A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition

Smartphone-sensors-based human activity recognition is attracting increasing interest due to the popularization of smartphones. It is a difficult long-range temporal recognition problem, especially with large intraclass distances such as carrying smartphones at different locations and small intercla...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-12, Vol.20 (23), p.6984
Main Authors: Zhao, Beidi, Li, Shuai, Gao, Yanbo, Li, Chuankun, Li, Wanqing
Format: Article
Language:English
Subjects:
activity recognition
Cellular telephones
Classification
Decision trees
Deep learning
Feature recognition
Human Activities
Human activity recognition
Humans
IndRNN
Locomotion
Markov analysis
Methods
Moving object recognition
Neural Networks, Computer
Recognition, Psychology
Recurrent neural networks
Sensors
SHL dataset
Smartphone
smartphone sensors
Smartphones
Support vector machines
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Summary:Smartphone-sensors-based human activity recognition is attracting increasing interest due to the popularization of smartphones. It is a difficult long-range temporal recognition problem, especially with large intraclass distances such as carrying smartphones at different locations and small interclass distances such as taking a train or subway. To address this problem, we propose a new framework of combining short-term spatial/frequency feature extraction and a long-term independently recurrent neural network (IndRNN) for activity recognition. Considering the periodic characteristics of the sensor data, short-term temporal features are first extracted in the spatial and frequency domains. Then, the IndRNN, which can capture long-term patterns, is used to further obtain the long-term features for classification. Given the large differences when the smartphone is carried at different locations, a group-based location recognition is first developed to pinpoint the location of the smartphone. The Sussex-Huawei Locomotion (SHL) dataset from the SHL Challenge is used for evaluation. An earlier version of the proposed method won the second place award in the SHL Challenge 2020 (first place if not considering the multiple models fusion approach). The proposed method is further improved in this paper and achieves 80.72% accuracy, better than the existing methods using a single model.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20236984