A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices

Choi, Ye Rim; Jeon, Yu-Mi; Wang, Lin; Kim, Kwanho

doi:10.3390/s17091936

Cited by 33 publications

(16 citation statements)

References 26 publications

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“…In addition to health promotion, these devices are also being tested to identify and diagnose medical conditions, including detection of falls 6 , mental status 7 , heart disease 8 , neurological diseases 9 and others.…”

Section: Introductionmentioning

confidence: 99%

Comparison of SpO2 and heart rate values on Apple Watch and conventional commercial oximeters devices in patients with lung disease

Pipek

Nascimento

Acencio

et al. 2021

Sci Rep

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Lung diseases have high mortality and morbidity, with an important impact on quality of life. Hypoxemic patients are advised to use oxygen therapy to prolong their survival, but high oxygen saturation (SpO2) levels can also have negative effects. Pulse oximeters are the most common way to assess oxygen levels and guide medical treatment. This study aims to assess whether wearable devices can provide precise SpO2 measurements when compared to commercial pulse oximeters. This is a cross-section study with 100 patients with chronic obstructive pulmonary disease and interstitial lung disease from an outpatient pneumology clinic. SpO2 and heart rate data were collected with an Apple Watch Series 6 (Apple) and compared to two commercial pulse oximeters. The Bland–Altman method and interclass correlation coefficient were used to compare their values. We observed strong positive correlations between the Apple Watch device and commercial oximeters when evaluating heart rate measurements (r = 0.995, p < 0.001) and oximetry measurements (r = 0.81, p < 0.001). There was no statistical difference in the evaluation of skin color, wrist circumference, presence of wrist hair, and enamel nail for SpO2 and heart rate measurements in Apple Watch or commercial oximeter devices (p > 0.05). Apple Watch 6 is a reliable way to obtain heart rate and SpO2 in patients with lung diseases in a controlled environment.

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Section: Introductionmentioning

confidence: 99%

Comparison of SpO2 and heart rate values on Apple Watch and conventional commercial oximeters devices in patients with lung disease

Pipek

Nascimento

Acencio

et al. 2021

Sci Rep

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“…Sensors, in combination with machine learning algorithms, have been explored in an increasing variety of applications. For example, they have been shown to validly identify periods of high stress in some samples, to accurately recognize types of physical activities, and to be potentially useful for identifying episodes of anxiety and depression in youth 43–46 . Wearable physiological and other sensors are relatively new to pediatric research, however, and feasibility and acceptability data are somewhat scant.…”

Section: Discussionmentioning

confidence: 99%

“…to be potentially useful for identifying episodes of anxiety and depression in youth. [43][44][45][46] Wearable physiological and other sensors are relatively new to pediatric research, however, and feasibility and acceptability data are somewhat scant. In the current study, most youth with migraine rated the study methods involving sensors to be acceptable and generally low burden.…”

Section: Range Of Responsesmentioning

confidence: 99%

Feasibility of using “SMARTER” methodology for monitoring precipitating conditions of pediatric migraine episodes

Connelly

Boorigie

2020

Headache

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Objective: To evaluate the feasibility in children of an intensive prospective data monitoring methodology for identifying precipitating conditions for migraine occurrence. Background: Migraine headaches are a common pain condition in childhood and can become increasingly chronic and disabling with repeated episodes. Identifying conditions that forecast when a child's migraine is likely to occur may facilitate next-generation adaptive treatments to prevent future migraine attacks. Methods: In this cohort study of a sample of 30 youth (ages 10-17) with migraine recruited through a pediatric headache clinic, smartphones supplemented with wearable biosensors were used over a period of 28 days to collect contextual data thought to be potentially relevant to headache occurrence. Self-reported data on headache occurrence, lifestyle, and perceptions of the environment were collected in 4 epochs per day using custom real-time reporting software. Data derived from the wearable biosensor included information on autonomic arousal and physical activity. Built-in sensors on participants' own phones also were used to indicate location and to quantify the sensory environment (e.g., ambient noise and light levels). Data fidelity was monitored to evaluate feasibility of the methods, and participant acceptability was assessed via an end-of-study survey. Results: Self-report data were obtained on a mean of 88.9% (24.9/28) of assigned days (SD = 22.4%) and at a mean of 68.9% (77.2/112) of assigned moments (SD = 24.5%). Data from the wearable biosensor were obtained for a mean of 18.7 hours per day worn (SD = 2.3 hours), with participants on average wearing the sensor on 20.3 days (SD = 9.9). Fidelity of obtaining objective data from phone sensors on the sensory environment and other environmental conditions was highly variable, with these data obtainable from 5 to 22/30 (16.7%-73.3%) of participants' own phones. Most participants (63.3%-100%) responded with at least "somewhat agree" to questions about acceptability of the study methods. However, 5 to 7/30 (16.7%-23.3%) patients indicated difficulties with burden and remembering to wear the sensor. Almost all participants (29/30, 96.7%) agreed that they would want information about when a migraine might occur. How to cite this article: Connelly MA, Boorigie ME. Feasibility of using "SMARTER" methodology for monitoring precipitating conditions of pediatric migraine episodes. Headache.

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“…The authors found that the most distinctive features for detecting stress are EDA and HR. Remote monitoring of child safety through stress patterns was tackled in [ 83 ]. Detecting stress and anxiety in children with autism spectrum disorder (ASD) was investigated in [ 58 ].…”

Section: Wearable Devices and Machine Learningmentioning

confidence: 99%

Machine Learning for Healthcare Wearable Devices: The Big Picture

Sabry

Eltaras

Labda

et al. 2022

Journal of Healthcare Engineering

147

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Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases’ diagnosis, and it can play a great role in elderly care and patient’s health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.

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A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices

Cited by 33 publications

References 26 publications

Comparison of SpO2 and heart rate values on Apple Watch and conventional commercial oximeters devices in patients with lung disease

Comparison of SpO2 and heart rate values on Apple Watch and conventional commercial oximeters devices in patients with lung disease

Feasibility of using “SMARTER” methodology for monitoring precipitating conditions of pediatric migraine episodes

Machine Learning for Healthcare Wearable Devices: The Big Picture

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