A Generic Multi-modal Dynamic Gesture Recognition System using Machine Learning
Human computer interaction facilitates intelligent communication between humans and computers, in which gesture recognition plays a prominent role. This paper proposes a machine learning system to identify dynamic gestures using tri-axial acceleration data acquired from two public datasets. These da...
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Zusammenfassung: | Human computer interaction facilitates intelligent communication between
humans and computers, in which gesture recognition plays a prominent role. This
paper proposes a machine learning system to identify dynamic gestures using
tri-axial acceleration data acquired from two public datasets. These datasets,
uWave and Sony, were acquired using accelerometers embedded in Wii remotes and
smartwatches, respectively. A dynamic gesture signed by the user is
characterized by a generic set of features extracted across time and frequency
domains. The system was analyzed from an end-user perspective and was modelled
to operate in three modes. The modes of operation determine the subsets of data
to be used for training and testing the system. From an initial set of seven
classifiers, three were chosen to evaluate each dataset across all modes
rendering the system towards mode-neutrality and dataset-independence. The
proposed system is able to classify gestures performed at varying speeds with
minimum preprocessing, making it computationally efficient. Moreover, this
system was found to run on a low-cost embedded platform - Raspberry Pi Zero
(USD 5), making it economically viable. |
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DOI: | 10.48550/arxiv.1809.05839 |