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Stacked Lstm Network for Human Activity Recognition Using Smartphone Data
- Source :
- EUVIP
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Sensor-based human activity recognition is an essential task for automatic behavior analysis for sports player, senior citizens, and IoT applications. The traditional approaches are based on hand-crafted features which use fixed mathematical rules to extract the features from the input data and are not capable of incremental learning. In this paper, we proposed a stacked long Short-term memory (LSTM) network for recognizing six human behaviors from the smartphone data. The network consists of a five LSTM cell that is trained end-to-end on the sensor data. The network is preceded by a single layer neural network that pre-processes the data for the stacked LSTM network. An L 2 regularizer is used in the cost function which helps the network in generalization. The network is evaluated on public domain UCI dataset and quantitative results are compared against six state-of-the-art methods. The performance is calculated in terms of precision-recall and the average accuracy. The proposed network improves the average accuracy by 0.93% as compared to the closest state-of-the-art method without any manual feature engineering.
- Subjects :
- Feature engineering
Artificial neural network
Generalization
business.industry
Computer science
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Function (mathematics)
Human behavior
Activity recognition
Task (computing)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Single layer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 8th European Workshop on Visual Information Processing (EUVIP)
- Accession number :
- edsair.doi...........a12dcd9919143ae34e98a8a4b18bcd09
- Full Text :
- https://doi.org/10.1109/euvip47703.2019.8946180