  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.
