Motion Detection and Prediction Using Machine Learning Algorithm - A Research Paper Overview

Aug 3, 2026

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Machine learning is presented as an increasingly important field enabled by faster processing and miniaturized sensors. Ultrasonic sensors (HCSR-04) placed at different orientations collect distance-related data over defined time intervals. The collected sensor readings undergo preprocessing, data analytics, and visualization, then are split into training and test subsets. A prediction model is built using logistic regression and linear regression and evaluated with metrics including accuracy, F1 score, and precision for comparative performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

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Introduction

Methodology

Data acquisition

Data pre-processing

Data visualization and analysis

Data splitting

Motion Detection and Prediction Using Machine Learning Algorithm Ms.Jeevitha R1 ,Ms.Harshitha N2 , Ms.Chandana P C3 , Mr.Koushik P4 , Mrs.Babitha S5 1, 2, 3, 4, 5(ECE, DBIT/ VTU, India) 1Corresponding Author: [babithagi@gmail.com](babithagi@gmail.com) To Cite this Article Ms.Jeevitha R, Ms.Harshitha N , Ms. Chandana P C , Mr.Koushik P and Mrs.Babitha S, “Motion Detection and Prediction Using Machine Learning Algorithm”, Journal of Science and Technology, Vol. 05, Issue 05, Sep-October 2020, pp220-226 Article Info Received: 15-06-2020 Revised: 25-08-2020 Accepted: 30-08-2020 Published: 14-09-2020 Abstract:Machine learning is a branch of Artificial Intelligence which is gaining importance in the 21st century with increasing processing speeds and miniaturization of sensors, the applications of Artificial Intelligence and cognitive technologies are growing rapidly. An array of ultrasonic sensors i.e., HCSR-04 is placed at different directions, collecting data for a particularinterval of a period during a particular day. The acquired sensor values are subjected topre-processing, data analytics, and visualization. The prepared data is now split into test and train. A prediction model is designed using logistic regression and linear regression and checked for accuracy, F1 score, and precision compared. Keywords: Machine Learning, Logistic Regression F1 score I. Introduction Machine learning is a field of Artificial Intelligence which is gaining importance in the 21st century with increasing processing speeds and miniaturization of sensors, the applications of Artificial Intelligence and cognitive technologies are growing rapidly. An array of ultrasonic sensors is placed at various locations, collecting data for a particular interval of a period during a particular day. The acquired sensor values are subjected to preprocessing, data analytics, and visualization. The prepared data is now split into test and train. A prediction model is designed using logistic regression and linear regression and checked for accuracy, F1 score, and precision.An array of ultrasonic sensors i.e., HCSR-04 is placed at different orientations, collecting data for a particular interval of time during a particular day. The acquired sensor values are subjected to pre-processing, data analytics, and visualization. The prepared data is now split into test and train. A prediction model is designed using logistic regression and linear regression and checked for accuracy, F1 score, and precision.Motion detection of objects or living beings might be interesting in many domains, such as security devices, radars, the positioning of industrial robots, and auto-guidance systems. Most of these applications need the detection system to be non-invasive and not disturb the normal work environment, devices, or living beings inthe region of detection. This involves the choice of discrete vectors for information transport, with high immunity to all other factors unless the state of motion. II. Methodology In this methodology, we are going to collect the data from the sensor. The sensor is going to acquire the data by finding the distance between the target object. The acquired data will be fed into the system as an input. Data acquisition is done from the array of sensors and storing the database. Further, data will be pre-processed thereby it removes noise and unwanted values. The pre-processed data now it is eligible for data visualization and data analytics. thereby it hence the process of AI. For the knowledge of correlation, heatmap analysis is done. The data will be split into train and test based on particular randomness 'X'. A predictive model is designed by using logistic regression. In verification, we verify its accuracy, f1 score, and precession. Fig.1.Flow Diagram Make the initial state as the current state after evaluating the initial state. Run the loop until there is no features present. Select a feature that has not been y

FAQs

How is sensor data collected for motion detection in this work?

Ultrasonic sensors (HCSR-04) are positioned at different directions/orientations to measure distances to the target. Data is collected over a time interval and stored for further processing.

What preprocessing steps are applied before building the model?

The collected data is pre-processed to remove noise and unwanted values. The workflow also includes reduction to make complex data more understandable for analysis.

Which machine learning models and evaluation metrics are used for prediction?

A prediction model is designed using logistic regression (and linear regression is also referenced for comparison). Accuracy, F1 score, and precision are used to evaluate performance.

Aug 3, 2026

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