A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer

By Yuqing Chen, Yang Xue. IEEE International Conference on Systems, Man and Cybernetics, 2015

341 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7125280b9daee53ddd7bfad3908f7bc0dedf718a

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “A majority of the CNNs consist of 5 to 9 layers [67, 91, 193, 232, 272, 300, 313], [130], [96], usually including 2 to 3 convolutional layers, 2 to 3 max pooling layers, followed by 1 to 2 fully connected layers before feeding the feature representation into the output layer (softmax layer in most c”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “In [14], [271], [272], [273], the tri-axial acceleration data was fed to different CNN architectures for HAR.”
Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors (2020, methodology): “We also compared our method with CNN [8], [21] and CNN with LSTM (ConvLSTM) [31] which use automatic feature generation.”
Deep Recurrent Neural Networks for Human Activity Recognition (2017, methodology): “Another model in [8] used deep CNNs with only a single accelerometer.”
Deep Learning for Sensor-based Activity Recognition: A Survey (2017, methodology): “For multi-modal signals, it is better to use CNN since (Bhattacharya and Lane, 2016) Body-worn, ambiemt RBM Gesture, ADL, transportation Self, D01 (Chen and Xue, 2015) Body-worn CNN ADL Self (Chen et al., 2016b) Body-worn CNN ADL D06 (Edel and K¨oppe, 2016) Body-worn RNN ADL D01, D04, Self (Fang and”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[232] Individual income classification Cloud-based MLP Chen and Xue [233] Activity recognition Cloud-based CNN Ha and Choi [234] Activity recognition Cloud-based CNN Edel and Köppe [235] Activity recognition Edge-based Binarized-LSTM Okita and Inoue [236] Multiple overlapping activities recognition ”
Deep Learning for RFID-Based Activity Recognition (2016, background): “Earlier research showed that deep learning can be applied to data from mobile phone sensors or an accelerometer for recognition of person’s simple physical activities [20,21].”
Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review (2021, background): “Chen and Xue [23] have proposed a CNN model to recognize the simple activities through tri-axial accelerometer signals of a smartphone.”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, neutral): “The most popular model is the Convolutional Neural Network (CNN), which was referenced in 30 papers [7], [32], [73], [77], [81], [88], [90],”
IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment (2019, methodology): “The CNN used in [30] performs an HAR task by using the data coming from a single accelerometer, making possible to construct an acceleration-based HAR on the mobile platform, with no extra hardware demand.”