TabCLR: Contrastive Learning Representation of Tabular Data Classification for Indoor-Outdoor Detection

Abstract

Indoor-outdoor detection (IOD) has gained prominence recently, particularly in positioning technology, leveraging smartphone-embedded sensors. It is pivotal in pedestrian localization, activity recognition, transportation mode classification, and power management of Internet of Things (IoT) devices. While several approaches have been explored for IOD, including threshold-based methods and machine learning-based models, challenges remain in addressing these models’ temporal variations and computational complexities. Supervised learning approaches heavily rely on labeled datasets, which are costly and time-consuming to synthesize. We propose TabCLR, the first self-supervised learning (SSL) framework for IOD, to overcome these challenges. TabCLR utilizes contrastive learning representation tailored for tabular data classification using smartphone inertial sensors. It comprises data augmentation, a novel encoder network with self-attention, and an optimized contrastive loss function. Evaluation of TabCLR on multiple indoor-outdoor datasets demonstrates its superiority in both supervised and semi-supervised classification compared to existing methods. Notably, TabCLR outperforms SCARF by 6%-7%, indicating its effectiveness in capturing temporal feature representation patterns. Visualization analysis further illustrates TabCLR’s distinctive clustering of feature embeddings compared to SCARF. TabCLR represents a significant advancement in SSL methodologies for indoor-outdoor detection classification. Its robust performance showcases its potential to enhance accuracy in indoor-outdoor integrated GPS systems, addressing critical challenges in IOD classification.

Keywords

• Contrasting learning representation
• contrastive loss
• IMU sensors
• indoor-outdoor detection
• self-supervise learning
• tabular data