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Home-Based Monitor for Gait and Activity Analysis

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Published on: August 8, 2019

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity

Claas Lendt¹, Theresa Braun², Bianca Biallas²

¹Institute for Movement Therapy and Movement-Oriented Prevention and Rehabilitation, German Sport University Cologne, Cologne, Germany. claas.lendt@stud.dshs-koeln.de.

The International Journal of Behavioral Nutrition and Physical Activity

|July 17, 2024

Summary

This summary is machine-generated.

Two no-code software methods accurately classify physical activities and postures from thigh-worn accelerometry data. SENS motion and ActiPASS demonstrate high agreement with reference labels in free-living conditions.

Keywords:

Accelerometer Activity classification Human activity recognition Physical behaviour Sedentary behaviour Validation

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Area of Science:

Biomedical Engineering
Human Movement Science
Wearable Technology

Background:

Accurate assessment of free-living physical behavior is crucial for understanding health and well-being.
Thigh-worn accelerometry offers a promising method for identifying activity types and postures.
User-friendly, no-code software solutions are needed to increase the adoption of accelerometry.

Purpose of the Study:

To evaluate the classification accuracy of two novel no-code software methods: SENS motion and ActiPASS.
To compare the performance of these methods in identifying physical activities and postures.
To assess their utility in both laboratory and free-living conditions.

Main Methods:

38 healthy adults wore thigh-mounted SENS motion accelerometers (12.5 Hz).
Participants performed standardized laboratory activities and unrestricted free-living activities.
Video recordings with chest-mounted cameras served as reference annotations for free-living data.
Classification outputs from SENS motion and ActiPASS software were compared to reference labels.

Main Results:

Analysis of 63.6 hours of activity data showed high agreement between algorithms and references.
Cohen's kappa coefficients in free-living conditions were 0.86 for SENS motion and 0.92 for ActiPASS.
Mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS motion and 0.92 (walking) to 0.99 (sedentary) for ActiPASS.

Conclusions:

Both SENS motion and ActiPASS no-code methods accurately classify basic physical activity types and postures.
The methods demonstrate high accuracy even with relatively low sampling frequency data.
Performance differences were noted, particularly in free-living cycling (SENS) and slow walking (ActiPASS), potentially due to differing activity class definitions.