Activity Recognizer using Machine Learning Classifiers (original) (raw)
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Activity Recognition using Accelerometer Sensor and Machine Learning Classifiers
IEEE 14th International Colloquium on Signal Processing and Its Applications, 2018
Activity recognition is considered as an important task in many applications, particularly in healthcare services. Among these applications include medical diagnostic, monitoring of users' daily routine and detection of abnormal cases. This paper presents an approach for the activity recognition using an accelerometer sensor embedded in a smartphone. This approach uses a publicly available accelerometer dataset as the raw input signal. The features of the signal are selected based on the time and frequency domain. Then, Principal Component Analysis (PCA) is used to reduce the dimensionality of the features and extract the most significant ones that can classify human activities. A comparison process is performed between the original raw data and PCA-based features and additionally, time and frequency-domain features are also compared using several machine learning classifiers. The obtained results show that the PCA-based features obtain higher recognition rate while frequency-domain features have higher accuracy, with the rate of 96.11% and 92.10% respectively.
International Journal of Knowledge Based Computer Systems, 2020
Increasing use of accelerometer and protractor sensors in recent years has created a field of study for the definition of human activities. This issue is tried to be solved by using machine learning methods. For this, it is solved by extracting different properties from the obtained signals, obtaining the characteristics specific to the activity and classifying these properties. In this study, the time and frequency domain properties of 4 different human activities were extracted, then a pre-treatment step was applied in accordance with the obtained feature set, and then the size was reduced with PCA and Fisher 'LDA methods. The k-NN classifier and perceptron classifiers were designed for the obtained feature set and the classification process was performed. In this study, the classification success of these methods using different parameters has been examined and the results are shown.
IRJET- Activity Recognizer using Machine Learning Classifiers
IRJET, 2020
Activity Recognition plays a major role in many applications particularly in day to day life, healthcare services. Medical diagnosis, users daily routine monitoring, abnormal case detection seeks more attention. This paper put forwards the approach of using accelerometer sensor embedded in a smartphone which is very much available .The raw input used here is a signal with publically available accelerometer dataset. Time domain and frequency domain feature of the signal are selected here. Principal Component Analaysis. (PCA)used here reduce the dimensionality of features. Among many features, PCA extract most significant features which classify activities of humans. In order to get the accurate results a comparison is performed between original raw data and PCA features. Machine learning approach is provided where the Classifiers compare the time and frequency domain features. The finally obtained result shows high accuracy in frequency domain features and high recognition rate in PCA based features. Comparison technique is highly emphasized here which provides the best results of activity and recognize with high precision.
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 Using Accelerometer Data
IRJET, 2023
Human Activity Recognition (HAR) has a wide range of applications due to the widespread usage of acquisition devices such as smartphones and its ability to capture human activity data. The ability to retrieve deeply embedded information for precise detection and its interpretation has been transformed by breakthroughs in Artificial Intelligence (AI). In this paper, the time series dataset, acquired from Wireless Sensor Data Mining Lab (WISDM) Lab, is used to extract features of common human activities from a raw signal data of smartphone accelerometer. A 2D convolutional neural network is used to visualize the data.
Frequency domain approach for activity classification using accelerometer
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2008
Activity classification was performed using MEMS accelerometer and wireless sensor node for wireless sensor network environment. Three axes MEMS accelerometer measures body's acceleration and transmits measured data with the help of sensor node to base station attached to PC. On the PC, real time accelerometer data is processed for movement classifications. In this paper, Rest, walking and running are the classified activities of the person. Both time and frequency analysis was performed to classify running and walking. The classification of rest and movement is done using Signal magnitude area (SMA). The classification accuracy for rest and movement is 100%. For the classification of walk and Run two parameters i.e. SMA and Median frequency were used. The classification accuracy for walk and running was detected as 81.25% in the experiments performed by the test persons.
Human Activity Recognition Based on Smart Phone’s 3-Axis Acceleration Sensor
Lecture Notes in Computer Science, 2017
Human activity recognition aims to identify the activities carried out by a person. Recognition is possible by using information that is retrieved from numerous physiological signals by attaching sensors to the subject's body. Lately, sensors like accelerometer and gyroscope are built-in inside the Smartphone itself, which makes activity recognition very simple. To make the activity recognition system work properly in smartphone which has power constraint, it is very essential to use an optimization technique which can reduce the number of features used in the dataset with less time consumption. In this paper, we have proposed a dimensionality reduction technique called fast feature dimensionality reduction technique (FFDRT). A dataset (UCI HAR repository) available in the public domain is used in this work. Results from this study shows that the fast feature dimensionality reduction technique applied for the dataset has reduced the number of features from 561 to 66, while maintaining the activity recognition accuracy at 98.72% using random forest classifier and time consumption in dimensionality reduction stage using FFDRT is much below the state of the art techniques.
Human activity recognition system using smartphone based on machine learning algorithms
INNOVATIONS IN COMPUTATIONAL AND COMPUTER TECHNIQUES: ICACCT-2021
Human Activity Recognition System aims to capture the state of the user with respect to the external/heterogeneous environment in order to analyze the human health conditions with the help of the numerous sensors attached to the different body parts of the human 3. The first hand-held mobile was developed in the year 1979 and from that year to 2011 it is surveyed that around 80% of the world population is now using the Smartphone and from that year it is been observed that there is been a tremendous increase/up gradation in the field of technology and size also and in this futuristic world of technology. As we can see now a days Smartphone are playing a key role and are used to collect the data of human activities and which is further used to monitor the health of the person. Prior to collect the human activity data we use to attach the sensors to the different body parts like chest, hands, thighs, wrist etc. In this paper, with the help of the m ach in e learn in g algorithms and the data mining techniques we are going to analyze the posture by keeping in mind about the attributes which are going to take part. We are using six of the attributes with the help of these attributes we are going to predict the conditions which are as follows: Sitting, Standing, Walking, Walking Upstairs, Walking downstairs and Laying.
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.
A Comprehensive Study of Activity Recognition Using Accelerometers
This paper serves a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in exp...