walking. The impaired fluctuations of body movement in the upper and lower body may contribute to gait and balance disorders in patients. Full article

(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation)

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17 pages, 645 KiB

Open AccessArticle

Automatic Video-Oculography System for Detection of Minimal Hepatic Encephalopathy Using Machine Learning Tools

by Alberto Calvo Córdoba, Cecilia E. García Cena and Carmina Montoliu

Sensors 2023, 23(19), 8073; https://doi.org/10.3390/s23198073 - 25 Sep 2023

Viewed by 897

Abstract

This article presents an automatic gaze-tracker system to assist in the detection of minimal hepatic encephalopathy by analyzing eye movements with machine learning tools. To record eye movements, we used video-oculography technology and developed automatic feature-extraction software as well as a machine learning [...] Read more.

n = 47

) of cirrhotic patients. Approximately half of them were diagnosed with minimal hepatic encephalopathy (MHE), a common neurological impairment in patients with liver disease. By using the actual gold standard, the Psychometric Hepatic Encephalopathy Score battery, PHES, patients were classified into two groups: cirrhotic patients with MHE and those without MHE. Eye movement tests were carried out on all participants. Using classical statistical concepts, we analyzed the significance of 150 eye movement features, and the most relevant (p-values ≤ 0.05) were selected for training machine learning algorithms. To summarize, while the PHES battery is a time-consuming exploration (between 25–40 min per patient), requiring expert training and not amenable to longitudinal analysis, the automatic video oculography is a simple test that takes between 7 and 10 min per patient and has a sensitivity and a specificity of 93%. Full article

(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation)

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17 pages, 3239 KiB

Open AccessArticle

Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices

by Weidong Xu, Jingke He, Weihua Li, Yi He, Haiyang Wan, Wu Qin and Zhuyun Chen

Sensors 2023, 23(18), 7874; https://doi.org/10.3390/s23187874 - 14 Sep 2023

Viewed by 1059

Abstract

To reduce the risks and challenges faced by frontline workers in confined workspaces, accurate real-time health monitoring of their vital signs is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers’ health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers’ health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches. Full article

(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation)

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16 pages, 2765 KiB

Open AccessArticle

Development and Earliest Validation of a Portable Device for Quantification of Hallux Extension Strength (QuHalEx)

by Elizabeth S. Hile, Mustafa Ghazi, Raghuveer Chandrashekhar, Josiah Rippetoe, Ashley Fox and Hongwu Wang

Sensors 2023, 23(10), 4654; https://doi.org/10.3390/s23104654 - 11 May 2023

Cited by 1 | Viewed by 1720

Abstract

Hallux strength is associated with sports performance and balance across the lifespan, and independently predicts falls in older adults. In rehabilitation, Medical Research Council (MRC) Manual Muscle Testing (MMT) is the clinical standard for hallux strength assessment, but subtle weakness and longitudinal changes in strength may go undetected. To address the need for research-grade yet clinically feasible options, we designed a new load cell device and testing protocol to Quantify Hallux Extension strength (QuHalEx). We aim to describe the device, protocol and initial validation. In benchtop testing, we used eight precision weights to apply known loads from 9.81 to 78.5 N. In healthy adults, we performed three maximal isometric tests for hallux extension and flexion on the right and left sides. We calculated the Intraclass Correlation Coefficient (ICC) with 95% confidence interval and descriptively compared our isometric force–time output to published parameters. QuHalEx benchtop absolute error ranged from 0.02 to 0.41 (mean 0.14) N. Benchtop and human intrasession output was repeatable (ICC 0.90–1.00, p < 0.001). Hallux strength in our sample (n = 38, age 33.5 ± 9.6 years, 53% female, 55% white) ranged from 23.1 to 82.0 N peak extension force and 32.0 to 142.4 N peak flexion, and differences of ~10 N (15%) between toes of the same MRC grade (5) suggest that QuHalEx is able to detect subtle weakness and interlimb asymmetries that are missed by MMT. Our results support ongoing QuHalEx validation and device refinement with a longer-term goal of widespread clinical and research application. Full article

(This article belongs to the Special Issue Biomedical Sensors for Diagnosis and Rehabilitation)

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