# Signals of complexity and fragmentation in accelerometer data

## TL;DR

This paper shows that analyzing accelerometer data through complexity measures can reveal health differences not captured by traditional methods.

## Contribution

The study introduces complexity-based measures to detect health differences in accelerometer data beyond traditional metrics.

## Key findings

- Healthy individuals show higher regularity in activity patterns compared to DM1 patients.
- The correlation dimension differentiates health status independent of average activity metrics.
- Activity patterns of DM1 patients are more fragmented and less regular.

## Abstract

There is a growing interest to analyze physiological data from a complex systems perspective. Accelerometer data is one type of data that is easy to obtain but often difficult to analyze for insights beyond basic levels of description. Previous work hypothesizes that an individual’s activity pattern can be seen as a complex dynamical system. Here, we explore this hypothesis further by investigating whether complexity-based measures quantifying repetitiveness and fragmentation of activity captured via accelerometer can detect health differences beyond traditional measures. Our results demonstrate that healthy individuals have a higher regularity (indicated by a lower correlation dimension), a higher probability of activity after a period of rest, and a lower probability of a period of rest after a period of activity compared with patients living with Myotonic Dystrophy type I (DM1), a chronic, progressive, complex, multisystem disease. For the correlation dimension, this difference was independent of the average, coefficient of variation and autocorrelation of the activity signals. This suggests that the correlation dimension can extract clinically relevant information from accelerometer data. Therefore, our results corroborate the idea that a complexity perspective may help to reveal the emergent characteristics of health and disease.