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Performed various Deep Learning techniques to detect Human Activity using Sequential Data detect human activities generated by sensor-based wearable devices

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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

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

Model

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

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Performed various Deep Learning techniques to detect Human Activity using Sequential Data detect human activities generated by sensor-based wearable devices

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