Accelerometer Data

Accelerometer data, representing three-dimensional movement, is increasingly used to analyze a wide range of activities and behaviors across diverse fields. Current research focuses on applying machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs, including LSTMs and GRUs), to extract meaningful features and classify complex patterns from accelerometer time series. This analysis enables applications such as precise activity recognition in humans and animals, improved seismic hazard assessment, and enhanced monitoring of patient acuity and animal welfare, ultimately leading to more objective and data-driven insights in various scientific and practical domains.