Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Data-Driven Zero-Velocity Detection for Foot-Mounted Inertial
  Navigation   We present two novel techniques for detecting zero-velocity events to improve
foot-mounted inertial navigation. Our first technique augments a classical
zero-velocity detector by incorporating a motion classifier that adaptively
updates the detector's threshold parameter. Our second technique uses a long
short-term memory (LSTM) recurrent neural network to classify zero-velocity
events from raw inertial data, in contrast to the majority of zero-velocity
detection methods that rely on basic statistical hypothesis testing. We
demonstrate that both of our proposed detectors achieve higher accuracies than
existing detectors for trajectories including walking, running, and
stair-climbing motions. Additionally, we present a straightforward data
augmentation method that is able to extend the LSTM-based model to different
inertial sensors without the need to collect new training data.

34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas