Enhancing Representation of Deep Features for Sensor-Based Activity Recognition

Li, Xue; Si, Xiandong; Ding, Ronggui; Zhan, Dechen

doi:10.1007/s11036-020-01689-y

Cited by 12 publications

(7 citation statements)

References 29 publications

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“…They proposed a CNN-based approach to extract cross-domain knowledge of human activity features, aiming to capture the dissimilarities between similar activities. Li et al [17] extracted activity features from raw CNN training data and enhanced human activity features by combining the features obtained from inverse CNN data through processes such as de-pooling, de-rectification, and de-convolution. It is worth noting that deep neural network models like CNNs eliminate the need for additional feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…Wan et al [16] used a three-layer CNN structure and LSTM for action classification recognition, resulting in accuracies of 91.00% and 85.86%, respectively. Li et al [17] employed a three-layer CNN structure and a three-layer reverse CNN structure to extract features from motion data. Based on domain knowledge, they selected a CNN (three-layer)-LSTM (one-layer) structure as the deep learning classifier.…”

Section: Sensor-based Har With Base Modelmentioning

confidence: 99%

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Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network

Yang,

Liao,

Zhao

et al. 2024

Mathematics

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Sensor-based human activity recognition (HAR) plays a fundamental role in various mobile application scenarios, but the model performance of HAR heavily relies on the richness of the dataset and the completeness of data annotation. To address the shortage of comprehensive activity types in collected datasets, we adopt the domain adaptation technique with a graph neural network-based approach by incorporating an adaptive learning mechanism to enhance the action recognition model’s generalization ability, especially when faced with limited sample sizes. To evaluate the effectiveness of our proposed approach, we conducted experiments using three well-known datasets: MHealth, PAMAP2, and TNDA. The experimental results demonstrate the efficacy of our approach in sensor-based HAR tasks, achieving impressive average accuracies of 98.88%, 98.58%, and 97.78% based on the respective datasets. Furthermore, we conducted transfer learning experiments to address the domain adaptation problem. These experiments revealed that our proposed model exhibits exceptional transferability and distinguishing ability, even in scenarios with limited available samples. Thus, our approach offers a practical and viable solution for sensor-based HAR tasks.

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Section: Related Workmentioning

confidence: 99%

Section: Sensor-based Har With Base Modelmentioning

confidence: 99%

Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network

Yang,

Liao,

Zhao

et al. 2024

Mathematics

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“…Generally, information fusion is divided into data-level fusion, feature-level fusion and decision-level fusion. Several references have shown that feature-level fusion achieves better performance than no fusion [32][33][34][35]. In addition, many decision-level fusion methods are proposed, such as simple voting, majority voting, weighted majority, and fusion score method [36,37].…”

Section: Information Fusionmentioning

confidence: 99%

Smartphone sensors‐based human activity recognition using feature selection and deep decision fusion

Zhang

Yao

Fei

et al. 2023

IET Cyber-Phy Sys Theory & Ap

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Human activity recognition (HAR) with smartphone sensors is a significant research direction in human‐cyber‐physical systems. Aiming at the problem of feature redundancy and low recognition accuracy of HAR, this paper presents a novel system architecture comprising three parts: feature selection based on an oppositional and chaos particle swarm optimization (OCPSO) algorithm, multi‐input one‐dimensional convolutional neural network (MI‐1D‐CNN) utilizing time‐domain and frequency‐domain signals, and deep decision fusion (DDF) combining D‐S evidence theory and entropy. The proposed architecture is evaluated on the UCI HAR and WIDSM datasets. The results highlight that OCPSO performs better than particle swarm optimization (PSO) in feature selection, convergence speed, and recognition accuracy. Moreover, it is shown that for the MI‐1D‐CNN classifier, the frequency‐domain signals (95.96%) perform better than time‐domain signals (95.66%). In addition, this paper investigates the impact of the convolution layers, feature maps, filter sizes, and decision fusion methods on recognition accuracy. The results demonstrate that the DDF method (97.81%) outperforms single‐layer decision fusion in improving the recognition accuracy on the UCI HAR dataset.

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“…Despite the feature-learning approaches, the recent development of deep learning techniques brought a leap in the HAR performance. Typical works evaluated upon the PAMAP2 include: Shaohua et al [54] adopted CNN framework for HAR achieved an average accuracy of 91.00%; Li et al [55] validate a CNN-LSTM framework with the dataset by achieving an average accuracy ranging from 96.97% to 97.37%.…”

Section: Comparison With Related Workmentioning

confidence: 99%

Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition

Yan¹,

Liao²,

Zhao³

et al. 2022

Preprint

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The sensor-based human activity recognition (HAR) in mobile application scenarios is often confronted with sensor modalities variation and annotated data deficiency. Given this observation, we devised a graph-inspired deep learning approach toward the sensor-based HAR tasks, which was further used to build a deep transfer learning model toward giving a tentative solution for these two challenging problems. Specifically, we present a multi-layer residual structure involved graph convolutional neural network (ResGCNN) toward the sensor-based HAR tasks, namely the HAR-ResGCNN approach. Experimental results on the PAMAP2 and mHealth data sets demonstrate that our ResGCNN is effective at capturing the characteristics of actions with comparable results compared to other sensor-based HAR models (with an average accuracy of 98.18% and 99.07%, respectively). More importantly, the deep transfer learning experiments using the ResGCNN model show excellent transferability and few-shot learning performance. The graph-based framework shows good meta-learning ability and is supposed to be a promising solution in sensor-based HAR tasks.

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Enhancing Representation of Deep Features for Sensor-Based Activity Recognition

Cited by 12 publications

References 29 publications

Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network

Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network

Smartphone sensors‐based human activity recognition using feature selection and deep decision fusion

Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition

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