论文标题
帕金森氏病的步态恢复系统使用嵌入式平台上的机器学习
Gait Recovery System for Parkinson's Disease using Machine Learning on Embedded Platforms
论文作者
论文摘要
步态冻结（FOG）是诊断为帕金森氏病（PD）的患者的常见步态缺陷。为了帮助这些患者从雾发中恢复，需要节奏的听觉刺激（RAS）。作者提出了一个无处不在的嵌入式系统，该系统通过加速度计信号通过机器学习（ML）子系统检测雾事件。通过进行推断，我们避免了基于云的系统（例如延迟和网络连接依赖性）中普遍存在的问题。与最佳性能标准ML系统相比，使用的资源有效分类器将模型大小的要求降低了约400倍，最佳分类准确性的权衡仅为1.3％。上述权衡取舍有助于在包括基于微控制器的系统在内的各种嵌入式设备中的可部署性。该研究还探讨了在ATMEGA2560微控制器上部署模型的优化过程，最小系统延迟为44.5 ms。提议的资源有效ML模型的最小模型大小为1.4 kb，平均召回评分为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%.
