Memory-Augmented LSTM Autoencoder for Unsupervised Activity Recognition with IMU Sensor Fusion
arXiv:2606.28377v1 Announce Type: cross Abstract: 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 act
The increased sophistication of deep learning and memory-augmented autoencoders allows for more robust unsupervised approaches to activity recognition at a time when labeled data remains a bottleneck.
Improving unsupervised human activity recognition, especially with IMU sensors, lowers the barrier for deploying continuous health monitoring and rehabilitation systems without expensive, manually labeled datasets.
This advancement enables more adaptable and cost-effective deployment of AI systems in healthcare and other sectors where real-world data is noisy and labels are scarce, enhancing the autonomy of such systems.
- · Healthcare monitoring providers
- · Rehabilitation clinics
- · Deep learning researchers (unsupervised learning)
- · Wearable tech manufacturers
- · Companies relying heavily on manual data annotation for activity recognition
More accurate and pervasive health and activity tracking becomes feasible in real-world, unconstrained environments.
The reduced need for labeled data accelerates the development and deployment of personalized and proactive digital health interventions.
Ubiquitous, autonomous activity monitoring could transform insurance models and public health policies, shifting towards preventative and personalized healthcare.
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Read at arXiv cs.AI