Human_Activity_Recognition

Introduction

Human activity recognition (HAR) plays a crucial role in people’s daily life for its wide range of applications

Two main types of HAR:

• Video-based HAR: analyzes videos or images containing human motions from the camera
• Sensor-based HAR: motion from sensors – accelerometer, gyroscope, Bluetooth, sound sensors, etc.

Business use case

HAR using wearable devices has been actively investigated for a wide range of applications:

• Healthcare: fall detection systems, elderly monitoring, and disease prevention
• Sports training: energy expenditure, skill assessment
• Smart assistive technologies, i.e. smart homes: aid people with cognitive and physical limitations, etc.

Objectives of this project

• Focus on Sensor-based HAR: using accelerometer data to classify 6 activities

• Apply different types of Deep Learning technique to discover which method performs the best in term of: Generalization, Accuracy, f1-score, precision, recall, time given minimal data- preprocessing & transformation

Data source

• Wireless Sensor Data Mining Lab (Fordham University) http://www.cis.fordham.edu/wisdm/dataset.php

Reference: http://www.cis.fordham.edu/wisdm/includes/files/sensorKDD-2010.pdf

Model

• DNN (MLP)
• LSTM + Dense
• LSTM stacked 3 layers
• CNN-LSTM
• ConvLSTM