Gait Recovery System for Parkinson's Disease using Machine Learning on Embedded Platforms
This work addresses gait recovery for Parkinson's patients by enabling deployable, low-latency detection on embedded devices, though it is incremental in optimizing existing methods.
The authors tackled Freezing of Gait in Parkinson's Disease by developing an embedded system that detects events using a resource-efficient machine learning classifier, achieving a model size reduction of 400 times with only a 1.3% drop in accuracy and an average recall of 93.58%.
Freezing of Gait (FoG) is a common gait deficit among patients diagnosed with Parkinson's Disease (PD). In order to help these patients recover from FoG episodes, Rhythmic Auditory Stimulation (RAS) is needed. The authors propose a ubiquitous embedded system that detects FOG events with a Machine Learning (ML) subsystem from accelerometer signals . By making inferences on-device, we avoid issues prevalent in cloud-based systems such as latency and network connection dependency. The resource-efficient classifier used, reduces the model size requirements by approximately 400 times compared to the best performing standard ML systems, with a trade-off of a mere 1.3% in best classification accuracy. The aforementioned trade-off facilitates deployability in a wide range of embedded devices including microcontroller based systems. The research also explores the optimization procedure to deploy the model on an ATMega2560 microcontroller with a minimum system latency of 44.5 ms. The smallest model size of the proposed resource efficient ML model was 1.4 KB with an average recall score of 93.58%.