Evolutionary Design of Recurrent Neural Network Architecture for Human Activity Recognition

Viswambaran, Ramya Anasseriyil; Chen, Gang; Xue, Bing; Nekooei, Seyed Mohammad

doi:10.1109/cec.2019.8790050

Cited by 9 publications

(1 citation statement)

References 22 publications

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“…At present, in the fields of gait recognition and HAR, most studies use a CNN to extract the spatial waveform features of gait data [ 12 ]. Some studies use a recurrent neural network (RNN) [ 13 ], gated recurrent unit (GRU) [ 14 ], or LSTM [ 15 ] to extract the time-series correlation features of gait data.…”

Section: Introductionmentioning

confidence: 99%

Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors

Shi

Liu

Zhou

et al. 2023

Sensors

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Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait has obvious time-series characteristics, while CNN only collects waveform characteristics, and only uses CNN for gait recognition, this leads to a certain lack of time-series characteristics. LSTM can collect time-series characteristics, but LSTM results in performance degradation when processing long sequences. However, using CNN can compress the length of feature vectors. In this paper, a sequential convolution LSTM network for gait recognition using multimodal wearable inertial sensors is proposed, which is called SConvLSTM. Based on 1D-CNN and a bidirectional LSTM network, the method can automatically extract features from the raw acceleration and gyroscope signals without a manual feature design. 1D-CNN is first used to extract the high-dimensional features of the inertial sensor signals. While retaining the time-series features of the data, the dimension of the features is expanded, and the length of the feature vectors is compressed. Then, the bidirectional LSTM network is used to extract the time-series features of the data. The proposed method uses fixed-length data frames as the input and does not require gait cycle detection, which avoids the impact of cycle detection errors on the recognition accuracy. We performed experiments on three public benchmark datasets: UCI-HAR, HuGaDB, and WISDM. The results show that SConvLSTM performs better than most of those reporting the best performance methods, at present, on the three datasets.

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Section: Introductionmentioning

confidence: 99%

Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors

Shi

Liu

Zhou

et al. 2023

Sensors

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A Survey of Machine Learning and Meta-heuristics Approaches for Sensor-based Human Activity Recognition Systems

Saha

Rajak

Saha

et al. 2022

J Ambient Intell Human Comput

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GAN-based pseudo random number generation optimized through genetic algorithms

Wu,

Han,

Zhang

et al. 2024

Complex Intell. Syst.

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Pseudo-random number generators (PRNGs) are deterministic algorithms that generate sequences of numbers approximating the properties of random numbers, which are widely utilized in various fields. In this paper, we present a Genetic Algorithm Optimized Generative Adversarial Network (hereinafter referred to as GAGAN), which is designed for the effective training of discrete generative adversarial networks. In situations where non-differentiable activation functions, such as the modulo operation, are employed and traditional gradient-based backpropagation methods are inapplicable, genetic algorithms are utilized to optimize the parameters of the generator network. Based on this framework, we propose a novel recursive PRNG. Given that a PRNG can be constructed from one-way functions and their associated hardcore predicates, our proposed generator consists of two neural networks that simulate these functions and serve as the state transition function and the output function, respectively. The proposed PRNG has been rigorously tested using stringent benchmarks such as the NIST Statistical Test Suite (SP800-22) and the Chinese standard for random number generation (GM/T 0005-2021). Additionally, it has demonstrated outstanding performance in terms of Hamming distance. The results indicate that the proposed GAN-based PRNG has achieved a high degree of randomness and is highly sensitive to variations in the input.

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Evolutionary Design of Recurrent Neural Network Architecture for Human Activity Recognition

Cited by 9 publications

References 22 publications

Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors

Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors

A Survey of Machine Learning and Meta-heuristics Approaches for Sensor-based Human Activity Recognition Systems

GAN-based pseudo random number generation optimized through genetic algorithms

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