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Wearable Sensors for Physical Activity and Healthcare Monitoring

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

Deadline for manuscript submissions: 30 August 2024 | Viewed by 16411

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Dr. Russell Reid

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Guest Editor

Department of Engineering, Utah Tech University, St. George, UT 84770, USA
Interests: energy harvesting; wearable sensors; self-powered biosensors

Dr. Ifana Mahbub

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Guest Editor

Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
Interests: low-power CMOS analog and RF integrated circuit design; antennas and wireless interfaces for biomedical sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable sensors have bridged the gap from laboratory novelties to successful commercial products. Ongoing research continues to expand their capabilities, making them smaller, more physically robust, less invasive, inexpensive, and capable of detecting and treating a wider range of analytes and physical inputs. It is not a stretch to say that they have greatly impacted and will continue to transform various aspects of modern life such as healthcare and human performance analysis.

This Special Issue brings together current wearable sensor research and review articles in a cohesive way, which is not an easy task given how extensive the field has become. While it is not possible to cover every aspect of this growing research area in a single issue, our topics of interest are nevertheless somewhat broad. We will accept full-length research articles and reviews related to the following (non-exclusive) list of topics in relation to the title of the Special Issue:

Flexible/stretchable functional sensor electrodes;
Complete wearable sensor systems;
Self-powered sensors;
Flexible/stretchable electronics;
Wireless transmission;
Power conditioning circuits;
Powering wearable sensors;
Smart wearable sensors;
Wearable sensor fabrication using rapid and/or highly scalable processes.

Dr. Russell Reid
Dr. Ifana Mahbub
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.

Published Papers (12 papers)

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Research

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15 pages, 5309 KiB

Open AccessArticle

Ultra-Long-Term-EEG Monitoring (ULTEEM) Systems: Towards User-Friendly Out-of-Hospital Recordings of Electrical Brain Signals in Epilepsy

by Gürkan Yilmaz, Andrea Seiler, Olivier Chételat and Kaspar A. Schindler

Sensors 2024, 24(6), 1867; https://doi.org/10.3390/s24061867 - 14 Mar 2024

Viewed by 669

Abstract

Epilepsy is characterized by the occurrence of epileptic events, ranging from brief bursts of interictal epileptiform brain activity to their most dramatic manifestation as clinically overt bilateral tonic–clonic seizures. Epileptic events are often modulated in a patient-specific way, for example by sleep. But they also reveal temporal patterns not only on ultra- and circadian, but also on multidien scales. Thus, to accurately track the dynamics of epilepsy and to thereby enable and improve personalized diagnostics and therapies, user-friendly systems for long-term out-of-hospital recordings of electrical brain signals are needed. Here, we present two wearable devices, namely ULTEEM and ULTEEMNite, to address this unmet need. We demonstrate how the usability concerns of the patients and the signal quality requirements of the clinicians have been incorporated in the design. Upon testbench verification of the devices, ULTEEM was successfully benchmarked against a reference EEG device in a pilot clinical study. ULTEEMNite was shown to record typical macro- and micro-sleep EEG characteristics in a proof-of-concept study. We conclude by discussing how these devices can be further improved and become particularly useful for a better understanding of the relationships between sleep, epilepsy, and neurodegeneration. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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10 pages, 989 KiB

Open AccessArticle

Validity of the CALERA Research Sensor to Assess Body Core Temperature during Maximum Exercise in Patients with Heart Failure

by Antonia Kaltsatou, Maria Anifanti, Andreas D. Flouris, Georgia Xiromerisiou and Evangelia Kouidi

