ArticleEntropy (Basel, Switzerland)2021
An Information Gain-Based Model and an Attention-Based RNN for Wearable Human Activity Recognition.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network.Scientific reports · 2024Article
- Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes.Sensors (Basel, Switzerland) · 2022Article
- Sensor Data Analytics: Challenges and Methods for Data-Intensive Applications.Entropy (Basel, Switzerland) · 2022Article
- Evaluating the Effectiveness of Complexity Features of Eye Movement on Computer Activities Detection.Healthcare (Basel, Switzerland) · 2022Article
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6 authors.
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Abstract
Wearable sensor-based HAR (human activity recognition) is a popular human activity perception method. However, due to the lack of a unified human activity model, the number and positions of sensors in the existing wearable HAR systems are not the same, which affects the promotion and application. In this paper, an information gain-based human activity model is established, and an attention-based recurrent neural network (namely Attention-RNN) for human activity recognition is designed. Besides, the attention-RNN, which combines bidirectional long short-term memory (BiLSTM) with attention mechanism, was tested on the UCI opportunity challenge dataset. Experiments prove that the proposed human activity model provides guidance for the deployment location of sensors and provides a basis for the selection of the number of sensors, which can reduce the number of sensors used to achieve the same classification effect. In addition, experiments show that the proposed Attention-RNN achieves F1 scores of 0.898 and 0.911 in the ML (Modes of Locomotion) task and GR (Gesture Recognition) task, respectively.
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