scholarly journals Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4132 ◽  
Author(s):  
Ku Ku Abd. Rahim ◽  
I. Elamvazuthi ◽  
Lila Izhar ◽  
Genci Capi
Keyword(s):  
Activity Recognition ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Ensemble Methods ◽  
Daily Activities ◽  
Support Vector ◽  
Random Subspace ◽  
Ensemble Classifiers ◽  
Accuracy Rate

Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

scholarly journals Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 167
Author(s):  
Ho Sharon ◽  
Irraivan Elamvazuthi ◽  
Cheng-Kai Lu ◽  
S. Parasuraman ◽  
Elango Natarajan
Keyword(s):  
Rheumatoid Arthritis ◽  
Wearable Sensors ◽  
Joint Destruction ◽  
Infrared Camera ◽  
Ensemble Methods ◽  
Image Sensor ◽  
Random Subspace ◽  
Ensemble Classifiers ◽  
Accuracy Rate ◽  
Medical Disorders

Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints’ muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem with several numerical features. Three ensemble algorithms such as bagging, Adaboost, and random subspace were used in the study. These ensemble classifiers use k-NN (K-nearest neighbours) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. The assessment was based on set of performance measures such as precision, recall, F-measure, and receiver operating characteristic (ROC) curve. The performance was also measured based on the comparison of the overall classification accuracy rate between different ensembles classifiers and the base learners. Overall, it was found that for Dataset 1, random subspace classifier with k-NN shows the best results in terms of overall accuracy rate of 97.50% and for Dataset 2, bagging-RF shows the highest overall accuracy rate of 94.84% over different ensemble classifiers. The findings indicate that the efficiency of the base classifiers with ensemble classifier have substantially improved.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4189 ◽  
Author(s):  
Samanta Rosati ◽  
Gabriella Balestra ◽  
Marco Knaflitz
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Wearable Sensors ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Time Frequency ◽  
Security Applications ◽  
Biomechanical Behavior ◽  
Area Of Interest ◽  
Open Point

Human Activity Recognition (HAR) refers to an emerging area of interest for medical, military, and security applications. However, the identification of the features to be used for activity classification and recognition is still an open point. The aim of this study was to compare two different feature sets for HAR. Particularly, we compared a set including time, frequency, and time-frequency domain features widely used in literature (FeatSet_A) with a set of time-domain features derived by considering the physical meaning of the acquired signals (FeatSet_B). The comparison of the two sets were based on the performances obtained using four machine learning classifiers. Sixty-one healthy subjects were asked to perform seven different daily activities wearing a MIMU-based device. Each signal was segmented using a 5-s window and for each window, 222 and 221 variables were extracted for the FeatSet_A and FeatSet_B respectively. Each set was reduced using a Genetic Algorithm (GA) simultaneously performing feature selection and classifier optimization. Our results showed that Support Vector Machine achieved the highest performances using both sets (97.1% and 96.7% for FeatSet_A and FeatSet_B respectively). However, FeatSet_B allows to better understand alterations of the biomechanical behavior in more complex situations, such as when applied to pathological subjects.

Human body activity recognition using wearable inertial sensors integrated with a feature extraction–based machine-learning classification algorithm

2020 ◽  
pp. 095440542093789
Author(s):  
Chih-Ta Yen ◽  
Jia-De Lin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Activity Recognition ◽  
Inertial Sensors ◽  
Recognition Accuracy ◽  
Daily Activities ◽  
Linear Discriminant ◽  
Wearable Inertial Sensors

