A Survey of Datasets, Applications, and Models for IMU Sensor Signals

摘要

Inertial Measurement Units (IMUs) are small, low-cost sensors that can measure accelerations and angular velocities, making them valuable tools for a variety of applications, including robotics, virtual reality, and healthcare. With the advent of deep learning, there has been a surge of interest in using IMU data to train DNN models for various applications. In this paper, we survey the state-of-the-art ML models including deep neural network models and applications for IMU sensors. We first provide an overview of IMU sensors and the types of data they generate. We then review the most popular models for IMU data, including convolutional neural networks, recurrent neural networks, and attention-based models. We also discuss the challenges associated with training deep neural networks on IMU data, such as data scarcity, noise, and sensor drift. Finally, we present a comprehensive review of the most prominent applications of deep neural networks for IMU data, including human activity recognition, gesture recognition, gait analysis, and fall detection. Overall, this survey provides a comprehensive overview of the stateof-the-art deep neural network models and applications for IMU sensors and highlights the challenges and opportunities in this rapidly evolving field.