# Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network
###### Abstract  
Human Activity Recognition (HAR) has become an active field of research in the computer vision community. Recognizing the basic activities of human beings with the help of computers and mobile sensors can be beneficial for numerous real-life applications. The main objective of this paper is to recognize six basic human activities, viz., jogging, sitting, standing, walking and whether a person is going upstairs or downstairs. This paper focuses on predicting the activities using a deep learning technique called Convolutional Neural Network (CNN) and the accelerometer present in smartphones. Furthermore, the methodology proposed in this paper focuses on grouping the data in the form of nodes and dividing the nodes into three major layers of the CNN after which the outcome is predicted in the output layer. This work also supports the evaluation of testing and training of the two-dimensional CNN model. Finally, it was observed that the model was able to give a good prediction of the activities with an average accuracy of 89.67%. Considering that the dataset used in this research work was built with the aid of smartphones, coming up with an efficient model for such datasets and some futuristic ideas pose open challenges in the research community. A 2021 16 December 2  
market in future. Additionally, numerous challenging applications, including automated visual surveillance systems and human-computer interaction (HCI) based on several senses (multimodal), require some level of HAR. Thus, in such cases, an accomplished recognition of human behaviour becomes critical [3]. The research in the field of HAR has been going progressively since the 1980s. One of the reasons is the advent of high-tech computers and smartphones. Smartphones are frequently used by most of the population in most countries of the world. The need to replace human beings with computers to supervise activities of other human beings has kept the research of HAR going. Sensing technologies are widely used in Ambient Assisted Living (AAL) and smart homes. Some existing solutions include physiological, environmental, and vision sensors that frequently assist in human behaviour and activity recognition for health monitoring purposes. Activity recognition is one of the core features of a smart home. Aged and mentally retarded persons suffer from numerous kinds of health issues. As a result, it can lead to improper performance of activities of daily living in such populations. Therefore, the detection of abnormal behaviours becomes indispensable for elderly and dependent people to ensure that they perform their activities correctly with minimum deviation [4]. This can ensure that they are safe and feeling well [5]. Furthermore, suitable types of sensors are also used for different applications such as Falls, Indoor localization, ADL recognition and Anomaly detection. Wearable sensors such as certain accelerometers are used as sensing modalities for all such applications except for Indoor localization [2]. This supports the fact that an accelerometer is a widely used sensor for activity recognition. There are two major challenges to be tackled for human activity recognition. Firstly, it is tough to manage the huge amount of information produced by the devices and sensors. Secondly, understanding the mapping of data to well-defined movements of the body becomes difficult. Nonetheless, there are a few methodologies that have achieved incredible results in bringing out useful information from the readings produced by sensors [6; 7]. Mostly, their experiments required the participants to carry devices in a particular position and orientation. It also required attaching the device to different areas of the body, such as the arms, waist, or wrist. Consequently, the data gathered in such a constrained situation with limitations on device orientation and the number of activities could influence the effectiveness of those models.