Action recognition using interrelationships of 3D joints and frames based on angle sine relation and distance features using interrelationships

Islam, M. Shujah; Bakhat, Khush; Khan, Rahmatullah; Iqbal, Mansoor; Islam, Muhammad; Ye, Zhongfu

doi:10.1007/s10489-020-02176-3

Cited by 20 publications

(8 citation statements)

References 43 publications

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“…The parameters maximise the probability that is calculated by link prediction algorithms using data that has been actually observed. Several state-of-the-art methods for dimension reduction in feature extraction and selection for feature detection are presented in [24][25][26][27]. For instance, authors [28] established a non-asymptotic risk bound for the maximum likelihood estimator of network connection probabilities.…”

Section: Related Workmentioning

confidence: 99%

Link prediction grounded upon complex networks based model, based on MJMI-RW

Bakhat¹,

Kifayat²,

Islam

et al. 2023

Preprint

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Link prediction is the process of predicting the connection probability between two nodes based on observable network data, such as network structural topology and node properties. Despite the fact that traditional similarity-based methods are basic and effective, their generalisation performance varies greatly across networks. In this paper, we propose a novel link prediction approach, MJMI-RW, based on the Maxwell Jüttner distribution endowed by the Mutual Information, which recovers the probability of a node's connection by applying node characteristics to a system with less computation. Initially, the method investigates a comprehensive node feature representation by combining diverse structural topology information with node importance properties through feature construction and selection. The selected node features are then fed into a supervised learning task that solves the features matrix using the node features as input. The enhancements of MJMI-RW in terms of the average area under the curve and the precision of state-of-the-art algorithms compared to the finest baseline networks when compared to baseline methods. The limitation of MJMI-RW is its minimal computational complexity feature construction and substitution of complex features with semantic node attributes. Moreover, since inductive matrix completion is a supervised learning task in which the underlying low-rank matrix can be solved by representative nodes instead of all their nodes, it offers a potential link prediction solution for large-scale networks.

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Section: Related Workmentioning

confidence: 99%

Link prediction grounded upon complex networks based model, based on MJMI-RW

Bakhat¹,

Kifayat²,

Islam

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Instead of taking advantage of fine-tuning action recognition models, previous techniques were only tested on target datasets. Recent research [31][32][33]38,39] proposed view-invariant using 2D or 3D incorporating algorithms with joints performed on our evaluated datasets [25,[31][32][33][34][35]38,39] that do not correspond to legit, and thus these techniques struggle to improve the action recognition performance on regression tasks with practical and mostly used databases [40][41][42][43]. These algorithms were developed to investigate the transferability of action recognition using the human skeleton.…”

Section: Related Workmentioning

confidence: 99%

Employing FGP-3D, a Fully Gated and Anchored Methodology, to Identify Skeleton-Based Action Recognition

et al. 2023

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Recent years have seen an explosion in interest in and development of action recognition based on skeletal data. Contemporary methods using fully gated units can successfully extract characteristics from human skeletons by relying on the human topology that has been predefined. Despite advancements, fully gated unit-based techniques have trouble generalizing to other domains, particularly when dealing with various human topological structures. In this context, we introduce FGP-3D, a novel skeleton-based action recognition technique that can generalize across datasets while being effective at learning spatiotemporal features from human skeleton sequences. This is accomplished via a multi-head attention technique to learn an ideal dependence feature matrix from the uniform distribution. We next re-evaluate state-of-the-art techniques as well as the suggested novel descriptor FGP-3D in order to examine the cross-domain generalizability of skeleton-based action recognition in real-world video skeleton statistics. After being applied to commonly used action categorization datasets, experimental results demonstrate that the proposed FGP-3D, with pre-training, generalizes well and outperforms the state-of-the-art.

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“…4. Euclidean distance is computed to evaluate the distance between two joints; hip-knee joint, knee-ankle joint, and knee-hip joint [24] using ( 1…”

Section: B Joint Angle Trackingmentioning

confidence: 99%

Mono Camera-based Human Skeletal Tracking for Squat Exercise Abnormality Detection using Double Exponential Smoothing

Hisham¹,

Hassan²,

Ibrahim³

et al. 2022

IJACSA

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Human action analysis is an enthralling area of research in artificial intelligence, as it may be used to improve a range of applications, including sports coaching, rehabilitation, and monitoring. By forecasting the body's vital position of posture, human action analysis may be performed. Human body tracking and action recognition are the two primary components of video-based human action analysis. We present an efficient human tracking model for squat exercises using the open-source MediaPipe technology. The human posture detection model is used to detect and track the vital body joints within the human topology. A series of critical body joint motions are being observed and analysed for aberrant body movement patterns while conducting squat workouts. The model is validated using a squat dataset collected from ten healthy people of varying genders and physiques. The incoming data from the model is filtered using the double exponential smoothing method;the Mean Squared Error between the measured and smoothed angles is determined to classify the movement as normal or abnormal. Level smoothing and trend control have parameters of 0.8928 and 0.77256, respectively. Six out of ten subjects in the trial were precisely predicted by the model. The mean square error of the signals obtained under normal and abnormal squat settings is 56.3197 and 29.7857, respectively. Thus, by utilising a simple threshold method, the low-cost camera-based squat movement condition detection model was able to detect the abnormality of the workout movement.

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Action recognition using interrelationships of 3D joints and frames based on angle sine relation and distance features using interrelationships

Cited by 20 publications

References 43 publications

Link prediction grounded upon complex networks based model, based on MJMI-RW

Link prediction grounded upon complex networks based model, based on MJMI-RW

Employing FGP-3D, a Fully Gated and Anchored Methodology, to Identify Skeleton-Based Action Recognition

Mono Camera-based Human Skeletal Tracking for Squat Exercise Abnormality Detection using Double Exponential Smoothing

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