Nabil Zerrouki

Accelerometer and Camera-Based Strategy for Improved Human Fall Detection

In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow’s. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.

Automatic Classification of Human Body Postures Based on Curvelet Transform

This paper presents the design and implementation of a posture classification method. A new feature extraction strategy according to curvelet transform is provided for identifying the posture in images. First of all, human body is segmented. For this purpose, a background subtraction technique is applied. Then, a curvelet transform is used for extracting features from the posture image. To address the rotation invariance problem, five ratios are evaluated from the human body and they are also included in the set of features. Finally the human body postures are classified through support vector machines (SVM). Experimental results are obtained on the “Fall Detection” dataset. For evaluation, different state of the art statistical measures have been considered such as overall accuracy, the kappa coefficient, the F-measure coefficient, and the area under ROC curve (AUC) value. All of these evaluation measures demonstrate that the proposed approach provides a significant recognition rate.

Fall detection using supervised machine learning algorithms: A comparative study

Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

A simple strategy for fall events detection

The paper concerns the detection of fall events based on human silhouette shape variations. The detection of fall events is addressed from the statistical point of view as an anomaly detection problem. Specifically, the paper investigates the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart. The MEWMA fall detection scheme has been successfully applied to two publicly available fall detection databases, the UR fall detection dataset (URFD) and the fall detection dataset (FDD). The monitoring strategy developed was able to provide early alert mechanisms in the event of fall situations.

Combined curvelets and hidden Markov models for human fall detection

Fall events detection is one of the most crucial issues in the health care of elderly people. This paper proposes an innovative approach for reliably detecting fall incidents based on human silhouette shape variation in vision monitoring. This mission is achieved by: (i) introducing the curvelet transform and area ratios for identifying human postures in images; (ii) reducing the feature vector dimension using differential evolution technique; (iii) identifying postures by a support vector machine, and (iv) adapting a hidden Markov model for classifying video sequences into non-fall and fall events. Experimental results are obtained on several “Fall Detection” datasets. For evaluation, several assessment measures are computed. These evaluation measures demonstrate the effectiveness of the proposed methodology when compared to some state-of-the-art approaches.

Vision-based fall detection system for improving safety of elderly people

Recognition of human movements is very useful for several applications, such as smart rooms, interactive virtual reality systems, human detection and environment modeling. The objective of this work focuses on the detection and classification of falls based on variations in human silhouette shape, a key challenge in computer vision. Falls are a major health concern, specifically for the elderly. In this study, the detection is achieved with a multivariate exponentially weighted moving average (MEWMA) monitoring scheme, which is effective in detecting falls because it is sensitive to small changes. Unfortunately, an MEWMA statistic fails to differentiate real falls from some fall-like gestures. To remedy this limitation, a classification stage based on a support vector machine (SVM) is applied on detected sequences. To validate this methodology, two fall detection datasets have been tested: the University of Rzeszow fall detection dataset (URFD) and the fall detection dataset (FDD). The results of the MEWMA-based SVM are compared with three other classifiers: neural network (NN), naïve Bayes and K-nearest neighbor (KNN). These results show the capability of the developed strategy to distinguish fall events, suggesting that it can raise an early alert in the fall incidents.

Adaboost-based algorithm for human action recognition

This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of each class. Then, we apply the Adaboost algorithm for the classification process. We assessed the proposed approach using the UR Fall Detection dataset. In this study six classes of activities are considered namely: walking, standing, bending, lying, squatting, and sitting. Results demonstrate the efficiency of the proposed methodology.

Statistical control chart and neural network classification for improving human fall detection

This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszows fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.