Human activity recognition system using smartphone based on machine learning algorithms (original) (raw)

Human Activity Recognition System Using Smartphone Data Sensors with Python and Machine Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

This project depicts recognition Human activity Using data generated from user Smartphones Machine Learning repository to recognize six human activities. These activities are standing, sitting, laying, walking, upstair and walking, ddownstairs. Data is collected from embedded accelerometer, gyroscope and other sensor .Data is randomly divided into 7:3 ratios to From training and testing data set respectively. Activity Classification done using Machine Learning models Namely Random Forest. support vector machine, Neural Network and k-Nearest Neighbor. We have compared accuracy and performance of these model using confusion matrix and random simulation. Human Activity recognition(HAR) is classifying activity of person using responsive sensor that are affected from human movement. Both users and capabilities of smartphone With them. These facts makes HAR more important and Popular. This work focuses on recognition of Human activity using smartphone sensor different machine learning clssification approaches. Data retrieved from smartphones accelerometer and gyroscope sensor are classified On order to recognize human activity. Results of the approaches used compared in terms of efficiency and precision.

Prediction of Human Activity Recognition based on Smart phones using Machine Learning Technique

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

Human Activity Recognition database was built from the recording of 30 study participants performing activities of daily living (ADL) while caring a waist mounted smart phones with embedded inertial sensors. The objective is to classify activities into one of the six activities performed. The experiments have been carried out with a group of 30 volunteers within an age bracket 19-48. Each person six activities wearing a smart phone on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly portioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data. The sensor signals (accelerometer & gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/ window).

Recognize Human Physical Activity Using Smartphone Sensors : A Review

Abstract— Activity Recognition is a very important application in several real applications. it's a method to acknowledge the actions and goals of one or a lot of people from a series of observations on the people actions and therefore the environmental conditions. The Smartphone's are main platform for recognizing the human action. The Smartphone’s are equipped with numerous sensors like Accelerometers, GPS, Barometer, Compass Sensors, Gyroscope etc. of these sensors have its own practicality. In keeping with these practicalities, the Activity Recognition takes the raw sensor reading as inputs and predicts the user’s motion activity. This review paper provides the review of Smartphone sensors that are use for activity recognition. Additionally provides the review of process of Activity Recognition task including the data mining techniques. And also provides review on real-life applications of human activity recognition.

Application of smartphone in recognition of human activities with machine learning

The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023

The aim of activity recognition is to determine the physical action being performed by one or more users based on a series of observations made during the user's actions in the relevant environment. Significant advancements in the field of human activity have resulted in the creation of novel ways for supporting elderly persons in doing their tasks independently. Using ambient computing, this type of service will be manageable. Many of services are provided by ambient technology, involving home automation tools, monitoring the behaviour of diseased individuals, and utility management. Numerous academics are focusing their efforts on computer software architectures, system infrastructure, and distributed applications utilising sensor devices. Aim of this project is to develop an algorithm that can perform human activity recognition (HAR) better than the existing state-of-the-art approach. Several tasks must be done to achieve this goal. To compete with an existing HAR system, this study will rely on secondary data from the cutting-edge experiment; no new data will be collected. The central experiment will be used to quantitatively identify the best classifier based on prediction accuracy. The current study entails monitoring and assessing existing literature in order to generate hypotheses that may be tested via experiment.

Human Activity Recognition Using Accelerometer and Gyroscope Sensors

International Journal of Engineering and Technology, 2017

Mobile phones are pervasive, moderately specialized gadgets that have an effective and capable handling power enveloped with smaller segments that can do efficient and powerful calculations. One of the components that is built into the mobile phones to make it more robust are the sensors. Mobile phones are encompassed with several sensors, for example, proximity sensors, temperature sensors, accelerometers, gyroscopes and many more. These sensors have opened up ways to different fields in data mining and data analytics. The existence of these sensors has empowered people to control its information to perform different tasks. One such task is movement detection which is termed as activity recognition. In this paper an existing dataset has been used which consists of 10 volunteers, wearing a pair of accelerometers and gyroscopes close to their right lower arm and a pair of accelerometers and gyroscopes close to their left ankle. The subjects are asked to perform 12 exercises which are standing still, sitting and relaxing, lying down, walking, climbing stairs, waist-bends forward, frontal elevation of arms, knees bending (crouching), cycling, jogging, running, jumping front & back. 11 features were separated for the raw data collected from the sensors. In this paper, a novel automated method for classification of human activities, using wearable sensors which are also found interfaced within most of the modern mobile phones, is developed. The features are extracted from the recordings of data from individual as well as combination of sensors. The publicly available dataset is used for experimentation. The extracted features are classified using six popular classifiers: K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Conditional Inference Tree (C-Tree), J48 and Random Forest (RF). The experimental results are tabulated and analyzed. Activity recognition turns out to be critical in distinguishing and sending fast data about irregular physical body developments of a person.

