Stretchable Hybrid Electronics: All‐in‐One, Wireless, Stretchable Hybrid Electronics for Smart, Connected, and Ambulatory Physiological Monitoring (Adv. Sci. 17/2019)

Kim, Yun‐Soung; Mahmood, Musa; Yong-Kuk, Lee; Kim, Nam Kyun; Kwon, Shinjae; Herbert, Robert; Kim, Dong-Hyun; Cho, Hee Cheol; Yeo, Woon‐Hong

doi:10.1002/advs.201970104

Cited by 6 publications

(9 citation statements)

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“…For the SNR comparison ( 31 ), data were analyzed using the power spectral density estimation (Welch method) and the Hamming window for bias reduction. The entire region of inhaling and exhaling was extracted using MATLAB, and the noise was assumed to be resting between inhale and exhale regions.…”

Section: Methodsmentioning

confidence: 99%

Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis

et al. 2022

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Modern auscultation, using digital stethoscopes, provides a better solution than conventional methods in sound recording and visualization. However, current digital stethoscopes are too bulky and nonconformal to the skin for continuous auscultation. Moreover, motion artifacts from the rigidity cause friction noise, leading to inaccurate diagnoses. Here, we report a class of technologies that offers real-time, wireless, continuous auscultation using a soft wearable system as a quantitative disease diagnosis tool for various diseases. The soft device can detect continuous cardiopulmonary sounds with minimal noise and classify real-time signal abnormalities. A clinical study with multiple patients and control subjects captures the unique advantage of the wearable auscultation method with embedded machine learning for automated diagnoses of four types of lung diseases: crackle, wheeze, stridor, and rhonchi, with a 95% accuracy. The soft system also demonstrates the potential for a sleep study by detecting disordered breathing for home sleep and apnea detection.

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Section: Methodsmentioning

confidence: 99%

Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis

et al. 2022

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“…From these, 8 studies [17,[21][22][23][28][29][30][31] used data from healthy individuals to represent normal sinus rhythm. The remaining 6 studies [32][33][34][35][36][37] derived normal sinus rhythm data from patients with arrhythmia (such as paroxysmal atrial fibrillation) or were unclear about the control group. Three studies had cardiovascular (disease) prevention as the target.…”

Section: Cardiovascular Outcomes (All Levels)mentioning

confidence: 99%

“…Only the latter two enabled real-time cardiovascular monitoring locally on the patient side, required for real-time detection and prevention of acute cardiovascular disease, as real-time information exchange to an external system would require high battery consumption and was therefore not feasible. Smartphones were used in both benchmark [38][39][40] and nonbenchmark [21,30,31,35] studies. Embedded devices, however, had only been demonstrated in benchmark studies [41][42][43][44].…”

Section: Processing Device (Level 6)mentioning

confidence: 99%

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Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View

et al. 2022

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Background Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention. Objective This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology. Methods We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as “wearables,” “machine learning,” and “cardiovascular disease.” Methodologies were categorized and analyzed according to machine learning–based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready). Results After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies’ ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies’ models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes. Conclusions Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation.

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“…[ 1,2 ] In smart sports, wearable electronic products are integrated with advanced sensors and data analysis algorithms, [ 3–5 ] which can analyze human health in real‐time, and track and record accurate human activities. [ 6–9 ] The typical smart bracelets can effectively measure and analyze heart rate, [ 10,11 ] body temperature, [ 12 ] energy expenditure, [ 13,14 ] sleep quality, [ 15 ] and movement status. [ 16,17 ] These intelligent wearable electronic devices offer unprecedented opportunities for personalized health monitoring and exercise training.…”

Section: Introductionmentioning

confidence: 99%

Deep‐Learning‐Assisted Neck Motion Monitoring System Self‐Powered Through Biodegradable Triboelectric Sensors

Sun,

Zhu,

Jia

et al. 2023

Adv Funct Materials

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In the new era of artificial intelligence (AI) and the Internet of Things (IoT), big data collection and analysis for intelligent sports are of great importance in monitoring human health. Herein, naturally, biodegradable triboelectric nanogenerators (NB‐TENGs) are developed based on low‐cost, recyclable, and environmentally friendly corn bracts, which are further applied in neck motion recognition. Three NB‐TENGs are integrated into an elastic collar to create a neck‐condition monitoring triboelectric sensor (NCM‐TS). An intelligent behavioral monitoring system is achieved by combining NCM‐TS with a deep learning model, which allows the recognition of four types of neck motion with an average accuracy of 94%. The developed neck motion monitoring sensor has broad potential applications in sports health monitoring, rehabilitation training, and healthcare.

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Stretchable Hybrid Electronics: All‐in‐One, Wireless, Stretchable Hybrid Electronics for Smart, Connected, and Ambulatory Physiological Monitoring (Adv. Sci. 17/2019)

Cited by 6 publications

References 0 publications

Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis

Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis

Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View

Deep‐Learning‐Assisted Neck Motion Monitoring System Self‐Powered Through Biodegradable Triboelectric Sensors

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