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Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices
Wearable sensors have permeated into people's lives, ushering impactful applications in interactive systems and activity recognition.
Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks
To push this field forward, we build on recent advances in the area of continual machine learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation.
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.
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