Abstract
Recognition of human activity has a wide range of applications in medical
research and human survey systems. We present a powerful activity recognition system
based on a Smartphone in this paper. The system collects time series signals with a 3-
dimensional Smartphone accelerometer as the only sensor, from which 31 features in
the time domain and frequency domain are created. The quadratic classifier, k-nearest
neighbor algorithm, support vector machine, and artificial neural networks are used to
classify activities. Feature extraction and subset selection are used to reduce
dimensionality. In addition to passive learning, we use active learning techniques to
lower the cost of data tagging. The findings of the experiment demonstrate that the
categorization rate of passive learning is 84.4 percent and that it is resistant to common
cell phone postures and poses.
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