Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

Rustam, Furqan; Reshi, Aijaz Ahmad; Ashraf, Imran; Mehmood, Arif; Ullah, Saleem; Khan, Dost Muhammad; Choi, Gyu Sang

doi:10.1109/access.2020.3041822

Cited by 57 publications

(26 citation statements)

References 37 publications

(47 reference statements)

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“…with the CNN Model. In this study, experiments have also been conducted on some of the relevant machine learning models such as Random Forest (RF) [26,27], gradient boosting machine (GBM) [28,29], support vector classifier (SVC) [30], logistic regression (LR) [31], and k-nearest neighbor (KNN) [32] for comparative analysis of CNN with these models. ese models have been used with their best parameter settings as shown in Table 7.…”

Section: Performance Comparison Of Machine Learning Modelsmentioning

confidence: 99%

An Efficient CNN Model for COVID‐19 Disease Detection Based on X‐Ray Image Classification

et al. 2021

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Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set.

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Section: Performance Comparison Of Machine Learning Modelsmentioning

confidence: 99%

An Efficient CNN Model for COVID‐19 Disease Detection Based on X‐Ray Image Classification

et al. 2021

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“…In sensor-based methods, wearable sensors capture activities, causing inconvenience and long-time monitoring unavailability [23]. In the past decade, smartphones have become more powerful with many built-in sensors, including the accelerator, gyroscope.…”

Section: Related Workmentioning

confidence: 99%

“…In the past decade, smartphones have become more powerful with many built-in sensors, including the accelerator, gyroscope. The main impediments in using smartphones for HAR tasks are their higher noise ratio than wearable sensors and fast battery drain [23]. Several researchers have exerted Radio Frequency Identification (RFID) tags to recognize human activities [24].…”

Section: Related Workmentioning

confidence: 99%

A CSI-Based Human Activity Recognition Using Deep Learning

Moshiri

Shahbazian²,

Nabati

et al. 2021

Sensors

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The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.

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“…The classification algorithms such as SVM [28], artificial neural network [29], k-mean clustering [30] and decision tree (DT) [31] are widely applied in HAR. Besides, new branches of machine learning, such as deep learning [32][33][34] and ensemble learning [35][36][37], have also shown their merits in HAR. However, the deep learning-based approaches require a huge dataset for model training, which may not be applicable in actual scenarios.…”

Section: Related Workmentioning

confidence: 99%

A Novel Sensor-Based Human Activity Recognition Method Based on Hybrid Feature Selection and Combinational Optimization

Tian

Jie

et al. 2021

IEEE Access

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In recent years, sensor-based human activity recognition (HAR) has become a hot topic due to the advancement of sensing technologies, wireless communication technologies and nano-technologies. Since the sensor signals are usually non-stationary and quite noisy, both selecting the discriminant feature representations and finding out the optimal parameters for recognition algorithm play an important role for the enhanced performance and robustness of an HAR system. However, most of the previous research focused on one of them ignoring their interactions. Very few studies focused on these two aspects simultaneously. Considering the two factors separately may lead to inferior HAR performance. This paper presents a novel HAR framework which can optimize the feature set and the parameters of recognition algorithm synchronously for robust and optimal system performance. A new hybrid feature selection methodology using game-theory based feature selection (GTFS) and binary firefly algorithm (BFA), called GTFS-BFA, is proposed. GTFS-BFA is a hybrid methodology combining evidence from both filter and wrapper feature selection methods. It consists of two phases, namely pre-selection phase and re-selection phase. Pre-selection phase relies on game-theory-based filter method, while the re-selection phase uses binary firefly algorithm (BFA) as a wrapper method. The popular and efficient algorithm kernel extreme learning machine (KELM) is utilized as a classifier. The experimental results indicate that the proposed method can obtain better comprehensive performance in terms of four performance measures through a comparison to other existing methods on daily activity dataset from five body positions.

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Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

Cited by 57 publications

References 37 publications

An Efficient CNN Model for COVID‐19 Disease Detection Based on X‐Ray Image Classification

An Efficient CNN Model for COVID‐19 Disease Detection Based on X‐Ray Image Classification

A CSI-Based Human Activity Recognition Using Deep Learning

A Novel Sensor-Based Human Activity Recognition Method Based on Hybrid Feature Selection and Combinational Optimization

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