MEASURed: A Multi-Sensor Setting Activity Recognition Simulation Tool

Shingo Takeda, Paula Lago, Tsuyoshi Okita, Sozo Inoue,
Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA)
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Motion capture generates data which are often more accurate
than those captured by multiple of accelerometer
sensors by their physical specification. Based on the observation
that accelerometer data can be virtually build by
the second derivation of position data from motion capture,
we propose a simulator, called MEASURed, for activity
recognition classifier. MEASURed can accommodate
any number of virtual accelerometer sensors on the
body based on some given motion capture data. Therefore,
MEASURed can evaluate activity recognition classifiers in
settings with different number, placement, and sampling
rate of accelerometer sensors. Our results show that the
F1-Score estimated by MEASURed is close to that obtained
with the real accelerometer data.

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