Application of Machine Learning Classifiers for Predicting Human Activity

Involving machine learning in recognizing human activities is a widely discussed topic of this era. It has a noticeable growth of interest for implementing a wide range of applications such as health monitoring, indoor movements, navigation and location-based services. This paper compares the performance of various machine learning algorithms in the domain of human activity recognition. Data of different aged people is collected using a custom setup and custom hardware. The observed data are modeled using machine learning and neural network. As recorded human motions have variations and complexity, four dataset reduction techniques are used to manipulate the results. Best accuracy is obtained for SVM classifier with 99% accuracy and after applying PCA and SVD techniques the accuracy percentages increased to 100%. On the other hand, worst accuracy is obtained for Naive Bayes classifier before and after applying LDA technique for 100 components. The accuracy percentages are 77% and 98% respectively.