Human Activity Recognition using Signal Processing and Classical ML Algorithms

For many of the modern-day applications, human beings are being tracked based on their activity all throughout a given period. The applications may be in surveillance systems, health care, human interaction, marketing, etc. This has become a specific field of study altogether and people are extensively working on this field to find better solutions to the given real-world problems. The data is being collected through devices such as smartwatches (Fitbits, Apple Watch, etc.) or smartphones and is further used to analyze the activity of the human by whom the device is being used. Here we have used the data from UCI Machine Learning Repository which gives us the information acquired from 30 peoples’ smartphones. We have gyroscope and accelerometer data upon which appropriate signal processing techniques have been applied to generate features. Then these features are fed to various classical ML and ensemble models, and we perform a comparative analysis by looking into their specifics and accuracies.