Sensors 2024, 24(3), 807; https://doi.org/10.3390/s24030807 - 26 Jan 2024

Viewed by 863

Abstract

(1) Background: It is important to monitor the body core temperature (Tc) of individuals with chronic heart failure (CHF) during rest or exercise, as they are susceptible to complications. Gastrointestinal capsules are a robust indicator of the Tc at rest and during exercise. A practical and non-invasive sensor called CALERA Research was recently introduced, promising accuracy, sensitivity, continuous real-time analysis, repeatability, and reproducibility. This study aimed to assess the validity of the CALERA Research sensor when monitoring patients with CHF during periods of rest, throughout brief cardiopulmonary exercise testing, and during their subsequent recovery. (2) Methods: Twelve male CHF patients volunteered to participate in a 70-min protocol in a laboratory at 28 °C and 39% relative humidity. After remaining calm for 20 min, they underwent a symptom-limited stress test combined with ergospirometry on a treadmill, followed by 40 min of seated recovery. The Tc was continuously monitored by both Tc devices. (3) Results: The Tc values from the CALERA Research sensor and the gastrointestinal sensor showed no associations at rest (r = 0.056, p = 0.154) and during exercise (r = −0.015, p = 0.829) and a weak association during recovery (r = 0.292, p < 0.001). The Cohen’s effect size of the differences between the two Tc assessment methods for rest, exercise, and recovery was 1.04 (large), 0.18 (none), and 0.45 (small), respectively. The 95% limit of agreement for the CALERA Research sensor was −0.057 ± 1.03 °C. (4) Conclusions: The CALERA sensor is a practical and, potentially, promising device, but it does not provide an accurate Tc estimation in CHF patients at rest, during brief exercise testing, and during recovery. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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23 pages, 4722 KiB

Open AccessArticle

Investigating Activity Recognition for Hemiparetic Stroke Patients Using Wearable Sensors: A Deep Learning Approach with Data Augmentation

by Youngmin Oh, Sol-A Choi, Yumi Shin, Yeonwoo Jeong, Jongkuk Lim and Sujin Kim

Sensors 2024, 24(1), 210; https://doi.org/10.3390/s24010210 - 29 Dec 2023

Cited by 1 | Viewed by 847

Abstract

Measuring the daily use of an affected limb after hospital discharge is crucial for hemiparetic stroke rehabilitation. Classifying movements using non-intrusive wearable sensors provides context for arm use and is essential for the development of a home rehabilitation system. However, the movement classification of stroke patients poses unique challenges, including variability and sparsity. To address these challenges, we collected movement data from 15 hemiparetic stroke patients (Stroke group) and 29 non-disabled individuals (ND group). The participants performed two different tasks, the range of motion (14 movements) task and the activities of daily living (56 movements) task, wearing five inertial measurement units in a home setting. We trained a 1D convolutional neural network and evaluated its performance for different training groups: ND-only, Stroke-only, and ND and Stroke jointly. We further compared the model performance with data augmentation from axis rotation and investigated how the performance varied based on the asymmetry of movements. The joint training of ND + Stroke yielded an increased F1-score by a margin of 31.6% and 10.6% compared to ND-only training and Stroke-only training, respectively. Data augmentation further enhanced F1-scores across all conditions by an average of 11.3%. Finally, asymmetric movements decreased the F1-score by 25.9% compared to symmetric movements in the Stroke group, indicating the importance of asymmetry in movement classification. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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28 pages, 5263 KiB

Open AccessArticle

IoT-Based Solution for Detecting and Monitoring Upper Crossed Syndrome

by Ammar Shaheen, Hisham Kazim, Mazen Eltawil and Raafat Aburukba

Sensors 2024, 24(1), 135; https://doi.org/10.3390/s24010135 - 26 Dec 2023

Viewed by 969

Abstract

A sedentary lifestyle has caused adults to spend more than 6 h seated, which has led to inactivity and spinal issues. This context underscores the growing sedentary behavior, exemplified by extended sitting hours among adults and university students. Such inactivity triggers various health problems and spinal disorders, notably Upper Crossed Syndrome (UCS) and its association with thoracic kyphosis, which can cause severe spinal curvature and related complications. Traditional detection involves clinical assessments and corrective exercises; however, this work proposes a multi-layered system for a back brace to detect, monitor, and potentially prevent the main signs of UCS. Building and using a framework that detects and monitors signs of UCS has facilitated patient–doctor interaction, automated the detection process for improved patient–physician coordination, and helped improve patients’ spines over time. The smart wearable brace includes inertial measurement unit (IMU) sensors targeting hunched-back postures. The IMU sensors capture postural readings, which are then used for classification. Multiple classifiers were used where the long short-term memory (LSTM) model had the highest accuracy of 99.3%. Using the classifier helped detect and monitor UCS over time. Integrating the wearable device with a mobile interface enables real-time data visualization and immediate feedback for users to correct and mitigate UCS-related issues. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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16 pages, 1727 KiB

