A Novel Accelerometer-Based Technique for Robust Detection of Walking Direction

IEEE Trans Biomed Eng. 2018 Aug;65(8):1740-1747. doi: 10.1109/TBME.2017.2774924. Epub 2017 Nov 17.

Abstract

Objective: Distance estimation in pedestrian dead reckoning is acquired using vector norm of accelerations, which results in positive values. However, anteroposterior acceleration is negative when a step is taken backward, which must be detected for accurate localization. This paper proposes a novel approach for the detection of walking direction, which uses a dominant trend duration.

Methods: The approach evaluates anteroposterior acceleration out of a foot-worn accelerometer for temporal dominance of acceleration trends during swing phase of the walk. The approach is tested for forward and backward walks with speed variations on a straight path as well as for forward walk at normal speed on a turning path. To validate the detection accuracy, success rates per participant per walk trial are calculated and then overall success rate for all the trials are reported. Moreover, metrics precision, recall and F1 scores are calculated for detection reliability in both directions.

Results: Overall 98 ± 2% detection accuracy is achieved on linear path considering both directions and all speed variations, whereas 93 ± 7% on turning path including left and right turns. In comparison with the state-of-the-art bidirectional detection approach, the proposed approach delivers accurate detection with speed variations without requiring prior training and relies on a single sensory feature.

Conclusion: Dominant trend duration is a novel and reliable feature to detect directional changes during communal walk with speed variation.

Significance: The approach can be employed in different contexts, such as enabling pedestrian localization approaches to accommodate back stepping or any application that requires knowledge of changing directions while walking.

MeSH terms

  • Accelerometry / methods*
  • Adult
  • Algorithms
  • Female
  • Gait / physiology*
  • Gait Analysis / methods*
  • Humans
  • Male
  • Walking* / classification
  • Walking* / physiology
  • Wearable Electronic Devices