A CNN-LSTM Approach to Human Activity Recognition

By Ronald Mutegeki, D. Han. Digital Signal Processing and Signal Processing Education Workshop, 2020

383 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/824e86c2bddc5b536813e7d12b7445319f16d0d4

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Citations in the corpus

LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes (2021, methodology): “Lee [25] 1D-CNN 92.71% 2017 Hernandez [45] Bidir-LSTM 92.67% 2019 Mutegeki [31] CNN-LSTM 92.13% 2020 Ni [54] SDAE 97.15% 2020 The proposed approach 4-layer CNN-LSTM 99.39% -”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, background): “Similarly, other researchers [8, 82, 94, 95, 197, 245, 301, 302], [238] have also developed the CNN-LSTM model in varios application scenarios by taking advantage of the feature extraction ability of CNN and the time-series data reasoning ability of LSTM.”
Multi-input CNN-GRU based human activity recognition using wearable sensors (2021, background): “Authors in [28] exploited the advantages of both CNN and LSTM recurrent layers.”