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Identification of a Person in a Trajectory Based on Wearable Sensor Data Analysis.

Jinzhe Yan1,2, Masahiro Toyoura2, Xiangyang Wu1

  • 1Department of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
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This study introduces novel models to match human trajectories from cameras with wearable sensor data. The SyncScore model and Likelihood Fusion algorithm improve accuracy in identifying individuals from fragmented data.

Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Wearable Technology

Background:

  • Tracking human movement using edge device cameras is essential for various applications.
  • Matching camera-derived trajectories with wearable sensor data (acceleration, angular velocity) presents a modality challenge.
  • Incomplete trajectory data complicates individual identification.

Purpose of the Study:

  • To develop a robust method for synchronizing and matching human trajectories from cameras with wearable sensor data.
  • To address the challenge of fragmented and incomplete trajectory information.
  • To improve the accuracy of identifying individuals based on multimodal data.

Main Methods:

  • Proposed the SyncScore model to quantify similarity between trajectory segments and sensor data.
Keywords:
IMU sensordeep learningedge computinghuman trajectorymultimodal sensor data analysistime seriestransformer

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  • Developed a Likelihood Fusion algorithm for systematic, time-integrated similarity updates.
  • Utilized the UEA dataset for model validation.

    • Main Results:

    • The proposed models achieved an accuracy of 0.725.
    • Sensitivity and F1-score were also confirmed to be 0.725.
    • Demonstrated effective matching of human trajectories and sensor data.

    Conclusions:

    • The SyncScore model and Likelihood Fusion algorithm offer a viable solution for multimodal human movement analysis.
    • The method shows promise for applications requiring accurate individual identification from partial data.
    • The approach achieved competitive results on a standard benchmark dataset.