A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network

Mudawi, Naif Al; Alabduallah, Bayan Ibrahimm; Jalal, Ahmad; Kim, Wooseong

doi:10.3390/s23104716

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…Feature extraction is performed with the help of machine learning, and after that, movement classification is performed with the help of logistic regression, and optimal classification is performed in a short time. In [167], data gathered from physical motion, ambient, and vision-based sensors undergoes individual pre-processing tailored to each type. These specific pre-processors optimize the data for their respective category.…”

Section: Multi-modal-based Systemmentioning

confidence: 99%

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir,

Kim

2023

Electronics

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Smart cities are being developed worldwide with the use of technology to improve the quality of life of citizens and enhance their safety. Video surveillance is a key component of smart city infrastructure, as it involves the installation of cameras at strategic locations throughout the city for monitoring public spaces and providing real-time surveillance footage to law enforcement and other city representatives. Video surveillance systems have evolved rapidly in recent years, and are now integrated with advanced technologies like deep learning, blockchain, edge computing, and cloud computing. This study provides a comprehensive overview of video surveillance systems in smart cities, as well as the functions and challenges of those systems. The aim of this paper is to highlight the importance of video surveillance systems in smart cities and to provide insights into how they could be used to enhance safety, security, and the overall quality of life for citizens.

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Section: Multi-modal-based Systemmentioning

confidence: 99%

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir,

Kim

2023

Electronics

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“…However, the accuracy improvements for physical, signal, and both fused together are not very impressive. Another hybrid approach using both motion sensors and cameras has been suggested in [ 34 ]. A motion–state layer and an activity layer have been used along with long-short-term-memory and CNN to recognize ADLs.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

Javeed,

Mudawi,

Alazeb

et al. 2023

Sensors

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Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system’s results that an acceptable mean accuracy rate of 84.14% has been achieved.

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“…The availability of the internet is another plus that reinforces the development of such applications by providing the developers with access to tons of data. One such application is human activity recognition and localization, which sets its basis on the data acquired from the Internet of things (IoT) [4][5][6]. Modern smartphones and smartwatches contain various built-in sensors that provide information about the movements and positions of the user, and, if managed efficiently, the activity and location of the user can be accurately estimated.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Localization and Deep Human Activity Recognition through IoT Devices

Alazeb,

Azmat,

Al Mudawi

et al. 2023

Sensors

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Ubiquitous computing has been a green research area that has managed to attract and sustain the attention of researchers for some time now. As ubiquitous computing applications, human activity recognition and localization have also been popularly worked on. These applications are used in healthcare monitoring, behavior analysis, personal safety, and entertainment. A robust model has been proposed in this article that works over IoT data extracted from smartphone and smartwatch sensors to recognize the activities performed by the user and, in the meantime, classify the location at which the human performed that particular activity. The system starts by denoising the input signal using a second-order Butterworth filter and then uses a hamming window to divide the signal into small data chunks. Multiple stacked windows are generated using three windows per stack, which, in turn, prove helpful in producing more reliable features. The stacked data are then transferred to two parallel feature extraction blocks, i.e., human activity recognition and human localization. The respective features are extracted for both modules that reinforce the system’s accuracy. A recursive feature elimination is applied to the features of both categories independently to select the most informative ones among them. After the feature selection, a genetic algorithm is used to generate ten different generations of each feature vector for data augmentation purposes, which directly impacts the system’s performance. Finally, a deep neural decision forest is trained for classifying the activity and the subject’s location while working on both of these attributes in parallel. For the evaluation and testing of the proposed system, two openly accessible benchmark datasets, the ExtraSensory dataset and the Sussex-Huawei Locomotion dataset, were used. The system outperformed the available state-of-the-art systems by recognizing human activities with an accuracy of 88.25% and classifying the location with an accuracy of 90.63% over the ExtraSensory dataset, while, for the Sussex-Huawei Locomotion dataset, the respective results were 96.00% and 90.50% accurate.

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A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network

Cited by 4 publications

References 46 publications

A Survey of Video Surveillance Systems in Smart City

A Survey of Video Surveillance Systems in Smart City

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

Intelligent Localization and Deep Human Activity Recognition through IoT Devices

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