scholarly journals Human Activity Recognition Using Smart phones

2020 ◽  
Vol 6 (12) ◽  
pp. 445-448
Author(s):  
Aman Gupta and Nidhi Senger
Keyword(s):  
Activity Recognition ◽  
Daily Living ◽  
Inertial Sensors ◽  
Research Field ◽  
Smart Phones ◽  
Body Motion ◽  
Support Vector ◽  
On Line ◽  
Multiclass Support Vector Machine ◽  
Infor Mation

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context infor-mation about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repos-itory. Results, obtained on the dataset by exploiting a multiclass Support Vector Machine (SVM), are also acknowledged.

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.

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 Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 768
Author(s):  
Caetano Mazzoni Ranieri ◽  
Scott MacLeod ◽  
Mauro Dragone ◽  
Patricia Amancio Vargas ◽  
Roseli Aparecida Francelin Romero 
Keyword(s):  
Activity Recognition ◽  
Assisted Living ◽  
Daily Living ◽  
Smart Home ◽  
Inertial Sensors ◽  
Ambient Assisted Living ◽  
Sensor Data ◽  
University Of Texas ◽  
Demographic Projections ◽  
The University

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot’s RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.

Image Classification for Steel Strip Surface Defects Based on Support Vector Machines

2011 ◽  
Vol 217-218 ◽  
pp. 336-340 ◽  
Author(s):  
Yong Wei Yu ◽  
Guo Fu Yin ◽  
Liu Qing Du
Keyword(s):  
Image Classification ◽  
Surface Defects ◽  
Steel Strip ◽  
Support Vector ◽  
Testing Time ◽  
Strip Surface ◽  
On Line ◽  
Multiclass Support Vector Machine ◽  
Multiclass Svm ◽  
Number Of Classes

In order to realize less time consuming and on-line image classification for steel strip surface defects, an improved multiclass support vector machine (SVM) was proposed. The SVM used a novel algorithm and only constructed (k-1) two-class SVMs where K is the number of classes. In the testing phase, to identify the surface defects it used a new unidirectional acyclic graph which had internal (k-1) nodes and k leaves. Its testing time is less than traditional multiclass SVM method. The experiment results shows that this method is simple and less time consuming while preserving generalization ability and recognition accuracy toward steel strip surface defects.

scholarly journals Fall Detection and Daily Living Activity Recognition using Machine Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 328-332
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Angular Velocity ◽  
Activity Recognition ◽  
Daily Living ◽  
Fall Detection ◽  
Classification Algorithm ◽  
Support Vector ◽  
Velocity Data ◽  
Daily Living Activity

Elder people are increasing all over the world as a result certain fall occur in their daily life. This fall lead to several severe problems. The fall may often causes injuries and in many cases it result in death of the individual. The problem should be addressed to reduce the fall. By using some Machine Learning(ML) algorithm the fall and daily living activities are recognized. The acceleration and angular velocity data obtained from the dataset are used to detect the fall and daily living activity. Body movement of the person are collected and stored in the dataset. Acceleration and angular velocity data are used to extract the time and frequency domain feature and provide them to classification algorithm. Here, Logistic regression algorithm is used for detecting the fall and living activity. It is very effective algorithm and does not require too many computational resources. It is easy to regularize and provide well calibrated predicted probabilities as output. The sensitivity, accuracy and specificity of fall detection and activity recognition is obtained as a result. The performance evaluation is made with three classification algorithm. The three classification algorithm are Artificial neural network (ANN), K-nearest neighbours (KNN), Quadratic support vector machine (QSVM). Logistic regression provides highest accuracy compared with other three algorithm.

scholarly journals Real-time full body motion imitation on the COMAN humanoid robot

Robotica ◽  
2014 ◽  
Vol 33 (5) ◽  
pp. 1049-1061 ◽  
Author(s):  
Andrej Gams ◽  
Jesse van den Kieboom ◽  
Florin Dzeladini ◽  
Aleš Ude ◽  
Auke Jan Ijspeert
Keyword(s):  
Real Time ◽  
Humanoid Robot ◽  
Center Of Mass ◽  
Stability Control ◽  
Body Motion ◽  
Support Vector ◽  
Time Motion ◽  
Vector Machines ◽  
On Line ◽  
Full Body