This study employed wearable inertial sensors integrated with an activity-recognition algorithm to recognize six types of daily activities performed by humans, namely walking, ascending stairs, descending stairs, sitting, standing, and lying. The sensor system consisted of a microcontroller, a three-axis accelerometer, and a three-axis gyro; the algorithm involved collecting and normalizing the activity signals. To simplify the calculation process and to maximize the recognition accuracy, the data were preprocessed through linear discriminant analysis; this reduced their dimensionality and captured their features, thereby reducing the feature space of the accelerometer and gyro signals; they were then verified through the use of six classification algorithms. The new contribution is that after feature extraction, data classification results indicated that an artificial neural network was the most stable and effective of the six algorithms. In the experiment, 20 participants equipped the wearable sensors on their waists to record the aforementioned six types of daily activities and to verify the effectiveness of the sensors. According to the cross-validation results, the combination of linear discriminant analysis and an artificial neural network was the most stable classification algorithm for data generalization; its activity-recognition accuracy was 87.37% on the training data and 80.96% on the test data.

A New Optimal Ensemble Algorithm Based on SVDD Sampling for Imbalanced Data Classification

2020 ◽  
pp. 2150020
Author(s):  
Jamshid Pirgazi ◽  
Abbas Pirmohammadi ◽  
Reza Shams
Keyword(s):  
Imbalanced Data ◽  
Ensemble Methods ◽  
Data Classification ◽  
Admissible Solution ◽  
Optimal Number ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
Algorithm Optimization ◽  
Optimal Ensemble ◽  
Imbalanced Data Classification

Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness.

Research on the Taxonomy of Activity Recognition Based on Inertial Sensors

2013 ◽  
Vol 823 ◽  
pp. 107-110
Author(s):  
Zi Ming Xiao ◽  
Yu Long Shi ◽  
Yong Xue ◽  
Feng Hu ◽  
Yu Chuan Wu
Keyword(s):  
Activity Recognition ◽  
Inertial Sensors ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Future Research ◽  
Support Vector ◽  
Bayesian Decision ◽  
Classification Techniques ◽  
Vector Machines ◽  
Bayesian Decision Making

This paper introduces some techniques on classifying human activities with inertial sensors and point out a number of characteristics of classification algorithm. The goal of human activity recognition is to automatically analyze ongoing activities from people who wear inertial sensor. Initially, we provide introduce information about the activity recognition, such as the way of acquisition, sensors used and the steps of activity recognition using machine learning algorithm. Next, we focus on the classification techniques together with a detailed taxonomy, and the classification techniques implemented and compared in this study are: Decision Tree Algorithm (DTA), Bayesian Decision Making (BDM), Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Hidden Markov Model (HMM)[. Finally, we make a summarize about it investigate the directions for future research.

scholarly journals Analyzing performance of classifiers for medical datasets

2018 ◽  
Vol 7 (2.15) ◽  
pp. 136 ◽  
Author(s):  
Rosaida Rosly ◽  
Mokhairi Makhtar ◽  
Mohd Khalid Awang ◽  
Mohd Isa Awang ◽  
Mohd Nordin Abdul Rahman
Keyword(s):  
Breast Cancer ◽  
Cross Validation ◽  
Ensemble Methods ◽  
Data Sets ◽  
Ensemble Classifiers ◽  
Classification Models ◽  
Data Set ◽  
Mining Tool ◽  
Fold Cross Validation

This paper analyses the performance of classification models using single classification and combination of ensemble method, which are Breast Cancer Wisconsin and Hepatitis data sets as training datasets. This paper presents a comparison of different classifiers based on a 10-fold cross validation using a data mining tool. In this experiment, various classifiers are implemented including three popular ensemble methods which are boosting, bagging and stacking for the combination. The result shows that for the classification of the Breast Cancer Wisconsin data set, the single classification of Naïve Bayes (NB) and a combination of bagging+NB algorithm displayed the highest accuracy at the same percentage (97.51%) compared to other combinations of ensemble classifiers. For the classification of the Hepatitisdata set, the result showed that the combination of stacking+Multi-Layer Perception (MLP) algorithm achieved a higher accuracy at 86.25%. By using the ensemble classifiers, the result may be improved. In future, a multi-classifier approach will be proposed by introducing a fusion at the classification level between these classifiers to obtain classification with higher accuracies.  

