Activity Recognition and IoT-Based Analysis Using Time Series and CNN

Abstract

Using time series data obtained from accelerometer and gyroscope sensors on an iPhone 6s, the authors address the topic of human activity and attribute detection. The collection contains time series data from 24 subjects who completed six activities in 15 trials. The aim is to appropriately identify the six activities using machine learning techniques. The usage of a convolutional neural network (CNN) for the categorization of human activity and attribute identification data obtained from accelerometer and gyroscope sensors on an iPhone 6s is proposed in this research. The collection contains time series data from 24 subjects who completed six activities in 15 trials. The study begins by pre-processing the data by transforming the folders into class labels and plotting the time series data. The time-series data is made up of multivariate data from both the accelerometer and gyroscope sensors, totaling 12 characteristics. The accelerometer sums up two acceleration vectors, gravity, and user acceleration, which may be distinguished using core motion tracking technology.