Download or read book Predicting Activity Type from Accelerometer Data written by Yonglei Zheng and published by . This book was released on 2012 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of physical activity is important in improving people's health as it can help people understand the relationship between physical activity and health. Accelerometers, due to its small size, low cost, convenience and its ability to provide objective information about the frequency, intensity, and duration of physical activity, has become the method of choice in measuring physical activity. Machine learning algorithms based on the featurized representation of accelerometer data have become the most widely used approaches in physical activity prediction. To improve the classification accuracy, this thesis first explored the impact of the choice of data (raw vs processed) as well as the choice of features on the performance of various classifiers. The empirical results showed that the machine learning algorithms with strong regularization capabilities always performed better if provided with the most comprehensive feature set extracted from raw accelerometer signal. Based on the hypothesis that for some time series, the most discriminative information could be found at subwindows of various sizes, the Subwindow Ensemble Model (SWEM) was proposed. The SWEM was designed for the accelerometer-based physical activity data, and classified the time series based on the features extracted from subwindows. It was evaluated on six time series datasets. Three of them were accelerometer-based physical activity data, which the SWEM was designed for, and the rest were different types of time series data chosen from other domains. The empirical results indicated a strong advantage of the SWEM over baseline models on the accelerometerbased physical activity data. Further analysis confirmed the hypothesis that the most discriminative features could be extracted from subwindows of different sizes, and they were effectively used by the SWEM.

Download or read book Activity Learning written by Diane J. Cook and published by John Wiley & Sons. This book was released on 2015-02-23 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: Discovering activity patterns that emerge from behavior-based sensor data Recognizing occurrences of predefined or discovered activities in real time Predicting the occurrences of activities The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use. With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.

Download or read book Development and Validation of Accelerometer based Activity Classification Algorithms for Older Adults written by Jeffer Eidi Sasaki and published by . This book was released on 2014 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning algorithms to classify activity type from wearable accelerometers are important to improve our understanding of the relationship between physical activity (PA) and risk for physical disability in older adults. Therefore, the main objective of this dissertation was to develop and evaluate machine learning algorithms to predict activity type and intensity in older adults from a commercially available accelerometer (ActiGraph GT3X+). In Study 1, we developed machine learning algorithms to classify activity type and intensity from raw accelerometer data in older adults. Thirty-five older adults performed an activity routine comprised of different activities (5 min/activity) while wearing three ActiGraph GT3X+ activity monitors (dominant hip, wrist, and ankle) and a portable metabolic system. Accelerometer and steady-state metabolic data were used to develop artificial neural network, random forest, and support vector machine algorithms (ANNLab, RFLab, and SVMLab) to predict activity type and intensity in older adults using 20 s classification intervals. Classification accuracy of the models in detecting five activity categories ranged from 87% (ANNLab hip, RFLab hip, and SVMLab hip) to 96% (SVMLab wrist). The biases and root mean squared errors (RMSE) for predicted METs ranged from -0.01 MET (RMSE: 0.54 MET) for the RFLab wrist algorithm to 0.02 MET (RMSE: 0.67 MET) for the ANNLab hip algorithm. Study 2 evaluated the performance of the RFLab and SVMLab algorithms for predicting activity type in free-living conditions. Fifteen participants from Study 1 were observed for 2-3 h in their free-living environment while wearing three ActiGraph GT3X+ activity monitors (dominant hip, wrist, and ankle). The RFLab and SVMLab - algorithms were applied to hip, wrist, and ankle accelerometer data to classify five activity categories. Direct observation of activity type and duration served as criterion measures to evaluate percent correct classification rates of the algorithms. Correct classification rates ranged from 49% (SVMLab hip, SVMLab wrist, and RFLab wrist) to 55% (SVMLab ankle). New RF and SVM algorithms were developed using free-living accelerometer data (RFFL and SVMFL) and different classification intervals were also applied. Correct classification of activity types for the RFFL and SVMFL ranged from 53% (SVMFL wrist, 5 s classification intervals) to 71% (SVMFL ankle, 30 s classification intervals). Overall correct classification rates of up to 76% (RFFL hip and RFFL ankle, 30 s classification intervals) were achieved when classifying only three activity categories. Our machine learning algorithms accurately predict activity type from accelerometer data in older adults under 'laboratory conditions' but not in free-living conditions. We were able to improve free-living classification accuracy using algorithms developed under free-living conditions. Further refinement of the algorithms is required for achieving sufficient accuracy in classifying activity type in free-living older adults.

