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

Claas LendtTheresa BraunBianca BiallasIngo FrobösePeter J Johansson
Published in: The international journal of behavioral nutrition and physical activity (2024)
The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.
Keyphrases
  • physical activity
  • machine learning
  • deep learning
  • artificial intelligence
  • big data
  • high resolution
  • high speed