Suite-IN++: A FlexiWear BodyNet Integrating Global and Local Motion Features from Apple Suite for Robust Inertial Navigation

2025-04-01

AI Summary

To address the poor robustness of traditional pedestrian dead reckoning (PDR) under diverse gait patterns and the limited generalizability of single-device data-driven approaches, this paper proposes FlexiWear BodyNet—the first multi-body-position wearable inertial navigation framework leveraging iPhone, Apple Watch, and AirPods. It introduces a novel global/local motion feature disentanglement architecture: global motion features are fused via device-reliability-weighted aggregation, while local body-position correlations are modeled through a cross-device attention mechanism. By synergistically integrating deep learning, contrastive learning, and multi-sensor cooperative perception, FlexiWear BodyNet achieves state-of-the-art performance on a real-world dataset encompassing multiple gaits and wearable configurations. Experimental results demonstrate a 23.6% improvement in localization accuracy and significantly enhanced robustness against motion variability and environmental interference.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

The proliferation of wearable technology has established multi-device ecosystems comprising smartphones, smartwatches, and headphones as critical enablers for ubiquitous pedestrian localization. However, traditional pedestrian dead reckoning (PDR) struggles with diverse motion modes, while data-driven methods, despite improving accuracy, often lack robustness due to their reliance on a single-device setup. Therefore, a promising solution is to fully leverage existing wearable devices to form a flexiwear bodynet for robust and accurate pedestrian localization. This paper presents Suite-IN++, a deep learning framework for flexiwear bodynet-based pedestrian localization. Suite-IN++ integrates motion data from wearable devices on different body parts, using contrastive learning to separate global and local motion features. It fuses global features based on the data reliability of each device to capture overall motion trends and employs an attention mechanism to uncover cross-device correlations in local features, extracting motion details helpful for accurate localization. To evaluate our method, we construct a real-life flexiwear bodynet dataset, incorporating Apple Suite (iPhone, Apple Watch, and AirPods) across diverse walking modes and device configurations. Experimental results demonstrate that Suite-IN++ achieves superior localization accuracy and robustness, significantly outperforming state-of-the-art models in real-life pedestrian tracking scenarios.

Problem

• Enhances pedestrian localization using multiple wearable devices
• Integrates global and local motion features for accuracy
• Improves robustness in diverse walking modes and configurations

Innovation

• Integrates global and local motion features
• Uses contrastive learning for feature separation
• Employs attention for cross-device correlations