Improving Sensor-based Activity Recognition Using Motion Capture as Additional Information

Paula Lago, Shingo Takeda, Tsuyoshi Okita, Sozo Inoue,
ACM Int'l Conf. Pervasive and Ubiquitous Computing (Ubicomp) Poster
(Not Available)
(Not Available)
4 pages
We propose a new method for human activity recognition
using a single accelerometer sensor and additional sensors
for training. The performance of inertial sensors for complex

activities drops considerably compared with simple activ-
ities due to inter-class similarities. In such cases deploy-
ing more sensors may improve the performance. But such

strategy is often not feasible in reality due to costs or pri-
vacy concerns among others. In this context, we propose

a new method to use additional sensors only in training
phase. We introduce the idea of mapping the test data to
a codebook created from the additional sensor information.
Using the Berkeley MHAD dataset our preliminary results
show this worked positively; improving by 10.0% both the
average F1-score and the average accuracy. Notably, we
achieved an average improvement of 143.1% for the stand,
sit and sit to stand activities, typical activities for which the
inertial sensor is less informative when using the wrist-worn

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