Project Details

This Project is done as part of the Course: CSL7460 Mobile and Pervasive Computing

Instructor: Dr. Suchetana Chakraborty

Project Title: Research-Oriented Study of Mobile Sensing and Human Activity Recognition

Team Members

• Hemani Gajjar (B19CSE031)
• Gautam Jain (B19CSE033)

About

Due to the widespread availability of low-cost wearable devices and portable computing devices, massive amounts of data, such as motion, location, physiological signals, and environmental data, are being captured. Human activity recognition (HAR) is a research topic that aims to understand how human behavior develops through the interpretation of attributes derived from data.

Objectives (As part of the Course Project)

1. Study research publications and articles on applications that employ sensor data to identify a person's specific movements or habits
2. Study methodologies used to recognize Human Activity from obtained data from sensors like : Accelerometer and Gyroscope and classify activities into one of the five activities: Sitting, Standing, Walking, Walking Upstairs, Walking Downstairs
3. Compare and contrast the various strategies mentioned in various papers and find the most reliable method of activity recognition
4. Distinguish between hard-to-distinguish activities like Sitting and Standing

Relevant Documents

• Human Activity Recognition Part 1.ipynb

• Human Activity Recognition Part 2.ipynb

• Project Presentation

• Project Report

• Project Files Repository

Implementation Steps

For: Human Activity Recognition Part 1.ipynb

• This colab file includes the implementation of three models namely: Decision Tree, Random Forest and Logistic Regression classifiers. In addition, it contains the analysis of the results in terms of Accuracy and Run Time along with comparing the Feature Selection Technique on this dataset - Dataset Link

1. Data Visualization
2. Pre-processing
3. Feature Extraction
4. Classifier Training & Validation Strategy

For: Human Activity Recognition Part 2.ipynb

• This colab file includes the implementaion of the 2D CNN model with the focus on distinguishing between Sitting and Standing activites on this dataset - Dataset Link

1. Standardize data
2. Frame Preparation
3. 2D CNN Model

References

1. Activity recognition using smartphone sensors
2. HAR using smartphone sensors with two-stage continuous hidden Markov models
3. Dataset: Human Activity Recognition with Smartphones
4. WISDM: WIreless Sensor Data Mining

Technologies Used

• Scikit-learn
• Matplotlib
• TensorFlow
• Keras