Open AccessArticle

Assessment of an IMU-Based Experimental Set-Up for Upper Limb Motion in Obese Subjects

by Serena Cerfoglio, Nicola Francesco Lopomo, Paolo Capodaglio, Emilia Scalona, Riccardo Monfrini, Federica Verme, Manuela Galli and Veronica Cimolin

Sensors 2023, 23(22), 9264; https://doi.org/10.3390/s23229264 - 18 Nov 2023

Viewed by 919

Abstract

In recent years, wearable systems based on inertial sensors opened new perspectives for functional motor assessment with respect to the gold standard motion capture systems. The aim of this study was to validate an experimental set-up based on 17 body-worn inertial sensors (Awinda, Xsens, The Netherlands), addressing specific body segments with respect to the state-of-the art system (VICON, Oxford Metrics Ltd., Oxford, UK) to assess upper limb kinematics in obese, with respect to healthy subjects. Twenty-three obese and thirty healthy weight individuals were simultaneously acquainted with the two systems across a set of three tasks for upper limbs (i.e., frontal arm rise, lateral arm rise, and reaching). Root Mean Square error (RMSE) was computed to quantify the differences between the measurements provided by the systems in terms of range of motion (ROM), whilst their agreement was assessed via Pearson’s correlation coefficient (PCC) and Bland–Altman (BA) plots. In addition, the signal waveforms were compared via one-dimensional statistical parametrical mapping (SPM) based on a paired t-test and a two-way ANOVA was applied on ROMs. The overall results partially confirmed the correlation and the agreement between the two systems, reporting only a moderate correlation for shoulder principal rotation angle in each task (r~0.40) and for elbow/flexion extension in obese subjects (r = 0.66), whilst no correlation was found for most non-principal rotation angles (r < 0.40). Across the performed tasks, an average RMSE of 34° and 26° was reported in obese and healthy controls, respectively. At the current state, the presence of bias limits the applicability of the inertial-based system in clinics; further research is intended in this context. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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14 pages, 875 KiB

Open AccessArticle

Real-Time Sensor-Embedded Neural Network for Human Activity Recognition

by Ali Shakerian, Victor Douet, Amirhossein Shoaraye Nejati and René Landry, Jr.

Sensors 2023, 23(19), 8127; https://doi.org/10.3390/s23198127 - 28 Sep 2023

Cited by 2 | Viewed by 1151

Abstract

This article introduces a novel approach to human activity recognition (HAR) by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is affixed to the subject’s chest to capture their movements. Through the implementation of a convolutional neural network (CNN) on the microcontroller, the sensor is capable of detecting and predicting the wearer’s activities in real-time, eliminating the need for external processing devices. The article provides a comprehensive description of the sensor and the methodology employed to achieve real-time prediction of subject behaviors. Experimental results demonstrate the accuracy and high inference performance of the proposed solution for real-time embedded activity recognition. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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17 pages, 5845 KiB

Open AccessArticle

Counting Finger and Wrist Movements Using Only a Wrist-Worn, Inertial Measurement Unit: Toward Practical Wearable Sensing for Hand-Related Healthcare Applications

by Shusuke Okita, Roman Yakunin, Jathin Korrapati, Mina Ibrahim, Diogo Schwerz de Lucena, Vicky Chan and David J. Reinkensmeyer

