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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 68975

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Dr. Ryan S. McGinnis

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Guest Editor

Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
Interests: signal processing; machine learning; biomechanics; computational dynamics; development of digital biomarkers, phenotypes, and therapeutics
Special Issues, Collections and Topics in MDPI journals

Dr. Ellen W. McGinnis

E-Mail Website
Guest Editor

Department of Psychiatry, University of Vermont, Burlington, VT 05405, USA
Interests: Anxiety; depression; childhood; digital mental health; mHealth; mental health assessment

Special Issue Information

Dear Colleagues,

The emerging field of digital medicine leverages advances in wearable and mobile technology to have a direct impact on diagnosing, preventing, monitoring, and treating disease. The critical need for these technologies has been emphasized in recent months with the COVID-19 pandemic and associated physical distancing measures preventing many in-person healthcare visits. This crisis has highlighted the opportunity that digital medicine provides for improving access and quality of care. However, before this vision can be realized, it is critical that the novel wearable and mobile health technologies underpinning digital medicine undergo rigorous validation. Despite this need, validation efforts are fractured, with recent calls for a structured process for the validation of these technologies that encompasses technical, clinical, and system-level considerations.

This Special Issue will gather novel developments in the use of wearable sensors for digital medicine and particularly efforts to validate their use in human subjects. Studies that highlight novel hardware and/or methodological developments, validation of these approaches in human subjects, and systematic reviews of the literature in a particular subarea of digital medicine are encouraged. Approaches that discuss approaches for collecting and analyzing multi-modal data are of particular interest. Where appropriate, we strongly encourage authors to deposit their source code and data in a public repository (e.g., GitHub) to help accelerate progress in this field. Topics include but are not limited to the following topics.

Topics:

Wearable sensors
Sensor fusion algorithms
Machine learning applied to wearable sensor data
Multi-modal sensing
Digital biomarkers
Digital phenotypes
Mental health
Neurological disorders
Physical rehabilitation
Older adults
Digital medicine

Dr. Ryan S. McGinnis
Dr. Ellen W. McGinnis
Guest Editors

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Published Papers (12 papers)

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Editorial

Jump to: Research

5 pages, 216 KiB

Open AccessEditorial

Advancing Digital Medicine with Wearables in the Wild

by Ryan S. McGinnis and Ellen W. McGinnis

Sensors 2022, 22(12), 4576; https://doi.org/10.3390/s22124576 - 17 Jun 2022

Cited by 9 | Viewed by 2513

Abstract

This editorial provides a concise overview of the use and importance of wearables in the emerging field of digital medicine [...] Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

Research

Jump to: Editorial

16 pages, 2059 KiB

Open AccessArticle

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

by Chen Bai, Amal A. Wanigatunga, Santiago Saldana, Ramon Casanova, Todd M. Manini and Mamoun T. Mardini

Sensors 2022, 22(8), 3061; https://doi.org/10.3390/s22083061 - 15 Apr 2022

Cited by 3 | Viewed by 2750

Abstract

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932

\pm

0.005), locomotion (0.946

\pm

0.003), lifestyle (0.927

\pm

0.006), and strength flexibility exercise (0.915

\pm

0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932

\pm

0.005), (0.840

\pm

0.004), and (0.869

\pm

0.005), respectively. The root mean square error for EE estimation was 0.836

\pm

0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models’ performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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20 pages, 5371 KiB

Open AccessArticle

Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device’s Data

by Farida Sabry, Tamer Eltaras, Wadha Labda, Fatima Hamza, Khawla Alzoubi and Qutaibah Malluhi

Sensors 2022, 22(5), 1887; https://doi.org/10.3390/s22051887 - 28 Feb 2022

Cited by 29 | Viewed by 8940

Abstract

With the ongoing advances in sensor technology and miniaturization of electronic chips, more applications are researched and developed for wearable devices. Hydration monitoring is among the problems that have been recently researched. Athletes, battlefield soldiers, workers in extreme weather conditions, people with adipsia who have no sensation of thirst, and elderly people who lost their ability to talk are among the main target users for this application. In this paper, we address the use of machine learning for hydration monitoring using data from wearable sensors: accelerometer, magnetometer, gyroscope, galvanic skin response sensor, photoplethysmography sensor, temperature, and barometric pressure sensor. These data, together with new features constructed to reflect the activity level, were integrated with personal features to predict the last drinking time of a person and alert the user when it exceeds a certain threshold. The results of applying different models are compared for model selection for on-device deployment optimization. The extra trees model achieved the least error for predicting unseen data; random forest came next with less training time, then the deep neural network with a small model size, which is preferred for wearable devices with limited memory. Embedded on-device testing is still needed to emphasize the results and test for power consumption. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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13 pages, 3639 KiB

