Human Action Recognition system using Machine Learning algorithms with IOT Sensor-Based Dataset

Resumen

The idea of ambient assisted living has gained support and adoption because to the quick
advancement of wireless sensor networks and ongoing improvements in the development of
scientific solutions based on artificial intelligence. This is as a result of its broad use in
healthcare and smart homes. As it enhances quality of life, the idea of human activity recognition
(HAR) & classification has caught the interest of many studies in this respect. Before putting this
idea into practise, though, it must first be tested using benchmarked data sets to analyse how well
it performs in real-world circumstances. The activity classification techniques have been used in
this work's continuation to increase its accuracy. These algorithms can be used as a reference
point to evaluate the effectiveness of other ones. Recent developments in sophisticated
technologies have simplified the routine collection and storage of IoT sensor data that may be
used to support decision-making. However, there is an urgent need to gather and arrange patient
data in electronic form in the majority of nations. The collected data will then be examined for a
diagnosis, a forecast, and potential therapies depending on the patient's eligibility. In this study,
human activity is predicted using the WISDM Smartphone and Smart watch Activity and
Biometrics Dataset. This study offered pre-trained machine learning models with various human
activities. Then Q-SVM (Quadratic-Support Vector Machine), L-SVM (Linear-Support Vector
Machine), LDA (Linear Discriminant Analysis), and PNN (Probabilistic Neural Network)
classification algorithms are used to classify human activities such as: sitting, standing, walking,
sitting down and standing up. Furthermore, we have also compared the obtained results with its
counterpart algorithms in order to prove its effectiveness.