An Improved Three-Stage Classifier for Activity Recognition

2017 ◽  
Vol 32 (01) ◽  
pp. 1860003 ◽  
Author(s):  
Enrique Garcia-Ceja ◽  
Ramon F. Brena
Keyword(s):  
Activity Recognition ◽  
Cross Validation ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Research Area ◽  
Classification Model ◽  
Homogeneous Groups ◽  
Wide Range ◽  
Important Research Area ◽  
Selection Of

Recently, Human Activity Recognition (HAR) has become an important research area because of its wide range of applications in several domains such as health care, elder care, sports monitoring systems, etc. The use of wearable sensors — specifically the use of inertial sensors such as accelerometers and gyroscopes — has become the most common approach to recognize physical activities because of their unobtrusiveness and ubiquity. Overall, the process of building a HAR system starts with a feature extraction phase and then a classification model is trained. In the work of Siirtola et al. is proposed an intermediate clustering step to find the homogeneous groups of activities. For the recognition step, an instance is assigned to one of the groups and the final classification is performed inside that group. In this work we evaluate the clustering-based approach for activity classification proposed by Siirtola with two additional improvements: automatic selection of the number of groups and an instance reassignment procedure. In the original work, they evaluated their method using decision trees on a sports activities dataset. For our experiments, we evaluated seven different classification models on four public activity recognition datasets. Our results with 10-fold Cross Validation showed that the method proposed by Siirtola with our additional two improvements performed better in the majority of cases as compared to using the single classification model under consideration. When using Leave One User Out Cross Validation (user independent model) we found no differences between the proposed method and the single classification model.

scholarly journals Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation

2019 ◽  
Vol 9 (14) ◽  
pp. 2795 ◽  
Author(s):  
Ahmed Baraka ◽  
Heba Shaban ◽  
Mohamad Abou El-Nasr ◽  
Omneya Attallah
Keyword(s):  
Confusion Matrix ◽  
Wearable Sensors ◽  
Human Locomotion ◽  
Measurement Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Tree Classifier ◽  
Different Types

Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their daily life activities, but also provide quantitative, meaningful feedback data of patients to their therapists. This can pinpoint the cause of problems and help in maximizing their recovery rates. The popularity of using wearable sensors has received attention from a number of researchers from both the academic and industrial fields in the past few years. The different types of wearable sensors have given birth to the realization of a standard measurement model that can support different types of applications. Wireless body area networks (WBANs) are starting to replace traditional healthcare systems by enabling long-term monitoring of patients and tele-rehabilitation, especially those who suffer from chronic diseases. This paper investigates using wearable accelerometers and surface electromyography (EMG) in human locomotion monitoring for tele-rehabilitation. It proposes and investigates new positions for the proposed sensors, and compares the measured signals to similar techniques proposed in the literature. Realistic measurements show that the proposed positions of surface EMG sensors (on the forearm muscles) provide more reliable results in the classification of motion abnormality as compared to the sensor positions proposed in the literature (biceps muscles). Seven statistical features were extracted from accelerometer signals, and four time domain (TD) features are extracted from EMG signals. These features are used to construct six machine learning classifiers for automatic classification of Parkinson’s tremor. These models include; decision tree (DT), linear discriminant analysis analysis (LDA), k-nearest-neighbor (kNN), support vector machine (SVM), boosted tree and bagged tree classifiers. The performance of the applied classifiers is analyzed using accuracy, confusion matrix, and area under ROC (AUC) curve. The results are also compared to corresponding findings in the literature. The experimental results show that the highest classification accuracy is achieved when using the proposed measurement set and bagged tree classifier with a value of 99.6%.

scholarly journals Wearable Internet-of-Things platform for human activity recognition and health care

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772091156 ◽  
Author(s):  
Asif Iqbal ◽  
Farman Ullah ◽  
Hafeez Anwar ◽  
Ata Ur Rehman ◽  
Kiran Shah ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Wearable Sensors ◽  
Recognition Rate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Data Set

We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.

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