Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions

Abstract

This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.99%, precision of 98.0 ± 2.2%, recall of 97.9 ± 3.5% and F1 score of 0.9 ± 0.0. As for the specific behavior prediction, our best performing classifier, gave population multi-class average accuracy of 92.43 ± 10.32%, precision of 92.94 ± 9.80%, recall of 92.20 ± 10.16% and F1 score of 92.56 ± 9.94%. Our investigation showed that physical activity and sedentary behavior can be detected, graded and classified with good accuracy and precision from three-axial accelerometer data collected in free-living conditions on people with T1D. This is particularly significant in the context of automated glucose control systems for diabetes, in that the methods we propose have the potential to inform changes in treatment parameters in response to the intensity of physical activity, allowing patients to meet their glycemic targets.

Keywords: Artificial pancreas; Automated insulin delivery; Free-living conditions; Physical activity; Supervised learning; Type 1 diabetes mellitus; Wearable devices; Wrist-worn accelerometer.

Fig. 1.

Representative subject data. Black Feature f₆₅ [a.u.] and thresholds for activity intensity classification: Blue Threshold between level 0 and 1, Orange Threshold between level 1 and 2, Green Threshold between level 2 and 3. If the magnitude of feature f₆₅ is below the blue threshold, it is assigned to activity level 0, if it is between the blue and the orange threshold, it is assigned activity level 1, if it is between the orange and the green thresholds it is assigned activity level 2 and finally if it is above the green threshold it is assigned level 3.

Fig. 2.

Flow chart for Method 1.

Fig. 3.

Flow chart for behavior classification.

Fig. 4.

Population results for the activity level classification: Top Confusion matrix, where numbers in the matrix and color scale represent number of annotations; Bottom Violin plot, compares the distributions of the predicted activity levels across the actual annotated levels. The x− and y− axis show the predicted and annotated, respectively, activity level on a scale from 0 to 3, where 0 is sedentary, 1 is mild, 2 is moderate and 3 is severe.

Fig. 5.

Evaluation of accuracy [%] in predicting the level for the most common performed activities across the population, regardless of fitness condition. Compared activities were walking, hiking, running, yarwork, housework and high intensity interval training (HIIT). In each boxplot, the central mark is the median, the bottom and top edges indicate the 25th and 75th percentiles, respectively. Blue whiskers extend to the most extreme data points not considered outliers.

Fig. 6.

Comparison of performances with RF (Left) and LSTM (Right). Participant 4 confusion matrices: the x− and y− axis show the predicted and annotated, respectively; the main diagonal reports true positives, elements off the main diagonal reports false negatives column-wise and false positives row-wise. Numbers in the matrix and color scale represent percent of total time.

Fig. 7.

Feature selection based on importance. Red Selected; Blue Discarded.