Abstract
Each person has a unique gait, i.e., walking style, that can be used as a biometric for personal identification. Recent works have demonstrated effective gait recognition using deep neural networks, however most of these works predominantly focus on classification accuracy rather than model efficiency. In order to perform gait recognition using wearable devices on the edge, it is imperative to develop highly efficient low-power models that can be deployed on to small form-factor devices such as microcontrollers. In this paper, we propose a small CNN model with 4 layers that is very amenable for edge AI deployment and realtime gait recognition. This model was trained on a public gait dataset with 20 classes augmented with data collected by the authors, aggregating to 24 classes in total. Our model achieves 96.7% accuracy and consumes only 5KB RAM with an inferencing time of 70 ms and 125mW power, while running continuous inference on Arduino Nano 33 BLE Sense. We successfully demonstrated realtime identification of the authors with the model running on Arduino, thus underscoring the efficacy and providing a proof of feasiblity for deployment in practical systems in near future.
Abstract (translated)
每个人的步态都是独特的，也就是行走方式，可以作为个人识别的生物特征。最近的工作已经证明了使用深度神经网络有效识别人类步态，然而，大多数这些工作主要关注分类精度而不是模型效率。要在边缘使用可穿戴设备进行步态识别，就必须开发出能够在小尺寸设备上部署的高效低功耗模型。在本文中，我们提出了一个4层的紧凑型CNN模型，对边缘AI部署非常具有亲和力，并且可以实现实时步态识别。这个模型在由作者收集的20个类别的公开步态数据集上进行训练，累积到24个类别。我们的模型实现96.7%的准确率，并且在使用Arduino Nano 33 BLE Sense进行推理时，仅消耗5KB的RAM，推理时间为70ms，功率为125mW。通过在Arduino上运行我们的模型，我们成功实现了作者的实时识别，从而突出了其有效性和为即将到来的实际系统提供可行性的证明。
URL
https://arxiv.org/abs/2404.15312