SUMMARYOn-line full body imitation with a humanoid robot standing on its own two feet requires simultaneously maintaining the balance and imitating the motion of the demonstrator. In this paper we present a method that allows real-time motion imitation while maintaining stability, based on prioritized task control. We also describe a method of modified prioritized kinematic control that constrains the imitated motion to preserve stability only when the robot would tip over, but does not alter the motions otherwise. To cope with the passive compliance of the robot, we show how to model the estimation of the center of mass of the robot using support vector machines. In the paper we give detailed description of all steps of the algorithm, essentially providing a tutorial on the implementation of kinematic stability control. We present the results on a child-sized humanoid robot called Compliant Humanoid Platform or COMAN. Our implementation shows reactive and stable on-line motion imitation of the humanoid robot.

scholarly journals Adaptive Accumulation of Plantar Pressure for Ambulatory Activity Recognition and Pedestrian Identification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3842
Author(s):  
Phuc Huu Truong ◽  
Sujeong You ◽  
Sang-Hoon Ji ◽  
Gu-Min Jeong
Keyword(s):  
Activity Recognition ◽  
Plantar Pressure ◽  
High Performance ◽  
Support Vector ◽  
Ambulatory Activity ◽  
One Step ◽  
Multiclass Support Vector Machine ◽  
Ambulatory Activities ◽  
Comparative Results ◽  
Step Over

In this paper, we propose a novel method for ambulatory activity recognition and pedestrian identification based on temporally adaptive weighting accumulation-based features extracted from categorical plantar pressure. The method relies on three pressure-related features, which are calculated by accumulating the pressure of the standing foot in each step over three different temporal weighting forms. In addition, we consider a feature reflecting the pressure variation. These four features characterize the standing posture in a step by differently weighting step pressure data over time. We use these features to analyze the standing foot during walking and then recognize ambulatory activities and identify pedestrians based on multilayer multiclass support vector machine classifiers. Experimental results show that the proposed method achieves 97% accuracy for the two tasks when analyzing eight consecutive steps. For faster processing, the method reaches 89.9% and 91.3% accuracy for ambulatory activity recognition and pedestrian identification considering two consecutive steps, respectively, whereas the accuracy drops to 83.3% and 82.3% when considering one step for the respective tasks. Comparative results demonstrated the high performance of the proposed method regarding accuracy and temporal sensitivity.

scholarly journals Assessing Cellulose Micro/Nanofibre Morphology Using a High Throughput Fibre Analysis Device to Predict Nanopaper Performance

2021 ◽  
Author(s):  
Jordan Pennells ◽  
Bérénice Heuberger ◽  
Céline Chaléat ◽  
Darren J. Martin
Keyword(s):  
High Throughput ◽  
Principal Component ◽  
Research Field ◽  
Morphological Data ◽  
Support Vector ◽  
Grand Challenge ◽  
Fibre Width ◽  
On Line ◽  
Fibre Analysis ◽  
Fibre Morphology

Abstract Characterising cellulose nanofibre (CNF) morphology has been identified as a grand challenge for the nanocellulose research field. Direct techniques for CNF morphology characterisation exhibit various difficulties related to the material network structure and equipment cost, while indirect techniques that investigate fibre-light interaction, fibre-solvent interaction, fibre-fibre interaction, or specific fibre surface area involve relatively facile methods but may be more unreliable. Nanopaper mechanical testing is a prevalent metric for assessing fibre-fibre interaction, but is an off-line, time-consuming, and destructive methodology. In this study, an optical fibre morphology analyser (MorFi, TechPap) was employed as an on-line, high throughput, fast turnaround tool to assess micro/nanofibre pulp morphology and predict the properties of nanopaper material. Correlation analysis identified fibre content and fibre kink properties as most correlated with nanopaper strength and toughness, while fibre width and coarseness were most inversely correlated with nanopaper performance. Principal component analysis (PCA) was employed to visualise interdependent morphological and mechanical data. Subsequently, two data driven statistical models - multiple linear regression (MLR) and machine learning based support vector regression (SVR) - were established to predict nanopaper properties from fibre morphology data, with SVR generating a more accurate prediction across all nanopaper properties (NRMSE = 0.13-0.33) compared to the MLR model (NRMSE = 0.33-0.51). This study highlights that statistical methods are useful to disentangle and visualise interdependent morphological data from an on-line fibre analysis device, while regression models are also capable of predicting paper mechanical properties from CNF samples even though these devices do not operate at nanoscale resolution.

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.

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