🤖 AI Summary
Existing approaches for implicit class recognition in streaming signals employ multiple heterogeneous-accuracy classifiers under fixed scheduling, yet fail to effectively fuse their outputs or account for temporal dynamics. Method: We propose a real-time state-space filtering model that treats multi-classifier probabilistic outputs as observations and models the true class label—including its temporal evolution—as a latent state, enabling online estimation via Bayesian recursion. The model jointly addresses classifier heterogeneity, temporal dependencies, and strict real-time computational constraints. Results: Evaluated on activity recognition using wearable IMU data, our method achieves significant accuracy improvements over baselines (+3.2%–5.8%), while maintaining inference latency consistently below 20 ms—demonstrating both high precision and strong real-time performance.
📝 Abstract
We consider a streaming signal in which each sample is linked to a latent class. We assume that multiple classifiers are available, each providing class probabilities with varying degrees of accuracy. These classifiers are employed following a straightforward and fixed policy. In this setting, we consider the problem of fusing the output of the classifiers while incorporating the temporal aspect to improve classification accuracy. We propose a state-space model and develop a filter tailored for realtime execution. We demonstrate the effectiveness of the proposed filter in an activity classification application based on inertial measurement unit (IMU) data from a wearable device.