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.

Introduction

In the field of biometric identification, traditional methods such as fingerprint, facial recognition dominate. However, gait analysis is fast emerging as a unique and promising approach for identifying a person. Gait, the distinctive way an individual walks, carries inherent characteristics that can be leveraged for accurate non-intrusive person identification [2], [3]. Unlike static biometrics, such as fingerprints and facial features, gait analysis taps into the dynamic and behavioral aspects of an individual’s movement. Every person has a distinct gait, influenced by factors like anatomy, musculoskeletal structure, and personal habits. This distinctiveness makes gait analysis an intriguing and effective tool for identifying individuals in diverse settings, ranging from surveillance and security to healthcare and rehabilitation.

For this course project (Figure 1), we experimented on using light-weight convolutional neural network (CNN) models for edge-based gait detection for person identification. We perform pre-processing of the raw gait signals and model the CNN on the Edge Impulse framework. We use a popular gait dataset and further augment it with raw data collected from our team to train and test our model. We deploy our model on an Arduino Nano BLE 33 board for live inference and demonstration. We demonstrate highly accurate gait detection

For more information, please contact the authors:

• shanmugv@andrew.cmu.edu
• hpnair@andrew.cmu.edu
• pvellais@andrew.cmu.edu
• yongqiz2@andrew.cmu.edu