Human Activity Recognition on Smartphones using Machine Learning Algorithms

International Journal for Innovative Research in Science & Technology, 2018

Activity Recognition is one among the most imperative period at the back of various applications like human survey system, clinical investigation and it is a functioning examination subject in smart homes and smart health. Smart mobiles are outfitted with various worked in detecting sensors like a gyroscope, accelerometer, GPS sensor and compass sensor. We can structure a device to catch the condition of the user. Activity Recognition (AR) framework takes the unrefined sensor data from compact sensors as sources of information and assessments a human movement using data mining and machine learning systems. In this paper, we examine the execution of two sort calculations i.e. Random Forest (RF) and Modified Random Forest (MRF) in an online Activity Recognition framework running on Android frameworks and this technique can underpin online training and class the utilization of the accelerometer data most successfully. For the most part, first, we utilize the Random Forest classification algorithm related next we tend to use an improvement of Modified Random Forest i.e. MRF. For the rationale of Activity Recognition, Modified Random Forest will expel the computational complexities of the Random Forest through developing decision trees (creating littler preparing units for each activity and class may be done dependent on those diminished preparing sets). We will expect the general execution of these classifiers from a movement of observations on human movements like sitting, walking, running, resting and standing in an online activity recognition contraption. On this paper, we're proposed to break down the general execution of classifiers with constrained preparing records and confined open memory on the smart devices contrasted with offline.

Activity Recognition System using Smartphone

Human activity monitoring is becoming more automated gradually. In near future it would be required more advanced electronic systems to assess physical activities. Today‟s day most of the doctors and scientists use their memory or match data from text books for monitoring activities for checking dissimilarities of experimented data. Though for automated monitoring some digital modern instruments are working in some sophisticated places also. In this paper we will describe how to extract human walking features and patterns with Smartphone built in accelerometer. This will initiates the work to create model of different subjects of normal physique like short, average and long heights and walking of fast, medium and slow in speed. Walking features of healthy person can be compared with walking features of any patient with leg injury or some leg related diseases and result would give the nature of defects.

Human Activities Recognition Via Smartphones Using Supervised Machine Learning Classifiers

Primary Health Care Open Access, 2018

This paper presents a way of detecting twelve daily physical human activities such as sitting, laying, standing, attaching to table, walking, jogging, running, jumping, pushups, stairs down, going up stairs, and cycling with acceleration and gyroscope sensors data resulted from using android smart mobile phones. An android application was developed to collect raw data from the sensors. The subjects preformed the twelve activities with smart phones where it is installed. Five of the samples had been selected as train data, while the rest ten samples selected as test data. In order to classify the subjects' raw data, a program in Matlab R2016a was developed that applies twelve supervised classification algorithms models, and then compare between them in term of accuracy and speed factors. The twelve models are divided into two categories: Six of them under support vector machine (SVM); while the other six are under the k-nearest neighbor (k-NN). Finally, this study has the following results: The overall average accuracy rate with SVM cases is 89.79% in comparison with 87.81% for k-NN. The average speed rate is 47 seconds in SVM cases whereas it is 39 seconds in k-NN cases. With expansion of the number of activities up to 12 human actions, the result of the study showed that a good performance in terms of accuracy and speed was gained without losing an accuracy level achieved in the previous studies where maximum 8 activities were handled.

Smart Phone Based Data Mining for Human Activity Recognition

Procedia Computer Science, 2015

Automatic activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors, and permit continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in healthcare applications, for automatic and intelligent daily activity monitoring for elderly people. In this paper, we present novel data analytic scheme for intelligent Human Activity Recognition (AR) using smartphone inertial sensors based on information theory based feature ranking algorithm and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments with a publicly available database 1 of human activity with smart phone inertial sensors show that the proposed approach can indeed lead to development of intelligent and automatic real time human activity monitoring for eHealth application scenarios for elderly, disabled and people with special needs.

Human Activity Recognition Using Smartphone Sensors

Webology, Volume 18, Special Issue on Computing Technology and Information Management, September, 2021, 2021

In today’s digitalized world, smartphones are the devices which have become a basic and fundamental part of our life. Since, these greatest technology’s appearance, an uprising has been created in the industry of mobile communication. These greatest inventions of mankind are not just constricted for calling these days. As the capabilities and the number of smartphone users increase day by day, smartphones are loaded with various types of sensors which captures each and every moment, activities of our daily life. Two of such sensors are Accelerometer and Gyroscope which measures the acceleration and angular velocity respectively. These could be used to identify the human activities performed. Basically, Human Activity Recognition is a classifying activity with so many use cases such as health care, medical, surveillance and anti-crime securities. Smartphones have wide variety of applications in various fields and can be used to excavate different kinds of data which provide accurate insights and knowledge about the user's lifestyle. Nowadays creating lifelogs that is a technology to capture and record a user's life through his or her mobile devices, are becoming very important task. An immense issue in creating a detailed lifelog is the accurate detection of activities performed by human based on the collected data from the sensors. The data in the lifelogs has strong association with physical health variables. These data are motivational and they identify any type of behavioral changes. These data provide us the overall measure of physical activity. In this project, we have analyzed the smartphone sensors produced data and used them to recognize the activities performed by the user.