论文标题
使用0.5KB深学习模型实时传感器步态阶段检测
Real Time On Sensor Gait Phase Detection with 0.5KB Deep Learning Model
论文作者
论文摘要
卷积神经网络的步态相检测提供了准确的分类，但需要高计算成本，这抑制了实时的低功率对传感器处理。本文提出了基于细分的步态相检测，具有宽度和深度降尺度的U-NET（如模型），该模型仅需要0.5kb型号尺寸和每秒67K操作，精度为95.9％，即可轻松将其限制在传感器微控制器上的资源中。
Gait phase detection with convolution neural network provides accurate classification but demands high computational cost, which inhibits real time low power on-sensor processing. This paper presents a segmentation based gait phase detection with a width and depth downscaled U-Net like model that only needs 0.5KB model size and 67K operations per second with 95.9% accuracy to be easily fitted into resource limited on sensor microcontroller.
