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- W4200533373 abstract "One of the major challenges for fully enjoying the power of machine learning is the need for the high-quality labelled data. To tap-in the gold-mine of data generated by IoT devices with unprecedented volume and value, we discover and leverage the hidden connections among the multimodal data collected by various sensing devices. Different modal data can complement and learn from each other, but it is challenging to fuse multimodal data without knowing their perception (and thus the correct labels). In this work, we propose MultiSense, a paradigm for automatically mining potential perception, cross labelling each modal data, and then improving the learning models for recognizing human activity accurately. We design innovative solutions for segmenting, aligning, and fusing multimodal data from different sensors. We implement our framework and conduct comprehensive evaluations on a rich set of data. Our results demonstrate that MultiSense significantly improves the data usability and the power of the learning models. With 9 diverse activities performed by users, our framework automatically labels multimodal sensing data generated by five different sensing mechanisms (video, smart watch, smartphone, audio, and wireless-channel) with an average accuracy 98.5%, while for each single-modal data model, the accuracy is 95.2%, 92.1%, 71.1%, 91.5%, and 34.8% respectively. Furthermore, it enables the existing models to learn unknown activities from other modalities and thus greatly improves the activity recognition accuracy." @default.
- W4200533373 created "2021-12-31" @default.
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- W4200533373 date "2021-10-01" @default.
- W4200533373 modified "2023-10-18" @default.
- W4200533373 title "MultiSense: Cross Labelling and Learning Human Activities Using Multimodal Sensing Data" @default.
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- W4200533373 doi "https://doi.org/10.1109/mass52906.2021.00057" @default.
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