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%.

论文提出了一种基于嵌入式系统的机器学习子系统，以从加速度信号检测短暂的步态缺失并帮助帕金森病患者重获步态，该系统比基于云的系统具备更低的延迟和更广泛的可部署性，并探讨了在 ATMega2560 微控制器上部署模型的优化过程。

嵌入式平台上使用机器学习治疗帕金森病的步态恢复系统