Human Body 3D Reconstruction and Gait Analysis via Features Mining Framework

Akhter, Israr; Hafeez, Sadaf

doi:10.1109/ibcast54850.2022.9990213

Cited by 3 publications

(3 citation statements)

References 66 publications

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“…Figure 4 displays the human silhouette extracted after background subtraction. Afterwards, the head landmark has been detected using the human body shape and size [ 40 ] and the lowest point of body has been taken as the foot point of the human, calculated as:

where

signifies the foot landmark position in the

frame sequences calculated using the frames variance. The calculations for human position has been designed as:

where

provides the human position in a frame

and

denotes the boundary for the frame.…”

Section: Methodsmentioning

confidence: 99%

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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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where

signifies the foot landmark position in the

frame sequences calculated using the frames variance. The calculations for human position has been designed as:

where

provides the human position in a frame

and

denotes the boundary for the frame.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 4 displays the human silhouette extracted after background subtraction. Afterwards, the head landmark has been detected using the human body shape and size [40] and the lowest point of body has been taken as the foot point of the human, calculated as:…”

Section: Pre-processing Of Videosmentioning

confidence: 99%

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

Javeed,

Mudawi,

Alazeb

et al. 2023

Sensors

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show abstract

“…Using a variety of hypotheses to indicate spatially and temporally-associated associations across segmentation methods, researchers concluded a high percentage of success ( Ryoo & Aggarwal, 2009 ). Each connection was characterized by redistributing the target region and determining local features using a learning algorithm ( Akhter & Hafeez, 2022 ; Jalal, Akhtar & Kim, 2020 ; Ghadi et al, 2021 ; Akhter & Javeed, 2022 ). Due to this misunderstanding, they assumed a permanent environment and could not handle the natural world and e-learning approaches ( Berlin & John, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Postures anomaly tracking and prediction learning model over crowd data analytics

Aljuaid

Akhter

Alsufyani

et al. 2023

PeerJ Computer Science

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Innovative technology and improvements in intelligent machinery, transportation facilities, emergency systems, and educational services define the modern era. It is difficult to comprehend the scenario, do crowd analysis, and observe persons. For e-learning-based multiobject tracking and predication framework for crowd data via multilayer perceptron, this article recommends an organized method that takes e-learning crowd-based type data as input, based on usual and abnormal actions and activities. After that, super pixel and fuzzy c mean, for features extraction, we used fused dense optical flow and gradient patches, and for multiobject tracking, we applied a compressive tracking algorithm and Taylor series predictive tracking approach. The next step is to find the mean, variance, speed, and frame occupancy utilized for trajectory extraction. To reduce data complexity and optimization, we applied T-distributed stochastic neighbor embedding (t-SNE). For predicting normal and abnormal action in e-learning-based crowd data, we used multilayer perceptron (MLP) to classify numerous classes. We used the three-crowd activity University of California San Diego, Department of Pediatrics (USCD-Ped), Shanghai tech, and Indian Institute of Technology Bombay (IITB) corridor datasets for experimental estimation based on human and nonhuman-based videos. We achieve a mean accuracy of 87.00%, USCD-Ped, Shanghai tech for 85.75%, and IITB corridor of 88.00% datasets.

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Abnormal Action Recognition in Crowd Scenes via Deep Data Mining and Random Forest

Akhter

Jalal

2023

2023 4th International Conference on Advancements in Computational Sciences (ICACS)

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Human Body 3D Reconstruction and Gait Analysis via Features Mining Framework

Cited by 3 publications

References 66 publications

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

Postures anomaly tracking and prediction learning model over crowd data analytics

Abnormal Action Recognition in Crowd Scenes via Deep Data Mining and Random Forest

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