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Cho S, Aiello EM, Ozaslan B, Riddell MC, Calhoun P, Gal RL, Doyle FJ. Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes. J Diabetes Sci Technol 2024; 18:1146-1156. [PMID: 36799284 DOI: 10.1177/19322968231153896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
BACKGROUND Managing glycemia during and after exercise events in type 1 diabetes (T1D) is challenging since these events can have wide-ranging effects on glycemia depending on the event timing, type, intensity. To this end, advanced physical activity-informed technologies can be beneficial for improving glucose control. METHODS We propose a real-time physical activity detection and classification framework, which builds upon random forest models. This module automatically detects exercise sessions and predicts the activity type and intensity from tri-axial accelerometer, heart rate, and continuous glucose monitoring records. RESULTS Data from 19 adults with T1D who performed structured sessions of either aerobic, resistance, or high-intensity interval exercise at varying times of day were used to train and test this framework. The exercise onset and completion were both predicted within 1 minute with an average accuracy of 81% and 78%, respectively. Activity type and intensity were identified within 2.38 minutes and from the exercise onset. On participants assigned to the test set, the average accuracy for activity type and intensity classification was 74% and 73%, respectively, if exercise was announced. For unannounced exercise events, the classification accuracy was 65% for the activity type and 70% for its intensity. CONCLUSIONS The proposed module showed high performance in detection and classification of exercise in real-time within a minute of exercise onset. Integration of this module into insulin therapy decisions can help facilitate glucose management around physical activity.
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Affiliation(s)
- Sunghyun Cho
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Eleonora M Aiello
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Basak Ozaslan
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Michael C Riddell
- Physical Activity & Chronic Disease Unit, School of Kinesiology & Health Science, Faculty of Health, York University, Toronto, ON, Canada
| | | | - Robin L Gal
- Jaeb Center for Health Research, Tampa, FL, USA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
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2
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Fushimi E, Aiello EM, Cho S, Riddell MC, Gal RL, Martin CK, Patton SR, Rickels MR, Doyle FJ. Online Classification of Unstructured Free-Living Exercise Sessions in People with Type 1 Diabetes. Diabetes Technol Ther 2024. [PMID: 38417016 DOI: 10.1089/dia.2023.0528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Background: Managing exercise in type 1 diabetes is challenging, in part, because different types of exercises can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval, or resistance for the purpose of incorporation into an automated insulin delivery (AID) system. Methods: A long short-term memory network model was developed with real-world data from 30-min structured sessions of at-home exercise (aerobic, resistance, or mixed) using triaxial accelerometer, heart rate, and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose. Results: A total of 1610 structured exercise sessions were used to train, validate, and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for aerobic, 65% for interval, and 77% for resistance. In addition, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of -20.8 (40.3) mg/dL were achieved for sessions classified as aerobic, -16.2 (39.0) mg/dL for sessions classified as interval, and -11.6 (38.8) mg/dL for sessions classified as resistance. Conclusions: The proposed algorithm reliably identified physical activity associated with expected change in glucose, which could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class.
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Affiliation(s)
- Emilia Fushimi
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
- Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), La Plata, Argentina
| | - Eleonora M Aiello
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | - Sunghyun Cho
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
| | - Michael C Riddell
- School of Kinesiology and Health Science, Faculty of Health, Muscle Health Research Centre, York University, Toronto, Canada
| | - Robin L Gal
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Corby K Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | | | - Michael R Rickels
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
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3
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Dénes-Fazakas L, Simon B, Hartvég Á, Kovács L, Dulf ÉH, Szilágyi L, Eigner G. Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:2412. [PMID: 38676028 PMCID: PMC11054023 DOI: 10.3390/s24082412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 03/28/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024]
Abstract
Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.
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Affiliation(s)
- Lehel Dénes-Fazakas
- Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary; (L.D.-F.); (B.S.); (Á.H.); (L.K.); (L.S.); (G.E.)
- Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
- Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, 1034 Budapest, Hungary
| | - Barbara Simon
- Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary; (L.D.-F.); (B.S.); (Á.H.); (L.K.); (L.S.); (G.E.)
| | - Ádám Hartvég
- Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary; (L.D.-F.); (B.S.); (Á.H.); (L.K.); (L.S.); (G.E.)
| | - Levente Kovács
- Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary; (L.D.-F.); (B.S.); (Á.H.); (L.K.); (L.S.); (G.E.)
- Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
| | - Éva-Henrietta Dulf
- Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary; (L.D.-F.); (B.S.); (Á.H.); (L.K.); (L.S.); (G.E.)
- Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
- Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului Str. 28, 400014 Cluj-Napoca, Romania
| | - László Szilágyi
- Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary; (L.D.-F.); (B.S.); (Á.H.); (L.K.); (L.S.); (G.E.)
- Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
- Computational Intelligence Research Group, Sapientia Hungarian University of Transylvania, 540485 Tîrgu Mureș, Romania
| | - György Eigner
- Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary; (L.D.-F.); (B.S.); (Á.H.); (L.K.); (L.S.); (G.E.)
- Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
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Jacobs PG, Herrero P, Facchinetti A, Vehi J, Kovatchev B, Breton MD, Cinar A, Nikita KS, Doyle FJ, Bondia J, Battelino T, Castle JR, Zarkogianni K, Narayan R, Mosquera-Lopez C. Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Rev Biomed Eng 2024; 17:19-41. [PMID: 37943654 DOI: 10.1109/rbme.2023.3331297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
OBJECTIVE Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. METHODS Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. SIGNIFICANCE These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.
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Jung S, de l’Escalopier N, Oudre L, Truong C, Dorveaux E, Gorintin L, Ricard D. A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment. SENSORS (BASEL, SWITZERLAND) 2023; 23:4000. [PMID: 37112339 PMCID: PMC10145775 DOI: 10.3390/s23084000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/03/2023] [Accepted: 04/13/2023] [Indexed: 06/19/2023]
Abstract
This paper presents a novel approach to creating a graphical summary of a subject's activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.
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Affiliation(s)
- Sylvain Jung
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France
- AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France
- ENGIE Lab CRIGEN, F-93249 Stains, France
| | - Nicolas de l’Escalopier
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, F-92190 Clamart, France
| | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
| | - Charles Truong
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
| | - Eric Dorveaux
- AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France
| | - Louis Gorintin
- Novakamp, 10-12 Avenue du Bosquet, F-95560 Baillet en France, France
| | - Damien Ricard
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, F-92190 Clamart, France
- Ecole du Val-de-Grâce, Service de Santé des Armées, F-75005 Paris, France
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6
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Dénes-Fazakas L, Siket M, Szilágyi L, Kovács L, Eigner G. Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:8568. [PMID: 36366265 PMCID: PMC9658555 DOI: 10.3390/s22218568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/28/2022] [Accepted: 11/05/2022] [Indexed: 06/16/2023]
Abstract
Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate-the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity.
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Affiliation(s)
- Lehel Dénes-Fazakas
- Physiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
- Applied Informatics and Applied Mathematics Doctoral School, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
| | - Máté Siket
- Physiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
- Applied Informatics and Applied Mathematics Doctoral School, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
- Institute for Computer Science and Control, Eötvös Lóránd Research Network, H-1111 Budapest, Hungary
| | - László Szilágyi
- Physiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
- Biomatics and Applied Artificial Intelligence Institution, John von Neumann Faculty of Informatics, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
- Computational Intelligence Research Group, Sapientia Hungarian University of Transylvania, 540485 Tîrgu Mureş, Romania
| | - Levente Kovács
- Physiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
- Biomatics and Applied Artificial Intelligence Institution, John von Neumann Faculty of Informatics, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
| | - György Eigner
- Physiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
- Biomatics and Applied Artificial Intelligence Institution, John von Neumann Faculty of Informatics, Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
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7
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McCarthy M, Ilkowitz J, Zheng Y, Vaughan Dickson V. Exercise and Self-Management in Adults with Type 1 Diabetes. Curr Cardiol Rep 2022; 24:861-868. [PMID: 35524882 DOI: 10.1007/s11886-022-01707-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/20/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW The purpose of this review paper is to examine the most recent evidence of exercise-related self-management in adults with type 1 diabetes (T1D). RECENT FINDINGS This paper reviews the benefits and barriers to exercise, diabetes self-management education, the role of the healthcare provider in assessment and counseling, the use of technology, and concerns for special populations with T1D. Adults with T1D may not exercise at sufficient levels. Assessing current levels of exercise, counseling during a clinical visit, and the use of technology may improve exercise in this population.
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Affiliation(s)
- Margaret McCarthy
- Rory Meyers College of Nursing, New York University, New York, NY, USA.
| | - Jeniece Ilkowitz
- Pediatric Diabetes Center, NYU Langone Health, New York, NY, USA
| | - Yaguang Zheng
- Rory Meyers College of Nursing, New York University, New York, NY, USA
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8
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Riddell MC, Shakeri D, Scott SN. A Brief Review on the Evolution of Technology in Exercise and Sport in Type 1 Diabetes: Past, Present, and Future. Diabetes Technol Ther 2022; 24:289-298. [PMID: 34809493 DOI: 10.1089/dia.2021.0427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
One hundred years ago, insulin was first used to successfully lower blood glucose levels in young people living with what was then called juvenile diabetes. While insulin was not a cure for diabetes, it allowed individuals to resume a near normal life and have some freedom to eat more liberally and gain the strength they needed to live a more active lifestyle. Since then, a number of therapeutic and technical advances have arisen to further improve the health and wellbeing of individuals living with type 1 diabetes, allowing many to participate in sport at the local, regional, national or international level of competition. This review and commentary highlights some of the key advances in diabetes management in sport over the last 100 years since the discovery of insulin.
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Affiliation(s)
- Michael C Riddell
- School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada
| | - Dorsa Shakeri
- School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada
| | - Sam N Scott
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern University Hospital, University of Bern, Bern, Switzerland
- Team Novo Nordisk Professional Cycling Team, Atlanta, Georgia, USA
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