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End-to-End Motion Classification Using Smartwatch Sensor Data

Windler, Torben; Ghauri, Junaid Ahmed; Syed, Muhammad Usman; Belostotskaya, Tamara; Chikukwa, Valerie; Drumond, Rafael Rêgo


Analysis of smart devices’ sensor data for the classification of human activities has become increasingly targeted by industry and motion research. With the popularization of smartwatches, this data becomes available to everyone. The user’s data from accelerometers and gyroscopes is conventionally analyzed as a multivariate time series to obtain reliable information about the user’s activity at a specific moment. Due to the particular sampling rate instabilities of each device, previous approaches mainly work with feature extraction methods to generalize the information independently of the gear, which requires a lot of time and expertise. To overcome this problem, we present an end-to-end model for activity classification based on convolutional neural networks of different dimensions without extensive feature extraction. The data preprocessing is not computationally intensive and the model can deal with the irregularities of the data. By representing the input as twofold – both, interpolated 1D time series and encoded time series as images with the help of Gramian Angular Summation Fields – the use of computer vision techniques is enabled. ... mehr

Verlagsausgabe §
DOI: 10.5445/KSP/1000098011/12
Veröffentlicht am 16.07.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000121377
Erschienen in Archives of Data Science, Series A
Band 6
Heft 1
Seiten P12, 19 S. online
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