Classifying Human Activities using Machine Learning and Deep Learning Techniques

TL;DR

This paper compares machine learning and deep learning methods for human activity recognition using inertial sensor data, demonstrating that Linear SVC and GRU models achieve superior accuracy.

Contribution

It presents a comprehensive evaluation of traditional machine learning and deep learning techniques on a Kaggle HAR dataset, highlighting the most effective models for activity classification.

Findings

Linear SVC outperforms other ML classifiers.

GRU achieves the highest accuracy among deep learning models.

Deep learning models generally outperform traditional ML methods.

Abstract

Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smartphones or Smart Watches, which can help them manage their daily routines in a healthy way. With this objective, Kaggle has conducted a competition to classify 6 different human activities distinctly based on the inertial signals obtained from 30 volunteers smartphones. The main challenge in HAR is to overcome the difficulties of separating human activities based on the given data such that no two activities overlap. In this experimentation, first, Data visualization is done on expert generated features with the help of t distributed Stochastic Neighborhood Embedding followed by applying various Machine Learning techniques like Logistic Regression, Linear SVC, Kernel SVM, Decision trees to better classify the 6 distinct human activities. Moreover, Deep Learning techniques like Long Short-Term Memory (LSTM), Bi-Directional LSTM, Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU) are trained using raw time series data. Finally, metrics like Accuracy, Confusion matrix, precision and recall are used to evaluate the performance of the Machine Learning and Deep Learning models. Experiment results proved that the Linear Support Vector Classifier in machine learning and Gated Recurrent Unit in Deep Learning provided better accuracy for human activity recognition compared to other classifiers.