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dc.contributor.authorLiu, Qingzhong
dc.contributor.authorZhaoxian, Zhou
dc.contributor.authorSarbagya, Shakya Ratna
dc.contributor.authorPrathyusha, Uduthalapally
dc.contributor.authorMengyu, Qiao
dc.contributor.authorAndrew, Sung H.
dc.date.accessioned2021-12-15T19:49:03Z
dc.date.available2021-12-15T19:49:03Z
dc.date.issued2018
dc.identifier.citationLiu Q, Zhou Z, Shakya SR, Uduthalaplly P, Qiao M and Sung AH (2018). Smartphone sensorbased activity recognition by using machine learning and deep learning algorithms. International Journal of Machine Learning and Computing,8(2): 121-126. doi:10.18178/ijmlc.2018.8.2.674en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3247
dc.descriptionArticle originally published International Journal of Machine Learning and Computing
dc.description.abstractSmartphones are widely used today, and it becomes possible to detect the user's environmental changes by using the smartphone sensors, as demonstrated in this paper where we propose a method to identify human activities with reasonably high accuracy by using smartphone sensor data. First, the raw smartphone sensor data are collected from two categories of human activity: motion-based, e.g., walking and running; and phone movement-based, e.g., left-right, up-down, clockwise and counterclockwise movement. Firstly, two types of features extraction are designed from the raw sensor data, and activity recognition is analyzed using machine learning classification models based on these features. Secondly, the activity recognition performance is analyzed through the Convolutional Neural Network (CNN) model using only the raw data. Our experiments show substantial improvement in the result with the addition of features and the use of CNN model based on smartphone sensor data with judicious learning techniques and good feature designs.en_US
dc.publisherInternational Journal of Machine Learning and Computingen_US
dc.relation.ispartofseriesVol. 8,;No. 2
dc.subjectSVMen_US
dc.subjectCNNen_US
dc.subjectfeature extractionen_US
dc.subjecthuman activity recognitionen_US
dc.subjectsensorsen_US
dc.subjectsmartphoneen_US
dc.titleSmartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithmsen_US
dc.typeArticleen_US


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