Label-Efficient Learning and Continuous Monitoring of Motor Function Symptoms

Parkinson’s disease (PD) is a progressive neurodegenerative disorder of the central nervous system affecting both motor and non-motor functions. It is characterized by weakened and damaged neurons and eventually neuronal death. PD significantly impacts quality of life, thus in-home monitoring, particularly of Freezing of Gait (FoG), is paramount in symptom management and disease progression observations. Existing technologies for monitoring symptoms are power-hungry, operate in controlled settings and rely on large amounts of labeled data, which are scarce, limiting real-world deployment.

 

Researchers at Arizona State University have developed a novel computationally-efficient framework for real-time FoG detection called LIFT-PD (Label-efficient In-home Freezing-of-gait Tracking). This framework combines self-supervised pre-training on unlabeled data with a differential hopping windowing technique which learns from limited labeled instances. Power consumption is further optimized by activating a deep learning module only during active periods. Experimental results demonstrate that LIFT-PD achieves a 7.25% increase in precision and a 4.4% improvement in accuracy compared to supervised models. Further, it uses as little as 40% of the labeled training data compared to supervised learning. When compared to continuous inference, the model activation module reduces inference time by up to 67%.

 

LIFT-PD paves the way for practical, energy-efficient, and unobtrusive in-home monitoring of PD patients with minimal data labeling requirements.

 

Potential Applications

Benefits and Advantages

For more information about this opportunity, please see

 

For more information about the inventor(s) and their research, please see

Dr. Zadeh’s laboratory webpage