An Exploratory Study of Blood Glucose Estimation from Photoplethysmography Signals using Machine Learning

• URL: http://arxiv.org/abs/2606.15927v1
• Date: Sun, 14 Jun 2026 17:05:57 GMT
• Title: An Exploratory Study of Blood Glucose Estimation from Photoplethysmography Signals using Machine Learning
• Abstract summary: Diabetes and extreme blood sugar levels are some of the major health problems faced by humans today across the world.<n>We first present a paired dataset comprising continuous PPG signals from a smartwatch along with glucose values recorded using a CGM device.<n>Preliminary results suggest that some predictive signals may exist, though more exploration is needed with more data from a larger number of individuals.
• Score: 1.4323566945483497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diabetes and extreme blood sugar levels are some of the major health problems faced by humans today across the world. While Continuous Glucose Monitoring (CGM) has emerged as an effective technology for management of diabetes as well as for monitoring blood sugar levels, this technology has traditionally been invasive (that is, requiring the piercing of the skin) and carries the risk of irritation, induration, etc. This highlights the need for accurate and non-invasive CGM methods that can be deployed at scale. With the emergence of various sensing technologies and their integration in wearables like the smart-watch, we now have the capability to continuously monitor body signals like the Photoplethysmogram (PPG) in a non-invasive manner. Having the ability to continuously monitor blood glucose through CGMs and continuously monitor PPG signals through a smart-watch offers an opportunity to get dense data on these two, opening the possibility of building machine learning and deep learning based models to estimate blood glucose level from PPG signals. In this work, we first present a paired dataset comprising continuous PPG signals from a smartwatch along with glucose values recorded using a CGM device. We also present the results of some preliminary experimental explorations performed on our dataset. These preliminary results suggest that some predictive signals may exist, though more exploration is needed with more data from a larger number of individuals. The dataset can be accessed at https://zenodo.org/records/20577959

Related papers

• Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers [44.735350554750916]
  We remotely recruited the largest dataset to date across the US to study insulin resistance.<n>Deep neural network models were developed to predict insulin resistance based on readily available digital and blood biomarkers.<n>Model showed 93% sensitivity and 95% adjusted specificity in obese and sedentary participants.
  arXiv  Detail & Related papers  (2025-04-30T16:10:20Z)
• Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study [43.28613210217385]
  We employ and compare three state-of-the-art generative models to generate PCG data.<n>Our results demonstrate that the generated PCG data closely resembles the original datasets.<n>In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs.
  arXiv  Detail & Related papers  (2024-12-17T18:07:40Z)
• Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting [6.466206145151128]
  GlucoNet is an AI-powered sensor system for continuously monitoring behavioral and physiological health.<n>We propose a decomposition-based transformer model that incorporates patients' behavioral and physiological data.<n>G GlucoNet achieves a 60% improvement in RMSE and a 21% reduction in the number of parameters, improving RMSE and MAE by 51% and 57%, using data obtained involving 12 participants with T1-Diabetes.
  arXiv  Detail & Related papers  (2024-11-16T05:09:20Z)
• emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography [47.160223334501126]
  emg2qwerty is a large-scale dataset of non-invasive electromyographic signals recorded at the wrists while touch typing on a QWERTY keyboard.<n>With 1,135 sessions spanning 108 users and 346 hours of recording, this is the largest such public dataset to date.<n>We show strong baseline performance on predicting key-presses using sEMG signals alone.
  arXiv  Detail & Related papers  (2024-10-26T05:18:48Z)
• From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [47.23780364438969]
  We present GluFormer, a generative foundation model for CGM data that learns nuanced glycemic patterns and translates them into predictive representations of metabolic health.<n>GluFormer generalizes to 19 external cohorts spanning different ethnicities and ages, 5 countries, 8 CGM devices, and diverse pathophysiological states.<n>In a longitudinal study of 580 adults with CGM data and 12-year follow-up, GluFormer identifies individuals at elevated risk of developing diabetes more effectively than blood HbA1C%.
  arXiv  Detail & Related papers  (2024-08-20T13:19:06Z)
• Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning [4.07484910093752]
  In the U.S., over a third of adults are pre-diabetic, with 80% unaware of their status. Existing wearable glucose monitors are limited by the lack of models trained on small datasets.
  arXiv  Detail & Related papers  (2024-06-12T07:05:53Z)
• Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features [0.0]
  Diabetes is a prevalent chronic condition that compromises the health of millions of people worldwide. Here, we present an alternative method to overcome shortcomings based on non-invasive optical photoplethysmography for detecting diabetes. We classify non-Diabetic and Diabetic patients using the PPG signal and algorithms for training Logistic Regression and eXtreme Gradient Boosting. Our findings are within the same range reported in the literature, indicating that machine learning methods are promising for developing remote, non-invasive, and continuous measurement devices for detecting and preventing diabetes.
  arXiv  Detail & Related papers  (2023-08-02T14:10:03Z)
• Personalized Understanding of Blood Glucose Dynamics via Mobile Sensor Data [0.0]
  We augment Continuous Blood Glucose (CGM) data with sensor input collected by a smart phone. This data set is novel in terms of it's size, the inclusion of GPS data, and the fact that it was collected non-intrusively from a free-living patient.
  arXiv  Detail & Related papers  (2023-02-02T20:26:05Z)
• Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction [4.040272012640556]
  We propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model. For the OhioT1DM dataset, containing eight weeks' data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 minutes and 60 minutes of prediction horizon (PH) Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management.
  arXiv  Detail & Related papers  (2021-01-18T02:31:38Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.