Comparative Analysis of Different Machine Learning Classifiers on Human Activity Recognition Applications

Human Activity Recognition is an active field of research and is the basis for a wide range of modern day gadgets such as fitness bands, sleep-tracking devices etc.[6] In this research based project, we present our efforts of executing a performance comparison in order to determine the most effective machine learning algorithm for Human Activity Recognition applications. Our objective in this project is to train Artificial Neural Networks, Random Forest Classifiers, k-NN and Support Vector Machine algorithms over an HAR dataset (Human Activity Recognition using Smartphones Dataset) which is publicly available. We then graphically compare the accuracy achieved by each of the algorithms thereby finding out the approach that should be used by developers in Human Activity Recognition applications (such as fitness tracking platforms and home automation platforms) in order to improvise and contrive their platforms. It was observed that Support Vector Machines demonstrated the best performance with a staggering accuracy and is recommended for HAR applications. I.INTRODUCTION Nowadays, the use of smartphones has become deeply intertwined in our day to day lives.With smartphones being excessively used for wide range of applications such as gaming, information transfer, instant communication; it is also giving rise to Human Activity Recognition applications such as health monitoring, fall detection, context-aware mobile applications, human survey system and home automation etc. Smartphone-based activity recognition system is an active area of research owing to implications for a modern lifestyle and an urban design.As for example, for health reasons, some people show keen interest in tracking the amount of time they usually spend sitting down or the number of stairs they have climbed each day.For home automation, walking up the stairs or sitting on the sofa could trigger the lights or television to turn on.Human Activity Recognition Devices find their current applications in rapidly evolving fitness bands, smartphone applications, smart watches and medical research.[10,11] Understanding human activities has created a demand in health-care domain, especially in rehabilitation assistance, physiotherapist assistance, elder care support services and cognitive impairment.In a world of today, where almost every single person owns a smartphone, gathering the information relevant for Human Activity Recognition has not remained a difficult task as these devices are already equipped with embedded motion sensors (e.g.accelerometer, gyroscope, magnetometer, GPS, etc.) that can be used for activity detection.Sensors will record and monitor the patient's activities and report automatically when any abnormality is detected, so, a huge amount of resources can be saved.Other applications such as human survey system and location indicator are greatly benefitting from this study.Training process is always necessary when a new activity is added in to the system.The same algorithm parameters are needed to be trained and fine-tuned when the algorithm runs on different devices with various built-in sensors.However, labeling a training data (time-series data) is a time consuming procedure and it is not always possible to label all the training data by the users.As a result, we present a performance comparison among four machine learning algorithms in order to determine the most efficient algorithm for Human Activity Recognition problems.Unlike speech recognition, there is no grammar and strict definition for human activities.This causes twofold confusions.On one hand, the same activity may vary from subject to subject, which leads to the intraclass variations.The performing speed and strength also increases the interclass gaps.On the other hand, different activities may express similar shapes (e.g., using a laptop and reading).This is termed as interclass similarity which is a common phenomenon in HAR.Accurate and distinctive features need to be designed and extracted from the dataset to deal with these problems.The dataset we deal with in the project is 'Human Activity Recognition using Smartphones Dataset' taken from UCI Machine Learning Repository.It is a Classification dataset with 561 attributes/features, 10299 instances, and 6 classes.The dataset consists of elements based on experiments conducted on a group of 30 volunteers within an age group of 19-48 years.They performed a series of activities comprising of six basic activities: three static postures (standing, sitting, lying) and three dynamic activities (walking, walking downstairs and walking upstairs).All the participants were wearing a smartphone (Samsung Galaxy S II) on the waist during the experiment execution.The experiments were video-recorded to label the data manually.The obtained dataset was randomly divided into two sets, where 70% of the volunteers were selected for generating the training data and 30% to test the data.