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Challenge of Detecting Personal Deviations and Trends in Sensor Based Activity Data.

Miriam Lingg^1,2, Chantal Beutter¹, Stefan Sigle¹

¹MOLIT Institute gGmbH, Heilbronn, Germany.

Studies in Health Technology and Informatics

|September 12, 2023

Summary

This study developed a smartphone-based concept for detecting trends in personalized movement behavior. The Mann-Kendall trend test with Yue-Wang correction proved effective for analyzing variable physical activity data.

Keywords:

Electronic Data Processing Exercise Nonparametric Smartphone Statistics Trend Detection

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Area of Science:

Biomedical Engineering
Health Informatics
Wearable Technology

Background:

Physical activity is crucial for health, necessitating effective monitoring of movement behavior.
Smartphones offer a convenient platform for recording personal movement data.
Developing methods to analyze this data for health insights is of significant interest.

Purpose of the Study:

To create a concept for detecting trends in personalized movement behavior using smartphone data.
To evaluate the suitability of statistical methods for analyzing physical activity trends.

Main Methods:

Initial prototype used control charts, which were unsuitable for variable activity data.
A second prototype employed the non-parametric Mann-Kendall (MK) trend test.
The MK test was enhanced with the Yue-Wang correction for serial correlation and extended trend modeling (Theil-Sen slope).

Main Results:

Control charts were ineffective due to high variability in movement data.
The MK test with Yue-Wang correction proved robust and suitable for activity data analysis.
The MK test demonstrated robustness against outliers and suitability for detecting step-like trends.

Conclusions:

Live trend detection using the MK test can be simulated by overlapping time periods.
Further extension of trend modeling is recommended for enhanced concept development.
Test sensitivity can be optimized through parameter adjustments.