Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human activity recognition from mobile inertial sensors using recurrence
  plots   Inertial sensors are present in most mobile devices nowadays and such devices
are used by people during most of their daily activities. In this paper, we
present an approach for human activity recognition based on inertial sensors by
employing recurrence plots (RP) and visual descriptors. The pipeline of the
proposed approach is the following: compute RPs from sensor data, compute
visual features from RPs and use them in a machine learning protocol. As RPs
generate texture visual patterns, we transform the problem of sensor data
classification to a problem of texture classification. Experiments for
classifying human activities based on accelerometer data showed that the
proposed approach obtains the highest accuracies, outperforming time- and
frequency-domain features directly extracted from sensor data. The best results
are obtained when using RGB RPs, in which each RGB channel corresponds to the
RP of an independent accelerometer axis.

11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph