This repository contains all the relevant code and data for three different Human Activity Recognition (HAR) projects using different datasets. The models used in these projects range from traditional machine learning algorithms such as Random Forests to more complex deep learning architectures like Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks.
This dataset was built from the recordings of study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone equipped with embedded inertial sensors.
The dataset can be downloaded from https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones
This dataset contains six different activities (Walking, Jogging, Upstairs, Downstairs, Sitting, and Standing) recorded by 36 users with a smartphone in their pocket. The dataset can be downloaded from https://www.cis.fordham.edu/wisdm/
This dataset includes data of 18 different physical activities, performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor. The dataset can be downloaded from https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring