An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Ghadi, Yazeed Yasin; Khalid, Nida; Alsuhibany, Suliman A.; Shloul, Tamara al; Jalal, Ahmad; Park, Jeong-Min

doi:10.32604/cmc.2022.024422

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Besides sensor-based systems, medical condition identification based on video camera inputs and depth maps have been employed in various cases, such as for elderly help, fall detection, depression detection [14,15], etc. Though not as accurate as sensors attached to the body, these approaches are much less invasive and could be scaled.…”

Section: Introductionmentioning

confidence: 99%

A Novel Computationally Efficient Approach to Identify Visually Interpretable Medical Conditions from 2D Skeletal Data

Jesudhas¹,

Raghuveera²

2023

Computer Systems Science and Engineering

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Timely identification and treatment of medical conditions could facilitate faster recovery and better health. Existing systems address this issue using custom-built sensors, which are invasive and difficult to generalize. A low-complexity scalable process is proposed to detect and identify medical conditions from 2D skeletal movements on video feed data. Minimal set of features relevant to distinguish medical conditions: AMF, PVF and GDF are derived from skeletal data on sampled frames across the entire action. The AMF (angular motion features) are derived to capture the angular motion of limbs during a specific action. The relative position of joints is represented by PVF (positional variation features). GDF (global displacement features) identifies the direction of overall skeletal movement. The discriminative capability of these features is illustrated by their variance across time for different actions. The classification of medical conditions is approached in two stages. In the first stage, a low-complexity binary LSTM classifier is trained to distinguish visual medical conditions from general human actions. As part of stage 2, a multi-class LSTM classifier is trained to identify the exact medical condition from a given set of visually interpretable medical conditions. The proposed features are extracted from the 2D skeletal data of NTU RGB + D and then used to train the binary and multi-class LSTM classifiers. The binary and multi-class classifiers observed average F1 scores of 77% and 73%, respectively, while the overall system produced an average F1 score of 69% and a weighted average F1 score of 80%. The multi-class classifier is found to utilize 10 to 100 times fewer parameters than existing 2D CNN-based models while producing similar levels of accuracy.

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Section: Introductionmentioning

confidence: 99%

A Novel Computationally Efficient Approach to Identify Visually Interpretable Medical Conditions from 2D Skeletal Data

Jesudhas¹,

Raghuveera²

2023

Computer Systems Science and Engineering

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Effective Gait Abnormality Detection in Parkinson’s Patients for Multi-Sensors Surveillance System

Alazeb,

Batool,

Mudawi

et al. 2024

IEEE Access

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Exploiting Human Pose and Scene Information for Interaction Detection

Waheed¹,

Chelloug²,

Alsufyani³

et al. 2023

Computers, Materials &Amp; Continua

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Identifying human actions and interactions finds its use in many areas, such as security, surveillance, assisted living, patient monitoring, rehabilitation, sports, and e-learning. This wide range of applications has attracted many researchers to this field. Inspired by the existing recognition systems, this paper proposes a new and efficient human-object interaction recognition (HOIR) model which is based on modeling human pose and scene feature information. There are different aspects involved in an interaction, including the humans, the objects, the various body parts of the human, and the background scene. The main objectives of this research include critically examining the importance of all these elements in determining the interaction, estimating human pose through image foresting transform (IFT), and detecting the performed interactions based on an optimized multi-feature vector. The proposed methodology has six main phases. The first phase involves preprocessing the images. During preprocessing stages, the videos are converted into image frames. Then their contrast is adjusted, and noise is removed. In the second phase, the human-object pair is detected and extracted from each image frame. The third phase involves the identification of key body parts of the detected humans using IFT. The fourth phase relates to three different kinds of feature extraction techniques. Then these features are combined and optimized during the fifth phase. The optimized vector is used to classify the interactions in the last phase. The MSR Daily Activity 3D dataset has been used to test this model and to prove its efficiency. The proposed system obtains an average accuracy of 91.7% on this dataset.

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An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Cited by 3 publications

References 43 publications

A Novel Computationally Efficient Approach to Identify Visually Interpretable Medical Conditions from 2D Skeletal Data

A Novel Computationally Efficient Approach to Identify Visually Interpretable Medical Conditions from 2D Skeletal Data

Effective Gait Abnormality Detection in Parkinson’s Patients for Multi-Sensors Surveillance System

Exploiting Human Pose and Scene Information for Interaction Detection

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