Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
By Yu Zhao, Rennong Yang, Guillaume Chevalier, Maoguo Gong. Mathematical Problems in Engineering, 2017
348 citations recorded by the source corpus.
Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as shortcut for gradients, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked) dimensions, aiming to enhance the recognition rate. When testing with the Opportunity dataset and the public domain UCI dataset, the accuracy is significantly improved compared with previous results.
Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/9e4bc8195ea32032100d6344e791ddc5a194d054
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Thinagaran Perumal
- S. Padmavathi
- Parisa Rashidi
- Sakorn Mekruksavanich
- Anuchit Jitpattanakul
- Elangovan Ramanujam
In Computer Vision and Pattern Recognition
- Thinagaran Perumal
- S. Padmavathi
- Parisa Rashidi
- Graziano Pravadelli
- Azra Bihorac
- Sakorn Mekruksavanich
- Anuchit Jitpattanakul
- Elangovan Ramanujam
Citations in the corpus
- Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review (2021, background): “Zhao et al. [48], have proposed residual bi-directional LSTM architecture with an advantage to concatenate the forward state and backward state i.e. positive and negative time direction.”
- 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% -”
- Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, methodology): “The second most used model was the Long Short-TermMemory (LSTM) model, which was used in 17 papers [7], [83], [89], [102], [107], [112], [125], [130], [137], [139], [152], [153], [172],”