A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data

Bhuiyan, Rasel Ahmed; Ahmed, Nadeem; Amiruzzaman, Md; Islam, Rashedul

doi:10.3390/s20236990

Cited by 31 publications

(16 citation statements)

References 46 publications

(63 reference statements)

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“…Most of these researches focused on deep learning due to its better performance and few of them used barometric sensors for activity recognition [ 29 ]. Rasel et al [ 30 ] extracted the spatial features using acidometer sensors and classified using multiclass SVM for final activity recognition. Zhao et al [ 31 ] introduced a combined framework for activity recognition.…”

Section: Related Workmentioning

confidence: 99%

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Khan

Alhaisoni

et al. 2021

Sensors

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Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions based on computer vision (CV) have been proposed in the literature which did not prove to be successful due to large video sequences which need to be processed in surveillance systems. The problem exacerbates in the presence of multi-view cameras. Recently, the development of deep learning (DL)-based systems has shown significant success for HAR even for multi-view camera systems. In this research work, a DL-based design is proposed for HAR. The proposed design consists of multiple steps including feature mapping, feature fusion and feature selection. For the initial feature mapping step, two pre-trained models are considered, such as DenseNet201 and InceptionV3. Later, the extracted deep features are fused using the Serial based Extended (SbE) approach. Later on, the best features are selected using Kurtosis-controlled Weighted KNN. The selected features are classified using several supervised learning algorithms. To show the efficacy of the proposed design, we used several datasets, such as KTH, IXMAS, WVU, and Hollywood. Experimental results showed that the proposed design achieved accuracies of 99.3%, 97.4%, 99.8%, and 99.9%, respectively, on these datasets. Furthermore, the feature selection step performed better in terms of computational time compared with the state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Khan

Alhaisoni

et al. 2021

Sensors

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“…Ha and Ryu [ 30 ] proposed an ensemble method called Error Correction Output Coding (ECOC), with the random forest as the basic learner and the classification accuracy was 97.8%. Bhuiyan et al [ 31 ] used a multi-class support vector machine (MC-SVM) as a classifier to classify five kinds of daily actions performed by humans and achieved good recognition performance. Minarno [ 32 ] compared the classification performance of DT, RF, KNN, logistic regression (LR), SVM and ensemble voting classifier (ECLF).…”

Section: Related Workmentioning

confidence: 99%

Human Behavior Recognition Model Based on Feature and Classifier Selection

Gao

Huan

et al. 2021

Sensors

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With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.

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“…Metode ini memiliki kelebihan dari segi komputasi yang ringan dan dapat menghindari overfit [57]. Beberapa pendekatan filter digunakan dalam konteks HAR menggunakan sensor inersia antara lain Relief F [21][58], Fisher Score [21][58], Chi-Square [21][58], Mutual Information [41], Information Gain [8], Linear Discriminant Analysis (LDA) [53], dan Korelasi Pearson [4].…”

Section: Filterunclassified

“…al. [3] [44] Multi-layer Perceptron (MLP) [7], [9], [20] Long-Short Term Memory (LSTM) [10], [55], [51] Deep Neural Network (DNN) [42] Deep Convolution Neural Networks (DCNN) [14], [46] Extreme Learning Machine (ELM) [60] Machine Learning Support Vector Machine (SVM) [8], [13], [14], [15], [44], [43], [48], [20], [58], [21], [41], [53], [61], [62], [51] Decision Tree (DT) [4], [63], [8], [43], [58], [21], [51] Genetic Algorithm (GA) [43] Artificial Neural Network (ANN) [4], [15], [44] Hidden Markov Model [7], [13] K Nearest Neighbor (KNN) [3], [8], [14], [20], [62], [63], [51] Extremely Randomized Tree [20] Linear Regression <...>…”

Section: Ensemblementioning

confidence: 99%

“…Ilustrasi confusion matrix untuk klasifikasi multi-kelas direpresentasikan pada Gambar 9, dimana K merupakan kelas, i merepresentasikan kelas spesifik tertentu, dan n merupakan jumlah kelas. Selain metode klasifikasi yang digunakan, hal penting yang juga dapat berpengaruh pada akurasi klasifikasi HAR adalah bagaimana pra-proses pengolahan data serta rekayasa fitur dilakukan [53]. Gambar 14 menunjukkan perbandingan rata-rata akurasi pada jenis rekayasa fitur yang diperoleh dari meta analisis 38 publikasi.…”

Section: Rq-9: Performa Modelunclassified

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Studi Literatur Human Activity Recognition (HAR) Menggunakan Sensor Inersia

Pradani

Mahananto²

2021

RESTI

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Human activity recognition (HAR) is one of the topics that is being widely researched because of its diverse implementation in various fields such as health, construction, and UI / UX. As MEMS (Micro Electro Mechanical Systems) evolves, HAR data acquisition can be done more easily and efficiently using inertial sensors. Inertial sensor data processing for HAR requires a series of processes and a variety of techniques. This literature study aims to summarize the various approaches that have been used in existing research in building the HAR model. Published articles are collected from ScienceDirect, IEEE Xplore, and MDPI over the past five years (2017-2021). From the 38 studies identified, information extracted are the overview of the areas of HAR implementation, data acquisition, public datasets, pre-process methods, feature extraction approaches, feature selection methods, classification models, training scenarios, model performance, and research challenges in this topic. The analysis showed that there is still room to improve the performance of the HAR model. Therefore, future research on the topic of HAR using inertial sensors can focus on extracting and selecting more optimal features, considering the robustness level of the model, increasing the complexity of classified activities, and balancing accuracy with computation time.

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A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data

Cited by 31 publications

References 46 publications

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Human Behavior Recognition Model Based on Feature and Classifier Selection

Studi Literatur Human Activity Recognition (HAR) Menggunakan Sensor Inersia

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