Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Shah, Syed Muhammad Saqlain; Malik, Tahir Afzal; Khatoon, Robina; Hassan, Syed Saqlain; Shah, Faheem

doi:10.32604/cmc.2019.07948

Cited by 9 publications

(2 citation statements)

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“…The excavated data is tested with CNN and the accuracy was recorded as 92.63%.CNN layers used in this method extracts features of similar activities first and other activities later which results more time consumption in recognizing unknown or new activity. Continuous and discreet pattern mining ways, modelling based on reduced feature; probabilistic model and collecting of activity classification through simple mining methods are recently proposed by data scientists [15].These data are collected from smart home with more than one person residing in the test bed mode of activity recognition. For assessing the activity procedures of dissimilar inhabitants, to distinguish a person's actions collected from undisturbed sensor data authors of [16] suggested an approach based on FCA (formal concept analysis) with CNN.…”

Section: Related Workmentioning

confidence: 99%

An Imperceptible Method to Monitor Human Activity by Using Sensor Data with CNN and Bi-directional LSTM

Rajesh

Kavitha

2023

IJRITCC

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Deep learning (DL) algorithms have substantially increased research in recognizing day-to-day human activities All methods for recognizing human activities that are found through DL methods will only be useful if they work better in real-time applications. Activities of elderly people need to be monitored to detect any abnormalities in their health and to suggest healthy life style based on their day to day activities. Most of the existing approaches used videos, static photographs for recognizing the activities. Those methods make the individual to feel anxious that they are being monitored. To address this limitation we utilized the cognitive outcomes of DL algorithms and used sensor data as input to the proposed model which is collected from smart home dataset for recognizing elderly people activity, without any interference in their privacy. At early stages human activities the input for human activity recognition by DL models are done using single sensor data which are static and lack in recognizing dynamic and multi sensor data. We propose a DL architecture based on the blend of deep Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) in this research which replaces human intervention by automatically extracting features from multifunctional sensing devices to reliably recognize the activities. During the entire investigation process we utilized Tulum, a benchmark dataset that contains the logs of sensor data. We exhibit that our methodology outperforms by marking its accuracy as 98.76% and F1 score as 0.98.

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Section: Related Workmentioning

confidence: 99%

An Imperceptible Method to Monitor Human Activity by Using Sensor Data with CNN and Bi-directional LSTM

Rajesh

Kavitha

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…The gradients were supposed to work in arbitrary scales and parameter optimization regarding the action classification was evaluated as well. Interest points-based spatiotemporally windowed data [21] features were employed for human behavior classification while support vector machine-based human skeleton features [22] were presented for the same task as well. Multiclass support vector machines were used by Sharif et al [23] extracting three types of feature vectors from the input frames i.e., local binary patterns, the histogram of oriented gradients, and Harlick features.…”

Section: Features Based Action Recognition From 2d Videosmentioning

confidence: 99%

Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification

et al. 2020

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Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.

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Video Action Recognition Based on Hybrid Convolutional Network

Song

Tan

Zhou

et al. 2020

Lecture Notes in Computer Science

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Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Cited by 9 publications

References 0 publications

An Imperceptible Method to Monitor Human Activity by Using Sensor Data with CNN and Bi-directional LSTM

An Imperceptible Method to Monitor Human Activity by Using Sensor Data with CNN and Bi-directional LSTM

Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification

Video Action Recognition Based on Hybrid Convolutional Network

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