1,601 research outputs found

    Multi-sensor activity recognition of an elderly person.

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    The rapid increase in the number of ageing population brings major issues to health care including a rise in care cost, high demand in long- term care, burden to caregivers, and insufficient and ineffective care. Activity recognition can be used as the key part of the intelligent sys- tems to allow elderly people to live independently at homes, reduce care cost and burden to the caregivers, provide assurance for the fam- ilies, and promote better care. However, current activity recognition systems mainly focus on the technical aspect i.e. systems accuracy and neglects the practical aspects such as acceptance, usability, cost and privacy. The practicality of the system is the vital indication whether the system will be adopted. This research aims to develop the activity recognition system which considers both practical and technical aspects using multiple wrist-worn sensors. An extensive literature review in wearable sensor based activity recog- nition and its applications in healthcare have been carried out. Novel multi-sensor activity recognition utilising multiple low-cost, non-intrusive, non-visual wearable sensors is proposed. The sensor fusion is per- formed at feature and classi er levels using the proposed feature se- lection and classi er combination techniques. The multi-sensor ac- tivity recognition data sets have been collected. The rst data set contains data from accelerometer collected from seven young adults. The second data set contains data from accelerometer, altimeter, and temperature sensor collected from 12 elderly people in home environ- ment performing 10 activities. The third data set contains sensor data from accelerometer, gyroscope, temperature sensor, altimeter, barometer, and light sensor worn on the users wrist and a heart rate monitor worn over the users chest. The data set is collected from 12 elderly persons in a real home environment performing 13 activities. This research proposes two feature selection methods, Feature Com- bination (FC) and Maximal Relevancy and Maximal Complementary (MRMC), based on the relationship between feature and classes as well as the relationship between a group of features and classes. The experimental studies show that the proposed techniques can select an optimum set of features from irrelevant, overlapped, and partly over- lapped features. The studies also show that FC and MRMC obtain higher classi cation performances than popular techniques including MRMR, NMIFS, and Clamping. Two classi er combination tech- niques based on Genetic Algorithm (GA) are proposed. The rst technique called GA based Fusion Weight (GAFW), uses GA nd the optimum fusion weights. The results indicate that 99% of classi er fusion using GAFW achieves equal or higher accuracy than using only the best classi er. While other fusion weight techniques cannot guar- antee accuracy improvement, GAFW is a more suitable method for determining fusion weight regardless which fusion techniques are used. Another algorithm called GA based Combination Model (GACM) is proposed to nd the optimal combination between classi er, weight function, and classi er combiners. The algorithm does not only nd the model which has the minimum classi cation error but also select the one that is simpler. Other criteria e.g. select the classi er with low computation can also be easily added to the algorithm. The re- sults show that in general GACM can nd the optimum combinations automatically. The comparison against manually selection revealed that there is no statistical signi cant in the performances. Applications of the proposed work in home care and decision support system are discussed The results of this research will have a signi cant impact on the future health care where people can be health monitored from their homes to promote healthy living, detect any changes in behaviour, and improve quality of care

    Affect Recognition Using Electroencephalography Features

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    Affect is the psychological display of emotion often described with three principal dimensions: 1) valence 2) arousal and 3) dominance. This thesis work explores the ability of computers to recognize human emotions using Electroencephalography (EEG) features. The development of computer systems to classify human emotions using physiological signals has recently gained pace in the research and technological community. This is because by using EEG to analyze the cognitive state one will be able to establish a direct communication channel between a computer and the human brain. Other applications of recognizing the affective states from EEG include identifying stress and cognitive workload on individuals and assist them in relaxation. This thesis is an extensive study on the design of paradigms that help computer systems recognize emotional states given a multichannel Electroencephalogram (EEG) segment. The process of first extracting features from the EEG signals using signal processing and then constructing a predictive model via machine learning is often referred to as paradigms. In this work, we will first present a brief review of the state-of-the-art paradigms that have contributed to the topic of emotional affect recognition. Then the proposed paradigms to recognize the principal dimensions of affect are detailed. Feature selection is also performed in order to select the relevant features. The evaluation of the models created to predict the affective states will be performed quantitatively by calculating the generalization accuracy and qualitatively by interpreting them

