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Diffusion-Driven Inertial Generated Data for Smartphone Location Classification

Noa Cohen, Rotem Dror, Itzik Klein

TL;DR

The paper tackles the data scarcity challenge in inertial sensing for smartphone location recognition by introducing a diffusion-based data generation framework. It transforms time-series accelerometer data into images via delay embedding (AccSignal2Image), then trains a conditional Elucidated Diffusion Model (EDM) with SongUNet to generate class-conditioned embedded images, which are inverted back to time-domain signals. The method is evaluated on the RIDI dataset using both image- and signal-domain classifiers, with strong fidelity evidenced by a low Fréchet Inception Distance (1.22) and minimal classifier performance gaps between real and synthetic data. The findings suggest that high-quality synthetic inertial data can substantially reduce data collection requirements for robust SLR models and potentially extend to related domains in robotics and healthcare.

Abstract

Despite the crucial role of inertial measurements in motion tracking and navigation systems, the time-consuming and resource-intensive nature of collecting extensive inertial data has hindered the development of robust machine learning models in this field. In recent years, diffusion models have emerged as a revolutionary class of generative models, reshaping the landscape of artificial data generation. These models surpass generative adversarial networks and other state-of-the-art approaches to complex tasks. In this work, we propose diffusion-driven specific force-generated data for smartphone location recognition. We provide a comprehensive evaluation methodology by comparing synthetic and real recorded specific force data across multiple metrics. Our results demonstrate that our diffusion-based generative model successfully captures the distinctive characteristics of specific force signals across different smartphone placement conditions. Thus, by creating diverse, realistic synthetic data, we can reduce the burden of extensive data collection while providing high-quality training data for machine learning models.

Diffusion-Driven Inertial Generated Data for Smartphone Location Classification

TL;DR

The paper tackles the data scarcity challenge in inertial sensing for smartphone location recognition by introducing a diffusion-based data generation framework. It transforms time-series accelerometer data into images via delay embedding (AccSignal2Image), then trains a conditional Elucidated Diffusion Model (EDM) with SongUNet to generate class-conditioned embedded images, which are inverted back to time-domain signals. The method is evaluated on the RIDI dataset using both image- and signal-domain classifiers, with strong fidelity evidenced by a low Fréchet Inception Distance (1.22) and minimal classifier performance gaps between real and synthetic data. The findings suggest that high-quality synthetic inertial data can substantially reduce data collection requirements for robust SLR models and potentially extend to related domains in robotics and healthcare.

Abstract

Despite the crucial role of inertial measurements in motion tracking and navigation systems, the time-consuming and resource-intensive nature of collecting extensive inertial data has hindered the development of robust machine learning models in this field. In recent years, diffusion models have emerged as a revolutionary class of generative models, reshaping the landscape of artificial data generation. These models surpass generative adversarial networks and other state-of-the-art approaches to complex tasks. In this work, we propose diffusion-driven specific force-generated data for smartphone location recognition. We provide a comprehensive evaluation methodology by comparing synthetic and real recorded specific force data across multiple metrics. Our results demonstrate that our diffusion-based generative model successfully captures the distinctive characteristics of specific force signals across different smartphone placement conditions. Thus, by creating diverse, realistic synthetic data, we can reduce the burden of extensive data collection while providing high-quality training data for machine learning models.

Paper Structure

This paper contains 16 sections, 5 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Proposed Approach
  3. Signal-to-Image Transformation
  4. Diffusion Model Architecture
  5. Diffusion Model Training Process
  6. Smartphone location recognition
  7. Image-based CNN Classifier
  8. Signal-based CNN Classifier
  9. Experiments and Results
  10. Dataset and Preprocessing
  11. Visual Distribution Analysis
  12. t-distributed stochastic neighbor embedding
  13. Probability density functions
  14. Fréchet inception distance
  15. Classifiers elevation
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Block diagram of our proposed approach for synthetic specific force signal generation, showing the three main stages: signal-to-image transformation, using the AccSignal2Image transformation, a vision-based diffusion model implementing elucidating diffusion models with Song-Unet as neural backbone, and image-to-signal reconstruction, using the Image2AccSignal transformation.
  • Figure 2: (left) Original recorded accelerometer x-axis readings. (right) The resulting delay embedded image using AccSignal2Image transformation.
  • Figure 3: Comparison between real and synthetic data. Top row: t-SNE embeddings of the real and generated signals for each specific-force channel (x, y and z). Bottom row: Corresponding probability density functions illustrating the distributional similarity between real and synthetic data. The legends at the top and bottom differentiate between real and synthetic signals across various placements.