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Freezing of Gait (FoG) is a common gait deficit among patients diagnosed withParkinson's Disease (PD). In order to help these patients recover from FoGepisodes, Rhythmic Auditory Stimulation (RAS) is needed. The authors propose aubiquitous embedded system that detects FOG events with a Machine Learning (ML)subsystem from accelerometer signals . By making inferences on-device, we avoidissues prevalent in cloud-based systems such as latency and network connectiondependency. The resource-efficient classifier used, reduces the model sizerequirements by approximately 400 times compared to the best performingstandard ML systems, with a trade-off of a mere 1.3% in best classificationaccuracy. The aforementioned trade-off facilitates deployability in a widerange of embedded devices including microcontroller based systems. The researchalso explores the optimization procedure to deploy the model on an ATMega2560microcontroller with a minimum system latency of 44.5 ms. The smallest modelsize of the proposed resource efficient ML model was 1.4 KB with an averagerecall score of 93.58%.

Gait Recovery System for Parkinson's Disease using Machine Learning on Embedded Platforms