IF-ConvTransformer

نویسندگان

چکیده

Recent advances in sensor based human activity recognition (HAR) have exploited deep hybrid networks to improve the performance. These models combine Convolutional Neural Networks (CNNs) and Recurrent (RNNs) leverage their complementary advantages, achieve impressive results. However, roles associations of different sensors HAR are not fully considered by these models, leading insufficient multi-modal fusion. Besides, commonly used RNNs suffer from 'forgetting' defect, which raises difficulties capturing long-term information. To tackle problems, an framework composed Inertial Measurement Unit (IMU) fusion block applied ConvTransformer subnet is proposed this paper. Inspired filter, our IMU performs according physical relationships. Consequently, features modalities can be aggregated more effectively. Then, extracted fed into for classification. Thanks its convolutional self-attention layers, better capture local construct dependencies. Extensive experiments on eight benchmark datasets demonstrate superior performance framework. The source code will published soon.

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ژورنال

عنوان ژورنال: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies

سال: 2022

ISSN: ['2474-9567']

DOI: https://doi.org/10.1145/3534584