Sensors 2023, 23(12), 5690; https://doi.org/10.3390/s23125690 - 18 Jun 2023

Cited by 2 | Viewed by 1962

Abstract

The ability to count finger and wrist movements throughout the day with a nonobtrusive, wearable sensor could be useful for hand-related healthcare applications, including rehabilitation after a stroke, carpal tunnel syndrome, or hand surgery. Previous approaches have required the user to wear a ring with an embedded magnet or inertial measurement unit (IMU). Here, we demonstrate that it is possible to identify the occurrence of finger and wrist flexion/extension movements based on vibrations detected by a wrist-worn IMU. We developed an approach we call “Hand Activity Recognition through using a Convolutional neural network with Spectrograms” (HARCS) that trains a CNN based on the velocity/acceleration spectrograms that finger/wrist movements create. We validated HARCS with the wrist-worn IMU recordings obtained from twenty stroke survivors during their daily life, where the occurrence of finger/wrist movements was labeled using a previously validated algorithm called HAND using magnetic sensing. The daily number of finger/wrist movements identified by HARCS had a strong positive correlation to the daily number identified by HAND (R² = 0.76, p < 0.001). HARCS was also 75% accurate when we labeled the finger/wrist movements performed by unimpaired participants using optical motion capture. Overall, the ringless sensing of finger/wrist movement occurrence is feasible, although real-world applications may require further accuracy improvements. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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14 pages, 7867 KiB

Open AccessArticle

Inductive Coupling of Bipolar Signals with a Conjugate Coil Pair for an Analog Passive ECG Sensor Using a PPy-Coated pvCNT Dry Electrodes

by Mohammad Abu-Saude and Bashir I. Morshed

Sensors 2023, 23(11), 5283; https://doi.org/10.3390/s23115283 - 02 Jun 2023

Viewed by 1083

Abstract

The wireless capture of analog differential signals from fully passive (battery-less) sensors is technically challenging but it can allow for the seamless capture of differential biosignals such as an electrocardiogram (ECG). This paper presents a novel design for the wireless capture of analog differential signals using a novel conjugate coil pair for a wireless resistive analog passive (WRAP) ECG sensor. Furthermore, we integrate this sensor with a new type of dry electrode, namely conductive polymer polypyrrole (PPy)-coated patterned vertical carbon nanotube (pvCNT) electrodes. The proposed circuit uses dual-gate depletion-mode MOSFETs to convert the differential biopotential signals to correlated drain-source resistance changes and the conjugate coil wirelessly transmits the differences of the two input signals. The circuit rejects (17.24 dB) common mode signals and passing only differential signals. We have integrated this novel design with our previously reported PPy-coated pvCNT dry ECG electrodes, fabricated on a stainless steel substrate with a diameter of 10 mm, which provided a zero-power (battery-less) ECG capture system for long duration monitoring. The scanner transmits an RF carrier signal at 8.37 MHz. The proposed ECG WRAP sensor uses only two complementary biopotential amplifier circuits, each of which has a single-depletion MOSFET. The amplitude-modulated RF signal is envelope-detected, filtered, amplified, and transmitted to a computer for signal processing. ECG signals are collected using this WRAP sensor and compared with a commercial counterpart. Due to the battery-less nature of the ECG WRAP sensor, it has the potential to be a body-worn electronic circuit patch with dry pvCNT electrodes that stably operate for a long period of time. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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14 pages, 2361 KiB

Open AccessArticle

A Low-Power Wireless System for Predicting Early Signs of Sudden Cardiac Arrest Incorporating an Optimized CNN Model Implemented on NVIDIA Jetson

by Venkata Deepa Kota, Himanshu Sharma, Mark V. Albert, Ifana Mahbub, Gayatri Mehta and Kamesh Namuduri