    A Survey on Feature Selection Algorithms

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    One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

    Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow

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    Producción CientíficaThe purpose of this study is to evaluate the usefulness of the boosting algorithm AdaBoost (AB) in the context of the sleep apnea-hypopnea syndrome (SAHS) diagnosis. Methods: We characterize SAHS in single-channel airflow (AF) signals from 317 subjects by the extraction of spectral and non-linear features. Relevancy and redundancy analyses are conducted through the fast correlation-based filter (FCBF) to derive the optimum set of features among them. These are used to feed classifiers based on linear discriminant analysis (LDA) and classification and regression trees (CART). LDA and CART models are sequentially obtained through AB, which combines their performances to reach higher diagnostic ability than each of them separately. Results: Our AB-LDA and AB-CART approaches showed high diagnostic performance when determining SAHS and its severity. The assessment of different apnea-hypopnea index cutoffs using an independent test set derived into high accuracy: 86.5% (5 events/h), 86.5% (10 events/h), 81.0% (15 events/h), and 83.3% (30 events/h). These results widely outperformed those from logistic regression and a conventional event-detection algorithm applied to the same database. Conclusion: Our results suggest that AB applied to data from single-channel AF can be useful to determine SAHS and its severity. Significance: SAHS detection might be simplified through the only use of single-channel AF data.Ministerio de Economía y Competitividad (project TEC2011-22987)Junta de Castilla y León (project VA059U13

    Enhanced context-aware framework for individual and crowd condition prediction

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    Context-aware framework is basic context-aware that utilizes contexts such as user with their individual activities, location and time, which are hidden information derived from smartphone sensors. These data are used to monitor a situation in a crowd scenario. Its application using embedded sensors has the potential to monitor tasks that are practically complicated to access. Inaccuracies observed in the individual activity recognition (IAR) due to faulty accelerometer data and data classification problem have led to its inefficiency when used for prediction. This study developed a solution to this problem by introducing a method of feature extraction and selection, which provides a higher accuracy by selecting only the relevant features and minimizing false negative rate (FNR) of IAR used for crowd condition prediction. The approach used was the enhanced context-aware framework (EHCAF) for the prediction of human movement activities during an emergency. Three new methods to ensure high accuracy and low FNR were introduced. Firstly, an improved statistical-based time-frequency domain (SBTFD) representing and extracting hidden context information from sensor signals with improved accuracy was introduced. Secondly, a feature selection method (FSM) to achieve improved accuracy with statistical-based time-frequency domain (SBTFD) and low false negative rate was used. Finally, a method for individual behaviour estimation (IBE) and crowd condition prediction in which the threshold and crowd density determination (CDD) was developed and used, achieved a low false negative rate. The approach showed that the individual behaviour estimation used the best selected features, flow velocity estimation and direction to determine the disparity value of individual abnormality behaviour in a crowd. These were used for individual and crowd density determination evaluation in terms of inflow, outflow and crowd turbulence during an emergency. Classifiers were used to confirm features ability to differentiate individual activity recognition data class. Experimenting SBTFD with decision tree (J48) classifier produced a maximum of 99:2% accuracy and 3:3% false negative rate. The individual classes were classified based on 7 best features, which produced a reduction in dimension, increased accuracy to 99:1% and had a low false negative rate (FNR) of 2:8%. In conclusion, the enhanced context-aware framework that was developed in this research proved to be a viable solution for individual and crowd condition prediction in our society

    Sensor fusion in smart camera networks for ambient intelligence

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    This short report introduces the topics of PhD research that was conducted on 2008-2013 and was defended on July 2013. The PhD thesis covers sensor fusion theory, gathers it into a framework with design rules for fusion-friendly design of vision networks, and elaborates on the rules through fusion experiments performed with four distinct applications of Ambient Intelligence

    Gazo bunseki to kanren joho o riyoshita gazo imi rikai ni kansuru kenkyu

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    制度:新 ; 報告番号:甲3514号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2012/2/8 ; 早大学位記番号:新585
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