Proceedings ArticleDOI
A CNN-LSTM Approach to Human Activity Recognition
Ronald Mutegeki,Dong Seog Han +1 more
- pp 362-366
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TLDR
This paper proposes a holistic deep learning-based activity recognition architecture, a convolutional neural network-long short-term memory network (CNN-LSTM), which improves the predictive accuracy of human activities from raw data but also reduces the complexity of the model while eliminating the need for advanced feature engineering.Abstract:
To understand human behavior and intrinsically anticipate human intentions, research into human activity recognition HAR) using sensors in wearable and handheld devices has intensified. The ability for a system to use as few resources as possible to recognize a user's activity from raw data is what many researchers are striving for. In this paper, we propose a holistic deep learning-based activity recognition architecture, a convolutional neural network-long short-term memory network (CNN-LSTM). This CNN-LSTM approach not only improves the predictive accuracy of human activities from raw data but also reduces the complexity of the model while eliminating the need for advanced feature engineering. The CNN-LSTM network is both spatially and temporally deep. Our proposed model achieves a 99% accuracy on the iSPL dataset, an internal dataset, and a 92 % accuracy on the UCI HAR public dataset. We also compared its performance against other approaches. It competes favorably against other deep neural network (DNN) architectures that have been proposed in the past and against machine learning models that rely on manually engineered feature datasets.read more
Citations
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Journal ArticleDOI
Multi-input CNN-GRU based human activity recognition using wearable sensors
TL;DR: A Deep Neural Network based model, which uses Convolutional Neural Network, and Gated Recurrent Unit is proposed as an end-to-end model performing automatic feature extraction and classification of the activities as well, and establishes that the proposed model achieved superior classification performance than other similar architectures.
Journal ArticleDOI
Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges
Sen Qiu,Hongkai Zhao,Nan Jiang,Zhelong Wang,Long Liu,Yi An,Hongyu Zhao,Xin Miao,Ruichen Liu,Giancarlo Fortino +9 more
TL;DR: In this paper, a comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning, is presented.
Journal ArticleDOI
LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes
TL;DR: In this article, the authors proposed a generic HAR framework for smartphone sensor data, based on Long Short-Term Memory (LSTM) networks for time-series domains, and a hybrid LSTM network was proposed to improve recognition performance.
Journal ArticleDOI
A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data
TL;DR: A hybrid of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) is used, which does automatic feature extraction from the raw sensor data with minimal data pre-processing and outperforms the other compared approaches.
Journal ArticleDOI
iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition
TL;DR: In this paper, the authors proposed iSPLInception, a DL model motivated by the Inception-ResNet architecture, that not only achieves high predictive accuracy but also uses fewer device resources.
References
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Deep, convolutional, and recurrent models for human activity recognition using wearables
TL;DR: This paper rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors, and describes how to train recurrent approaches in this setting, introduces a novel regularisation approach, and illustrates how they outperform the state-of-the-art on a large benchmark dataset.
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Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables
TL;DR: In this paper, the authors rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors, and illustrate how they outperform the state-of-the-art on a large benchmark dataset.