Transportation mode recognition fusing wearable motion, sound and vision sensors

Richoz, Sebastian, Wang, Lin, Birch, Philip and Roggen, Daniel (2020) Transportation mode recognition fusing wearable motion, sound and vision sensors. IEEE Sensors Journal, 20 (16). pp. 9314-9328. ISSN 1530-437X

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We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time.

Item Type: Article
Keywords: Human activity recognition, transportation mode recognition, data fusion, machine learning, mobile sensing, wearable computing
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Sensor Technology Research Centre
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
T Technology
Depositing User: Daniel Roggen
Date Deposited: 07 Apr 2020 08:42
Last Modified: 16 Sep 2020 09:00

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Project NameSussex Project NumberFunderFunder Ref
Activity Sensing Technologies for Mobile UsersG2015HuaweiUnset