Machine-learning enabled wireless wearable sensors to study individuality of respiratory behaviors

Chen, Ang; Zhang, Jianwei; Zhao, Liangkai; Rhoades, Rachel Diane; Kim, Dong Yun; Wu, Ning; Liang, Jianming; Chae, Junseok

doi:10.1016/j.bios.2020.112799

Cited by 39 publications

(28 citation statements)

References 36 publications

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“…We classified the studies in three areas: technology development, attack prediction, and patient clustering. Technology development refers to contexts where machine learning is central to developing a new monitoring tool, [23][24][25][26][27][28][29][30][31][32][33] such as in cough and wheeze analysis. Attack prediction refers to studies that use machine learning to predict an asthma event (typically an attack) usually using mHealth data.…”

Section: Search Resultsmentioning

confidence: 99%

“…Many methods and devices for monitoring different aspects of a person have been studied individually and in combination. Machine learning can be applied to breath monitoring, 37,41 sleep monitoring, 23,[34][35][36]38,39,42 cough and wheeze, 24,26,27,[29][30][31]36 lung function monitoring, 23,25,[33][34][35]38,40 adherence monitoring, 32,35,38,43 and environment monitoring. 39,40,44 However, studies had different outcome measures; hence, it is difficult to conduct a direct comparison between studies.…”

Section: Search Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Tsang¹,

Pinnock²,

Wilson³

et al. 2022

JAA

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Background: Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on longterm management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods: We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results: Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion: In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

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Section: Search Resultsmentioning

confidence: 99%

Section: Search Resultsmentioning

confidence: 99%

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Tsang¹,

Pinnock²,

Wilson³

et al. 2022

JAA

View full text Add to dashboard Cite

show abstract

“…The posture classification accuracy of generic classification reaches 21.9±1.7%, but the weighted adaptive classification and individual classifier show very high scores, reaching 98.8±0.6% and 98.9±0.6%, respectively. After the occurrence of respiratory diseases or respiratory diseases such as chronic obstructive respiratory disease (COPD), asthma, and apnea, respiratory behaviour can be managed accurately and objectively [91].…”

Section: Respiratory Systemmentioning

confidence: 99%

Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

et al. 2021

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In artificial intelligence, deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing, and it also creates patterns for decision making. Deep learning or neural networks have been deployed in several fields, such as computer vision, natural language processing, and speech recognition. It has been used in many healthcare applications for the diagnosis and treatment of many chronic diseases. These algorithms have the power to avoid outbreaks of illness, recognize and diagnose illnesses, minimize running expenses for hospital management and patients. This paper discusses the deep learning methods used in different healthcare fields, i.e., identifying depression, heart diseases, physiological signals, lymph node metastases from breast cancer, etc. These diseases are categorized into the central nervous system, cardiovascular system, and respiratory system. For each category, after summarizing the studies, comparison tables are laid down using some important factors. Different applications, tools, methods, and data sets used for DL models are leveraged. Finally, research opportunities and challenges being faced for deep learning models are discussed.

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“…There have been significant innovations to achieve wearability using a range of material properties, structure, and integration. For example, stretchable sensors, such as strain gauges and many novel materials embedded bands, were attached to the human chest to measure local strain realizing the respiration monitoring [3][4][5]. Bioimpedance devices have been used for measuring lung capacity [6,7] because bioimpedance has a linear relationship with respiratory volume during normal breathing.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

George

Moon

Lee

2021

Sensors

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Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

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Machine-learning enabled wireless wearable sensors to study individuality of respiratory behaviors

Cited by 39 publications

References 36 publications

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

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