Open AccessArticle

Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors

by Sara Pagnamenta, Karoline Blix Grønvik, Kamiar Aminian, Beatrix Vereijken and Anisoara Paraschiv-Ionescu

Sensors 2022, 22(3), 1117; https://doi.org/10.3390/s22031117 - 1 Feb 2022

Cited by 5 | Viewed by 2813

Abstract

Long-term monitoring of real-life physical activity (PA) using wearable devices is increasingly used in clinical and epidemiological studies. The quality of the recorded data is an important issue, as unreliable data may negatively affect the outcome measures. A potential source of bias in PA assessment is the non-wearing of a device during the expected monitoring period. Identification of non-wear time is usually performed as a pre-processing step using data recorded by the accelerometer, which is the most common sensor used for PA analysis algorithms. The main issue is the correct differentiation between non-wear time, sleep time, and sedentary wake time, especially in frail older adults or patient groups. Based on the current state of the art, the objectives of this study were to (1) develop robust non-wearing detection algorithms based on data recorded with a wearable device that integrates acceleration and temperature sensors; (2) validate the algorithms using real-world data recorded according to an appropriate measurement protocol. A comparative evaluation of the implemented algorithms indicated better performances (99%, 97%, 99%, and 98% for sensitivity, specificity, accuracy, and negative predictive value, respectively) for an event-based detection algorithm, where the temperature sensor signal was appropriately processed to identify the timing of device removal/non-wear. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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18 pages, 2061 KiB

Open AccessArticle

SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies

by Jonas Chromik, Kristina Kirsten, Arne Herdick, Arpita Mallikarjuna Kappattanavar and Bert Arnrich

Sensors 2022, 22(1), 408; https://doi.org/10.3390/s22010408 - 5 Jan 2022

Cited by 8 | Viewed by 6711

Abstract

Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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15 pages, 885 KiB

Open AccessArticle

A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

by Zheming Li and Wei He

Sensors 2021, 21(21), 7207; https://doi.org/10.3390/s21217207 - 29 Oct 2021

Cited by 15 | Viewed by 3706

Abstract

Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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14 pages, 3309 KiB

Open AccessArticle

Evaluation of the Validity and Reliability of Connected Insoles to Measure Gait Parameters in Healthy Adults

by Damien Jacobs, Leila Farid, Sabine Ferré, Kilian Herraez, Jean-Michel Gracies and Emilie Hutin

Sensors 2021, 21(19), 6543; https://doi.org/10.3390/s21196543 - 30 Sep 2021

Cited by 19 | Viewed by 3464

Abstract

The continuous, accurate and reliable estimation of gait parameters as a measure of mobility is essential to assess the loss of functional capacity related to the progression of disease. Connected insoles are suitable wearable devices which allow precise, continuous, remote and passive gait assessment. The data of 25 healthy volunteers aged 20 to 77 years were analysed in the study to validate gait parameters (stride length, velocity, stance, swing, step and single support durations and cadence) measured by FeetMe^® insoles against the GAITRite^® mat reference. The mean values and the values of variability were calculated per subject for GAITRite^® and insoles. A t-test and Levene’s test were used to compare the gait parameters for means and variances, respectively, obtained for both devices. Additionally, measures of bias, standard deviation of differences, Pearson’s correlation and intraclass correlation were analysed to explore overall agreement between the two devices. No significant differences in mean and variance between the two devices were detected. Pearson’s correlation coefficients of averaged gait estimates were higher than 0.98 and 0.8, respectively, for unipedal and bipedal gait parameters, supporting a high level of agreement between the two devices. The connected insoles are therefore a device equivalent to GAITRite^® to estimate the mean and variability of gait parameters. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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15 pages, 1262 KiB