Sensors 2023, 23(4), 2270; https://doi.org/10.3390/s23042270 - 17 Feb 2023

Cited by 1 | Viewed by 1680

Abstract

The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA’s characteristics will be key to developing preventive strategies. Many lives could be saved if SCA’s early onset could be detected or predicted. Monitoring heart signals continuously is essential for diagnosing sporadic cardiac dysfunction. An electrocardiogram (ECG) can be used to continuously monitor heart function without having to go to the hospital. A zeolite-based dry electrode can provide safe on-skin ECG acquisition while the subject is out-of-hospital and facilitate long-term monitoring. To the ECG signal, a low-power 1 μW read-out circuit was designed and implemented in our prior work. However, having long-term ECG monitoring outside the hospital, i.e., high battery life, and low power consumption while transmission and reception of ECG signal are crucial. This paper proposes a prototype with a 10-bit resolution ADC and nRF24L01 transceivers placed 5 m apart. The system uses the 2.4 GHz worldwide ISM frequency band with GFSK modulation to wirelessly transmit digitized ECG bits at 250 kbps data rate to a physician’s computer (or similar) for continuous monitoring of ECG signals; the power consumption is only 11.2 mW and 4.62 mW during transmission and reception, respectively, with a low bit error rate of ≤0.1%. Additionally, a subject-wise cross-validated, three-fold, optimized convolutional neural network (CNN) model using the Physionet-SCA dataset was implemented on NVIDIA Jetson to identify the irregular heartbeats yielding an accuracy of 89% with a run time of 5.31 s. Normal beat classification has an F1 score of 0.94 and a ROC score of 0.886. Thus, this paper integrates the ECG acquisition and processing unit with low-power wireless transmission and CNN model to detect irregular heartbeats. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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15 pages, 555 KiB

Open AccessArticle

Model of the Performance Based on Artificial Intelligence–Fuzzy Logic Description of Physical Activity

by Adam Szulc, Piotr Prokopowicz, Krzysztof Buśko and Dariusz Mikołajewski

Sensors 2023, 23(3), 1117; https://doi.org/10.3390/s23031117 - 18 Jan 2023

Viewed by 1344

Abstract

The aim of the study was to build a fuzzy model of lower limb peak torque in an isokinetic mode. The study involved 93 male participants (28 male deaf soccer players, 19 hearing soccer players and 46 deaf untraining male). A fuzzy computational model of different levels of physical activity with a focus on the lower limbs was constructed. The proposed fuzzy model assessing lower limb peak torque in an isokinetic mode demonstrated its effectiveness. The novelty of our research lies in the use of hierarchical fuzzy logic to extract computational rules from data provided explicitly and then to determine the corresponding physiological and pathological mechanisms. The contribution of our research lies in complementing the methods for describing physiology, pathology and rehabilitation with fuzzy parameters, including the so-called dynamic norm embedded in the model. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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18 pages, 4446 KiB

Open AccessArticle

Pilot Validation Study of Inertial Measurement Units and Markerless Methods for 3D Neck and Trunk Kinematics during a Simulated Surgery Task

by Ce Zhang, Christian Greve, Gijsbertus Jacob Verkerke, Charlotte Christina Roossien, Han Houdijk and Juha M. Hijmans

Sensors 2022, 22(21), 8342; https://doi.org/10.3390/s22218342 - 30 Oct 2022

Cited by 2 | Viewed by 1897

Abstract

Surgeons are at high risk for developing musculoskeletal symptoms (MSS), like neck and back pain. Quantitative analysis of 3D neck and trunk movements during surgery can help to develop preventive devices such as exoskeletons. Inertial Measurement Units (IMU) and markerless motion capture methods are allowed in the operating room (OR) and are a good alternative for bulky optoelectronic systems. We aim to validate IMU and markerless methods against an optoelectronic system during a simulated surgery task. Intraclass correlation coefficient (ICC (2,1)), root mean square error (RMSE), range of motion (ROM) difference and Bland–Altman plots were used for evaluating both methods. The IMU-based motion analysis showed good-to-excellent (ICC 0.80–0.97) agreement with the gold standard within 2.3 to 3.9 degrees RMSE accuracy during simulated surgery tasks. The markerless method shows 5.5 to 8.7 degrees RMSE accuracy (ICC 0.31–0.70). Therefore, the IMU method is recommended over the markerless motion capture. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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15 pages, 2261 KiB

Open AccessPerspective

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

by Ahmad Yaser Alhaddad, Hussein Aly, Hoda Gad, Einas Elgassim, Ibrahim Mohammed, Khaled Baagar, Abdulaziz Al-Ali, Kishor Kumar Sadasivuni, John-John Cabibihan and Rayaz A. Malik

Sensors 2023, 23(11), 5003; https://doi.org/10.3390/s23115003 - 23 May 2023

Cited by 1 | Viewed by 1758

Abstract

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes. Full article

(This article belongs to the Special Issue Wearable Sensors for Physical Activity and Healthcare Monitoring)

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