6 citations · 6 across the 3 of their papers we have counts for
3 papers
Physically Plausible Data Augmentations for Wearable IMU-based Human Activity Recognition Using Physics Simulation
Nobuyuki Oishi, Philip Birch, Daniel Roggen +1
The scarcity of high-quality labeled data in sensor-based Human Activity Recognition (HAR) hinders model performance and limits generalization across real-world scenarios. Data aug…
In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models against Variability
Azhar Ali Khaked, Nobuyuki Oishi, Daniel Roggen +1
Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize healthcare by enabling continual health monitoring, disease prediction, an…
ETTrack: Enhanced Temporal Motion Predictor for Multi-Object Tracking
Xudong Han, Nobuyuki Oishi, Yueying Tian +4
Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, many methods that rely on filtering-based algor…