Determining Physical Activity Characteristics From Wristband Data for Use in Automated Insulin Delivery Systems

Sevil, Mert; Rashid, Mudassir; Maloney, Zacharie; Hajizadeh, Iman; Samadi, Sediqeh; Hobbs, Nicole; Brandt, Rachel; Park, Minsun; Quinn, Laurie; Çınar, Ali

doi:10.1109/jsen.2020.3000772

Cited by 40 publications

(25 citation statements)

References 105 publications

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“…For diabetes, there were also several approaches showing impressive performance using DL. For example, in the study by Sevil et al [ 31 ], the authors proposed DL with LSTM to determine physical activity states for use in automated insulin delivery systems. The approach exploited a multi-sensor wristband and achieved 94.8% classification accuracy.…”

Section: Resultsmentioning

confidence: 99%

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Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

Triantafyllidis¹,

Kondylakis²,

Katehakis³

et al. 2022

JMIR Mhealth Uhealth

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Background Major chronic diseases such as cardiovascular disease (CVD), diabetes, and cancer impose a significant burden on people and health care systems around the globe. Recently, deep learning (DL) has shown great potential for the development of intelligent mobile health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. Objective The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Methods A search was conducted on the bibliographic databases Scopus and PubMed to identify papers with a focus on the deployment of DL algorithms that used data captured from mobile devices (eg, smartphones, smartwatches, and other wearable devices) targeting CVD, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, and the study period as well as the DL algorithm used, the main DL outcome, the data set used, the features selected, and the achieved performance. Results In total, 20 studies were included in the review. A total of 35% (7/20) of DL studies targeted CVD, 45% (9/20) of studies targeted diabetes, and 20% (4/20) of studies targeted cancer. The most common DL outcome was the diagnosis of the patient’s condition for the CVD studies, prediction of blood glucose levels for the studies in diabetes, and early detection of cancer. Most of the DL algorithms used were convolutional neural networks in studies on CVD and cancer and recurrent neural networks in studies on diabetes. The performance of DL was found overall to be satisfactory, reaching >84% accuracy in most studies. In comparison with classic machine learning approaches, DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Most of the studies did not provide details on the explainability of DL outcomes. Conclusions The use of DL can facilitate the diagnosis, management, and treatment of major chronic diseases by harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth tools and interventions.

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Section: Resultsmentioning

confidence: 99%

“…The DL approach was used for physical activity classification for automated insulin delivery systems [ 31 ], combining different layers including fully connected, LSTM, softmax, regression, ReLU, and dropout layers. In addition, the authors used the L2 regularization term to reduce the risk of overfitting (value 0.05).…”

Section: Resultsmentioning

confidence: 99%

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

Triantafyllidis¹,

Kondylakis²,

Katehakis³

et al. 2022

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…A major development in automatically compensating for planned and spontaneous physical activity is the use of machine learning with physiological data from physical activity trackers to estimate refined metrics that capture the characteristics of the physical activity. 21 The refined estimates of energy expenditure computed from the multiple physiological measurements collected using a wristband device can better characterize the modality and intensity of the physical activity (see Appendix A). The energy expenditure estimates provide reliable inputs to the predictive models, though the time-varying nature of the delays in the glycemic effects of the disturbances must be accommodated by the modeling framework.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems

Sun

Rashid

Hobbs

et al. 2021

J Diabetes Sci Technol

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Background: Adaptive model predictive control (MPC) algorithms that recursively update the glucose prediction model are shown to be promising in the development of fully automated multivariable artificial pancreas systems. However, the recursively updated glycemic prediction models do not explicitly consider prior knowledge in the identification of the model parameters. Prior information of the glycemic effects of meals and physical activity can improve model accuracy and yield better glycemic control algorithms. Methods: A glucose prediction model based on regularized partial least squares (rPLS) method where the prior information is encoded as the regularization term is developed to provide accurate predictions of the future glucose concentrations. An adaptive MPC is developed that incorporates dynamic trajectories for the glucose setpoint and insulin dosing constraints based on the estimated plasma insulin concentration (PIC). The proposed adaptive MPC algorithm is robust to disturbances caused by unannounced meals and physical activities even in cases with missing glucose measurements. The effectiveness of the proposed adaptive MPC based on rPLS is investigated with in silico subjects of the multivariable glucose-insulin-physiological variables simulator (mGIPsim). Results: The efficacy of the proposed adaptive MPC strategy in regulating the blood glucose concentration (BGC) of people with T1DM is assessed using the average percent time in range (TIR) for glucose, defined as 70 to 180 mg/dL inclusive, and the average percent time in hypoglycemia (<70 and >54 mg/dL) and level 2 hypoglycemia (≤54 mg/dL). The TIR for a cohort of 20 virtual subjects of mGIPsim is 81.9% ± 7.4% (with no hypoglycemia or severe hypoglycemia) for the proposed MPC compared with 73.9% ± 7.6% (0.2% ± 0.1% in hypoglycemia and 0.1% ± 0.1% in level 2 hypoglycemia) for an MPC based on a recursive autoregressive exogenous (ARX) model. Conclusions: The adaptive MPC algorithm that incorporates prior knowledge in the recursive updating of the glucose prediction model can contribute to the development of fully automated artificial pancreas systems that can mitigate meal and physical activity disturbances.

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“…Wearable device sensors continuously measure multiple physiological variables to enable self-monitoring of health and preventive medicine [1][2][3][4][5][6]. These signals provide valuable information in real time and act as surrogates for reporting variations in the levels of hormones such as cortisol, lactate, and epinephrine, which cannot be measured in real-time, noninvasively, and in daily living, to indicate PA and APS [7][8][9][10][11][12]. Physiological measurements are also useful in automated medical intervention decisions in chronic diseases.…”

Section: Introductionmentioning

confidence: 99%

“…The physiological variables collected from wearable devices have been useful in noninvasive detection of PA and APS [7][8][9][10][11]. Recent developments in signal processing of wearable device biosignals and machine learning (ML) algorithms enabled the integrated analysis of PA and APS by enabling the detection and discrimination of concurrent incidences of PA and APS [17,18].…”

Section: Introductionmentioning

confidence: 99%

Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Sevil

Rashid

Maloney

et al. 2020

Signals

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Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS.

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Determining Physical Activity Characteristics From Wristband Data for Use in Automated Insulin Delivery Systems

Cited by 40 publications

References 105 publications

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems

Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

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