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      Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm

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      Measurement and Control
      SAGE Publications

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          Abstract

          Background:

          Detecting of human movements is an important task in various areas such as healthcare, fitness and eldercare. It is now possible to achieve this aim using mobile applications. These applications provide users, doctors and related persons a better understanding about daily physical activities. It can also lead to various useful habits by following the activities of the users in their daily life. In addition, dangerous actions such as the fall of elderly people or young children are identified and necessary precautions are taken as soon as possible. Classification of human motions with motion sensor data is among the current topics of study. Smart watches have these sensors built-in. Thus, it is possible to follow the activities of a user carrying only a smart watch.

          Methods:

          The purpose of this work is to detect human movements using smart watch sensor data and machine learning methods. The data are obtained from the accelerometer, gyroscope, step counter and heart rate sensors of the smart watch. The obtained data have been divided into 2 s windows and a data set containing 500 patterns for each class has been created for each class.

          Results and Discussion:

          After the features were determined, the data set to which the principal component analysis has been applied was classified by random forest, support vector machine, C4.5 and k-nearest neighbor methods, and their performances were compared. The most successful result was obtained from the random forest method.

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          Most cited references17

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          Activity recognition with smartphone sensors

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            Detecting periods of eating during free-living by tracking wrist motion.

            This paper is motivated by the growing prevalence of obesity, a health problem affecting over 500 million people. Measurements of energy intake are commonly used for the study and treatment of obesity. However, the most widely used tools rely upon self-report and require a considerable manual effort, leading to underreporting of consumption, noncompliance, and discontinued use over the long term. The purpose of this paper is to describe a new method that uses a watch-like configuration of sensors to continuously track wrist motion throughout the day and automatically detect periods of eating. Our method uses the novel idea that meals tend to be preceded and succeeded by the periods of vigorous wrist motion. We describe an algorithm that segments and classifies such periods as eating or noneating activities. We also evaluate our method on a large dataset (43 subjects, 449 total h of data, containing 116 periods of eating) collected during free-living. Our results show an accuracy of 81% for detecting eating at 1-s resolution in comparison to manually marked event logs of periods eating. These results indicate that vigorous wrist motion is a useful indicator for identifying the boundaries of eating activities, and that our method should prove useful in the continued development of body-worn sensor tools for monitoring energy intake.
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              COSAR: hybrid reasoning for context-aware activity recognition

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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                Measurement and Control
                Measurement and Control
                SAGE Publications
                0020-2940
                January 2019
                November 28 2018
                January 2019
                : 52
                : 1-2
                : 37-45
                Affiliations
                [1 ]Department of Information Systems Engineering, Faculty of Technology, Muğla Sıtkı Koçman University, Muğla, Turkey
                Article
                10.1177/0020294018813692
                cc27fef8-0212-43ba-a9f2-d7c75a4fa562
                © 2019

                https://creativecommons.org/licenses/by/4.0/


                Quantitative & Systems biology,Biophysics
                Quantitative & Systems biology, Biophysics

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