Open AccessArticle

A Preliminary Investigation of the Effects of Obstacle Negotiation and Turning on Gait Variability in Adults with Multiple Sclerosis

by Lara Weed, Casey Little, Susan L. Kasser and Ryan S. McGinnis

Sensors 2021, 21(17), 5806; https://doi.org/10.3390/s21175806 - 28 Aug 2021

Cited by 10 | Viewed by 3176

Abstract

Many falls in persons with multiple sclerosis (PwMS) occur during daily activities such as negotiating obstacles or changing direction. While increased gait variability is a robust biomarker of fall risk in PwMS, gait variability in more ecologically related tasks is unclear. Here, the effects of turning and negotiating an obstacle on gait variability in PwMS were investigated. PwMS and matched healthy controls were instrumented with inertial measurement units on the feet, lumbar, and torso. Subjects completed a walk and turn (WT) with and without an obstacle crossing (OW). Each task was partitioned into pre-turn, post-turn, pre-obstacle, and post-obstacle phases for analysis. Spatial and temporal gait measures and measures of trunk rotation were captured for each phase of each task. In the WT condition, PwMS demonstrated significantly more variability in lumbar and trunk yaw range of motion and rate, lateral foot deviation, cadence, and step time after turning than before. In the OW condition, PwMS demonstrated significantly more variability in both spatial and temporal gait parameters in obstacle approach after turning compared to before turning. No significant differences in gait variability were observed after negotiating an obstacle, regardless of turning or not. Results suggest that the context of gait variability measurement is important. The increased number of variables impacted from turning and the influence of turning on obstacle negotiation suggest that varying tasks must be considered together rather than in isolation to obtain an informed understanding of gait variability that more closely resembles everyday walking. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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17 pages, 8312 KiB

Open AccessArticle

Validation of Adhesive Single-Lead ECG Device Compared with Holter Monitoring among Non-Atrial Fibrillation Patients

by Soonil Kwon, So-Ryoung Lee, Eue-Keun Choi, Hyo-Jeong Ahn, Hee-Seok Song, Young-Shin Lee and Seil Oh

Sensors 2021, 21(9), 3122; https://doi.org/10.3390/s21093122 - 30 Apr 2021

Cited by 25 | Viewed by 11397

Abstract

There are few reports on head-to-head comparisons of electrocardiogram (ECG) monitoring between adhesive single-lead and Holter devices for arrhythmias other than atrial fibrillation (AF). This study aimed to compare 24 h ECG monitoring between the two devices in patients with general arrhythmia. Twenty-nine non-AF patients with a workup of pre-diagnosed arrhythmias or suspicious arrhythmic episodes were evaluated. Each participant wore both devices simultaneously, and the cardiac rhythm was monitored for 24 h. Selective ECG parameters were compared between the two devices. Two cardiologists independently compared the diagnoses of each device. The two most frequent monitoring indications were workup of premature atrial contractions (41.4%) and suspicious arrhythmia-related symptoms (37.9%). The single-lead device had a higher noise burden than the Holter device (0.04 ± 0.05% vs. 0.01 ± 0.01%, p = 0.024). The number of total QRS complexes, ventricular ectopic beats, and supraventricular ectopic beats showed an excellent degree of agreement between the two devices (intraclass correlation coefficients = 0.991, 1.000, and 0.987, respectively). In addition, the minimum/average/maximum heart rates showed an excellent degree of agreement. The two cardiologists made coherent diagnoses for all 29 participants using both monitoring methods. In conclusion, the single-lead adhesive device could be an acceptable alternative for ambulatory ECG monitoring in patients with general arrhythmia. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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19 pages, 360 KiB

Open AccessArticle

Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back

by Lukas Adamowicz, F. Isik Karahanoglu, Christopher Cicalo, Hao Zhang, Charmaine Demanuele, Mar Santamaria, Xuemei Cai and Shyamal Patel

Sensors 2020, 20(22), 6618; https://doi.org/10.3390/s20226618 - 19 Nov 2020

Cited by 17 | Viewed by 6161

Abstract

The ability to perform sit-to-stand (STS) transfers has a significant impact on the functional mobility of an individual. Wearable technology has the potential to enable the objective, long-term monitoring of STS transfers during daily life. However, despite several recent efforts, most algorithms for detecting STS transfers rely on multiple sensing modalities or device locations and have predominantly been used for assessment during the performance of prescribed tasks in a lab setting. A novel wavelet-based algorithm for detecting STS transfers from data recorded using an accelerometer on the lower back is presented herein. The proposed algorithm is independent of device orientation and was validated on data captured in the lab from younger and older healthy adults as well as in people with Parkinson’s disease (PwPD). The algorithm was then used for processing data captured in free-living conditions to assess the ability of multiple features extracted from STS transfers to detect age-related group differences and assess the impact of monitoring duration on the reliability of measurements. The results show that performance of the proposed algorithm was comparable or significantly better than that of a commercially available system (precision:

0.990

vs.

