International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 04 | Apr 2022
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Human Activity Recognition Using Smartphone Chandan Kashyap1, Chandrashekhar Munde1, Tejas Karande1, Charmi Chaniyara2 1Student,
Department of Information Technology, Atharva College of Engineering, Maharashtra, India Professor, Dept. of Information Technology, Atharva college of Engineering, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------2Assistant
feasibility and also making it cost efficient as the user won't have to rely on any type of external device.
Abstract – Human Activity Recognition is the study of
human activity using sensors which predict human activity like walking, running, sitting etc. This HAR model has been studied and tested using inbuilt 3-dimensional sensors present inside the smartphone. They are Accelerometer and Gyroscope. The readings from this sensors is taken as input and estimates motion actions using deep learning technique. In this paper CNN (Convolutional Neural Network) is used along with LSTM (Long Term Short Memory) for the performance prediction. The entire proposed system is implemented on a Smartphone device (Android Application) as it is available for all to use.
In this project we have tried to implement a fitness based android application that will be able to recognise human activities in a smartphone by taking raw values and classifying it using the CNN-LSTM model capable of predicting activities.
1.1 LITERATURE REVIEW The research around the field of Human Activity Recognition shows the rise in number of technologies that can be used to to achieve the desired output solution. Initially the approaches that were used for this problem task revolved around the different machine learning algorithms which can be used to solve such real world problems. However, due to recent advancements in the field of Artificial Intelligence, deep learning has started to gain the supremacy in terms of accuracy when trained with large amounts of data when it comes to prediction and classification using the older machine learning algorithms. This section talks through these few algorithms that can be implemented for the purpose of HAR.
Key Words: Human Activity Recognition, Deep learning, Convolutional Neural Network (CNN), CNN-LSTM, Gyroscope, Accelerometer, Smartphone Sensors.
1. INTRODUCTION In this emerging technological world, the smartphone has not only become omnipresent but also an integral part of the lifestyle that we live in. From day to day communication, entertainment and keeping yourself organized it can accomplish every task. It is almost like carrying a miniature version of a computer in your pocket. A recent survey from Statista shows that alone in the year 2020, approximately 1.5 billion smartphones were sold with an average increase of 23.72% yearly.
In a research study [1], many supervised machine learning algorithms were used for the implementation of HAR. Algorithms such as J48, Support Vector Machine (SVM), Naive Bayes and Multilayer Perceptron to classify the output in three categories: walking , running and sitting . Which is done by monitoring the changes after every 20 instances and by using a fixed window length without overlapping in the feature extraction stage. The SVM algorithm was found to be the one giving the highest accuracy of more more than 90%.
There are multiple aspects in which a smartphone can be used to bring about a change. One such domain that it can also cover is HAR (Human activity Recognition). HAR can be used for maintaining your lifestyle with regard to fitness and health. HAR is a technique of predicting what type of activity a person is doing based on the trace of their movement using sensors. Sensors which are used record the data in three dimensions (X axis, Y axis, Z axis). With the system that we have proposed, the smartphone will be able to classify these activities performed by the user like running, jogging, walking, sitting along with features to count the number of steps and returning other features like distance traveled and calories burnt while doing so.
A published[2] work reveals more information about HAR when implemented using ML and DL models. Models were created using algorithms like (Support Vector Machines)SVM, K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN). Where it found that the accuracy of SVM and CNN were very similar to each other even after adding dimension reduction.
It will be able to do so by taking inputs from the inertials sensors of the smartphone namely the Accelerometer, Gyroscope and the Pedometer which are inbuilt in your smartphone and which adds to the high availability,
So when further study is done, we find out that even though the accuracy rates of both ML and DL approaches are fluctuating . Studies[3] show why deep learning outperforms machine learning techniques. Unlike the ML
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