Download or read book Recursive Forecasting and Ordinal Statistical Models from Accelerometer Data written by Fatimah Alawad and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accelerometers are devices that measure acceleration along x-, y- and z-axes. These devices can be worn and used to predict activity intensity. The accuracy of conventional accelerometer analysis methods is sub-optimal but newer, more advanced methods that use raw data from the accelerometer for the prediction of activity intensity have been developed. As responses are correlated sequentially and collected over time, time-series methods can be considered to improve prediction accuracy. Prior responses, however, are not available at the testing stage or in practice. However, in testing, prior predictions can be used as in place of lagging responses on models which were built to use lagging responses as observations. This approach is known as recursive forecasting and applying it to accelerometer data is a unique approach in the literature. In addition, until recently, decision models for accelerometer data did not take into account the ordinality of the responses (for example, sedentary, moderate, and vigorous). This is significant information that we consider in this thesis. In this research, we develop more accurate decision models for predicting activity intensity from accelerometer data by using recursive forecasting. We also consider ordinal statistical models. Measuring activity intensity objectively is a crucial consideration in physiology and exercise science and these methods can be implemented in these disciplines to improve such measurement.

Download or read book IoT Sensor Based Activity Recognition written by Md Atiqur Rahman Ahad and published by Springer Nature. This book was released on 2020-07-30 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offer clear descriptions of the basic structure for the recognition and classification of human activities using different types of sensor module and smart devices in e.g. healthcare, education, monitoring the elderly, daily human behavior, and fitness monitoring. In addition, the complexities, challenges, and design issues involved in data collection, processing, and other fundamental stages along with datasets, methods, etc., are discussed in detail. The book offers a valuable resource for readers in the fields of pattern recognition, human–computer interaction, and the Internet of Things.

Download or read book Deep Learning for Time Series Forecasting written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-08-30 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.

Download or read book ESSA s Student Manual for Health Exercise and Sport Assessment written by Jeff S. Coombes and published by Elsevier Health Sciences. This book was released on 2020-08-01 with total page 622 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Static and Dynamic Posture practical New Test Accuracy, Reliability and Validity practical New activities reflecting recent advances in the field Increased focus on the interpretation, feedback and discussion of the data collected during the assessment with the participant

Download or read book Advances in Machine Learning Research and Application 2012 Edition written by and published by ScholarlyEditions. This book was released on 2012-12-26 with total page 1934 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Machine Learning Research and Application / 2012 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Machine Learning. The editors have built Advances in Machine Learning Research and Application / 2012 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Machine Learning in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Advances in Machine Learning Research and Application / 2012 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Download or read book Activity Recognition and Prediction for Smart IoT Environments written by Michele Ianni and published by Springer Nature. This book was released on with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Emerging Technologies to Promote and Evaluate Physical Activity written by Dan J Graham and published by Frontiers E-books. This book was released on 2014-10-23 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Increasingly, efforts to promote and measure physical activity are achieving greater precision, greater ease of use, and/or greater scope by incorporating emerging technologies. This is significant for physical activity promotion because more precise measurement will allow investigators to better understand where, when, and how physical activity is and is not occurring, thus enabling more effective targeting of particular behavior settings. Emerging technologies associated with the measurement and evaluation of physical activity are noteworthy because: (1) Their ease of use and transferability can greatly increase external validity of measures and findings; (2) Technologies can significantly increase the ability to analyze patterns; (3) They can improve the ongoing, systematic collection and analysis of public health surveillance due to real-time capabilities associated with many emerging technologies; (4) There is a need for research and papers about the cyberinfrastructure required to cope with big data (multiple streams, processing, aggregation, visualization, etc.); and (5) Increasingly blurred boundaries between measurement and intervention activity (e.g., the quantified-self /self-tracking movement) may necessitate a reevaluation of the conventional scientific model for designing and evaluating these sorts of studies. There have been many recent, disparate advances related to this topic. Advances such as crowdsourcing allow for input from large, diverse audiences that can help to identify and improve infrastructure for activity (e.g., large group identification of environmental features that are conducive or inhibiting to physical activity on a national and even global scale). Technologies such as Global Positioning Systems (GPS) and accelerometry are now available in many mobile phones and can be used for identifying and promoting activity and also understanding naturalistically-occurring activity. SenseCam and other personal, visual devices and mobile apps provide person point of view context to physical activity lifestyle and timing. Further, multiple sensor systems are enabling better identification of types of activities (like stair climbing and jumping) that could not previously be identified readily using objective measures like pedometers or accelerometers in isolation. The ability of activity sensors to send data to remote servers allows for the incorporation of online technology (e.g., employing an online social-network as a source of inspiration or accountability to achieve physical activity goals), and websites such as Stickk.com enable individuals to make public contracts visible to other users and also incorporates financial incentives and disincentives in order to promote behaviors including physical activity. In addition, the increasing use of active-gaming (e.g., Wii, XBox Kinect) in homes, schools, and other venues further underscores the growing link between technology and physical activity. Improvements in mathematical models and computer algorithms also allow greater capacity for classifying and evaluating physical activity, improving consistency across research studies. Emerging technologies in the promotion and evaluation of physical activity is a significant area of interest because of its ability to greatly increase the amount and quality of global recorded measurements of PA patterns and its potential to more effectively promote PA. Emerging technologies related to physical activity build on our own and others’ interdisciplinary collaborations in employing technology to address public health challenges. This research area is innovative in that is uses emerging resources including social media, crowdsourcing, and online gaming to better understand patterns of physical activity.

