Automatic Classification of Human Body Postures Based on Curvelet Transform

Zerrouki, Nabil; Houacine, Amrane

doi:10.1007/978-3-319-11758-4_36

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…To evaluate the capability of the proposed technique, we used 3-fold cross-validation in classification step and we computed different statistical metrics to quantify the accuracy of our results, including the overall accuracy and the Area Under Curve (AUC) [39]. Figure 9 illustrates a confusion matrix and summarizes equations of the main related metrics that are commonly used to assess the quality of a binary decision method and which will be used to assess the performance of the GLR-SVM based fall detection approach.…”

Section: Support-vector-machine-based Fall Classificationmentioning

confidence: 99%

An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment

et al. 2019

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Falls are an important healthcare problem for vulnerable persons like seniors. Response to potential emergencies can be fastened timely detection and classification of falls. This paper addresses the detection of human falls using relevant pixel-based features reflecting variations in body shape. Specifically, the human body is divided into five partitions that correspond to five partial occupancy areas. For each frame, area ratios are calculated and used as input data for fall detection and classification. First, the detection of falls is addressed from a statistical point of view as an anomaly detection problem. Towards this end, an integrated approach merging a detection step with a classification step is proposed for enabling efficient human fall detection in a home environment. In this regard, an effective fall detection approach using generalized likelihood ratio (GLR) scheme is designed. However, a GLR scheme cannot discriminate between true falls and like-fall events, such as lying down. To mitigate this limitation, the support vector machine algorithm has been successfully applied on features of the detected fall to recognize the type of fall. Tests on two publicly available datasets show the effectiveness of the proposed approach to appropriately detecting and identifying falls. Compared with the neural network, k-nearest neighbor, decision tree and naïve Bayes procedures, the two steps approach achieved better detection performance.INDEX TERMS Smart home, human fall, fall detection, classification, machine learning algorithms.

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Section: Support-vector-machine-based Fall Classificationmentioning

confidence: 99%

An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment

et al. 2019

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“…These areas are determined using a portioning centered on the body's gravity center. These ratios are discriminative enough to describe human body, and not too complex in order to permit a fast processing [10][11][12]. Figure 4 represents a time evolution of the five ratios while performing daily and fall activities respectively.…”

Section: Feature Extractionmentioning

confidence: 99%

Fall detection using supervised machine learning algorithms: A comparative study

Zerrouki

Harrou

Houacine

et al. 2016

2016 8th International Conference on Modelling, Identification and Control (ICMIC)

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“…Recently, several publically datasets have been collected using wearable sensors have been collected to evaluate the detection performance of machine learning and deep learning methods [12][13][14]. In [12], two accelerometers and one gyroscope were employed to collect data on daily activities and falls.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Survey on Recent Advances in Fall Detection Systems Using Machine Learning Formalisms

Zerrouki

Harrou

Sun

et al. 2022

2022 7th International Conference on Frontiers of Signal Processing (ICFSP)

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As reported by numerous World Health Organization studies, fall incidents are considered one of the leading causes of loss of autonomy, injuries, and even deaths, and this is not only among elderly people but also in other categories such as workers. Fall incidents also have a considerable impact on the budget allocated to the care of people suffering from the effects of falls. This work presents a comprehensive review of state-of-the-art fall detection technologies considering the most powerful machine learning methodologies, both classical formalism (shallow methods), and approaches based on deep learning formalism. The authors reviewed the most recent and effective methods for fall detection and presented the used sensors, cameras, applied pre-treatments, generated attributes, and algorithms used in this field of application. The present work is completed by a discussion presenting some limitations that need to be analyzed and taken into account to further improve the quality of fall detection and reduce their impacts.

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Automatic Classification of Human Body Postures Based on Curvelet Transform

Cited by 11 publications

References 14 publications

An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment

An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment

Fall detection using supervised machine learning algorithms: A comparative study

A Survey on Recent Advances in Fall Detection Systems Using Machine Learning Formalisms

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