Memory-Augmented LSTM Autoencoder for Unsupervised Activity Recognition with IMU Sensor Fusion
HAR using Inertial Measurement Unit (IMU) sensors is vital for healthcare monitoring and rehabilitation. Despite deep learning advancements, major challenges remain: reliance on labeled data, multi-sensor fusion complexity, and the limited ability of unsupervised methods to capture spatiotemporal dependencies. These issues are pronounced in real-world scenarios with noisy data, overlapping activities, and missing labels. We propose a fully unsupervised spatiotemporal feature fusion framework using a memory-augmented autoencoder. It enhances activity representations via short temporal windows of multi-sensor IMU data, enabling real-time applications. Our framework extracts hierarchical sta...
| Authors | Saeid Arabzadeh, Farshad Almasganj, Mohammad Mahdi Ahmadi |
|---|---|
| Primary category | cs.CV |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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