Download or read book Predictive Modeling for Physical Activity Intensity Levels Using Data from Accelerometer Placed on the Wrist written by Christopher Owusu Yirenkyi and published by . This book was released on 2019 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Transport Survey Methods written by Jean-Loup Madre and published by Emerald Group Publishing. This book was released on 2009-11-02 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifies various challenges to the world community of transport survey specialists as well as the larger constituency of practitioners, planners, and decision-makers that it serves and provides potential solutions and recommendations for addressing them.

Download or read book Evaluating AAL Systems Through Competitive Benchmarking written by Juan A. Botia and published by Springer. This book was released on 2013-08-30 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the international competition aimed at the evaluation and assessment of Ambient Assisted Living, EvAAL 2013, which was organized in three major events: the International Competition on Indoor Localization and Tracking for Ambient Assisted Living, which took place in Madrid, Spain, in July 2013; the International Competition on Activity Recognition for Ambient Assisted Living, which took place in Valencia, Spain, in July 2013; and the Final Workshop, which was held in Norrköping, Sweden, in September 2013. The papers included in this book describe the organization and technical aspects of the competitions and provide a complete technical description of the competing artefacts and report on the experience lessons learned by the teams during the competition.

Download or read book Human Activity and Behavior Analysis written by Md Atiqur Rahman Ahad and published by CRC Press. This book was released on 2024-04-29 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human Activity and Behavior Analysis relates to the field of vision and sensor-based human action or activity and behavior analysis and recognition. The book includes a series of methodologies, surveys, relevant datasets, challenging applications, ideas, and future prospects. The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. This volume focuses on relevant activities in three main subject areas: Healthcare and Emotion, Mental Health, and Nurse Care Records. The editors are experts in these arenas and the contributing authors are drawn from high-impact research groups around the world. This book will be of great interest to academics, students, and professionals working and researching in the field of human activity and behavior analysis.

Download or read book Handbook of Obesity Volume 1 written by George A. Bray and published by CRC Press. This book was released on 2014-02-10 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, we've developed a much better grasp of the biological and other factors associated with the development of obesity. Reflecting our evolving understanding of causes and consequences, Handbook of Obesity: Epidemiology, Etiology, and Physiopathology provides comprehensive coverage of the biological, behavioral, and environmental deter

Download or read book ACSM s Resource Manual for Guidelines for Exercise Testing and Prescription written by David P. Swain and published by Lippincott Williams & Wilkins. This book was released on 2012-12-26 with total page 883 pages. Available in PDF, EPUB and Kindle. Book excerpt: ACSM's Resource Manual for Guidelines for Exercise Testing and Prescription was created as a complement to ACSM's Guidelines for Exercise Testing and Prescription and elaborates on all major aspects of preventative rehabilitation and fitness programs and the major position stands of the ACSM. The 7th edition provides information necessary to address the knowledge, skills, and abilities set forth in the new edition of Guidelines, and explains the science behind the exercise testing and prescription. ACSM's Resource Manual is a comprehensive resource for those working in the fitness and clinical exercise fields, as well as those in academic training.

Download or read book Statistics in Precision Health written by Yichuan Zhao and published by Springer Nature. This book was released on with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: