# Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks

## TL;DR

This paper explores using AI to detect physical activity in diabetes patients by analyzing blood glucose and heart rate data.

## Contribution

A novel AI algorithm using RNNs is developed to accurately detect physical activity in diabetes patients.

## Key findings

- RNNs achieved a 0.99 area under the ROC curve for physical activity detection.
- Combining CGMS and HR signals improves detection accuracy for diabetes therapy.
- The approach can help personalize diabetes management through activity pattern analysis.

## 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.