0.868

in healthy adults) and a previously published algorithm (precision:

0.988

vs.

0.643

in persons with Parkinson’s disease). Moreover, features extracted from STS transfers at home were able to detect age-related group differences at a higher level of significance compared to data captured in the lab during the performance of prescribed tasks. Finally, simulation results showed that a monitoring duration of 3 days was sufficient to achieve good reliability for measurement of STS features. These results point towards the feasibility of using a single accelerometer on the lower back for detection and assessment of STS transfers during daily life. Future work in different patient populations is needed to evaluate the performance of the proposed algorithm, as well as assess the sensitivity and reliability of the STS features. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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15 pages, 1792 KiB

Open AccessArticle

Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions

by Malte Jacobsen, Till A. Dembek, Athanasios-Panagiotis Ziakos, Rahil Gholamipoor, Guido Kobbe, Markus Kollmann, Christopher Blum, Dirk Müller-Wieland, Andreas Napp, Lutz Heinemann, Nikolas Deubner, Nikolaus Marx, Stefan Isenmann and Melchior Seyfarth

Sensors 2020, 20(19), 5517; https://doi.org/10.3390/s20195517 - 26 Sep 2020

Cited by 15 | Viewed by 4873

Abstract

Atrial fibrillation (AF) is the most common arrhythmia and has a major impact on morbidity and mortality; however, detection of asymptomatic AF is challenging. This study aims to evaluate the sensitivity and specificity of non-invasive AF detection by a medical wearable. In this observational trial, patients with AF admitted to a hospital carried the wearable and an ECG Holter (control) in parallel over a period of 24 h, while not in a physically restricted condition. The wearable with a tight-fit upper armband employs a photoplethysmography technology to determine pulse rates and inter-beat intervals. Different algorithms (including a deep neural network) were applied to five-minute periods photoplethysmography datasets for the detection of AF. A total of 2306 h of parallel recording time could be obtained in 102 patients; 1781 h (77.2%) were automatically interpretable by an algorithm. Sensitivity to detect AF was 95.2% and specificity 92.5% (area under the receiver operating characteristics curve (AUC) 0.97). Usage of deep neural network improved the sensitivity of AF detection by 0.8% (96.0%) and specificity by 6.5% (99.0%) (AUC 0.98). Detection of AF by means of a wearable is feasible in hospitalized but physically active patients. Employing a deep neural network enables reliable and continuous monitoring of AF. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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11 pages, 1165 KiB

Open AccessArticle

Feasibility and Reliability of SmartWatch to Obtain 3-Lead Electrocardiogram Recordings

by Amirali Behzadi, Alireza Sepehri Shamloo, Konstantinos Mouratis, Gerhard Hindricks, Arash Arya and Andreas Bollmann

Sensors 2020, 20(18), 5074; https://doi.org/10.3390/s20185074 - 7 Sep 2020

Cited by 34 | Viewed by 10317

Abstract

Some of the recently released smartwatch products feature a single-lead electrocardiogram (ECG) recording capability. The reliability of obtaining 3-lead ECG with smartwatches is yet to be confirmed in a large study. This study aimed to assess the feasibility and reliability of smartwatch to obtain 3-lead ECG recordings, the classical Einthoven ECG leads I-III compared to standard ECG. To record lead I, the watch was worn on the left wrist and the right index finger was placed on the digital crown for 30 s. For lead II, the watch was placed on the lower abdomen and the right index finger was placed on the digital crown for 30 s. For lead III, the same process was repeated with the left index finger. Spearman correlation and Bland-Altman tests were used for data analysis. A total of 300 smartwatch ECG tracings were successfully obtained. ECG waves’ characteristics of all three leads obtained from the smartwatch had a similar duration, amplitude, and polarity compared to standard ECG. The results of this study suggested that the examined smartwatch (Apple Watch Series 4) could obtain 3-lead ECG tracings, including Einthoven leads I, II, and III by placing the smartwatch on the described positions. Full article

(This article belongs to the Special Issue Application of Wearables in Digital Medicine)

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