Fall detection via human posture representation and support vector machine

Abstract

Accidental falls of elderly people are a major cause of fatal injuries, especially for those living alone. We present a novel vision-based fall detection approach that analyzes an extracted human body using described human postures. First, a human body extracted by a background subtraction technique is located by a minimum area-enclosing ellipse. Then, a normalized directional histogram is developed around the center of the ellipse to represent a human posture by multidirectional statistical analysis. After that, 12 static and 8 dynamic features are derived from the normalized directional histogram. These features are fed into a directed acyclic graph support vector machine to distinguish four closely related human postures (standing, crouching, lying, and sitting). A fall-like accident is detected by counting the occurrences of lying postures in a short temporal window. After conducting majority voting, a fall event is determined by immobility verification. From the experimental results, an overall accuracy of 97.1% is obtained for recognition of the four postures, and only 1.0% of postures are misclassified as lying postures. Our fall detection system achieves up to 95.2% fall detection accuracy on a public fall dataset.

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

“…In contrast, wearable technologies are composed of accelerometer, gyroscope and force sensors, etc. Vision-based gait analysis use either a video camera [14], [15], a thermal vision sensor [16], [17] or a depth camera [18], [19]. Alternatively, the environment-based gait assessment rely on floor-deployed pressure sensors [20], [21] and infrared sensors [22], [23].…”

Efficient fall activity recognition by combining shape and motion features

“…From Table 8, we observe that our proposed scheme result is better than the methods presented in Refs. [12,15,16,[20][21][22]. Methods in Refs.…”

“…Support vector machines (SVM), convolutional neural networks (CNN), extreme learning machines (ELM), Gaussian mixture models (GMM), naive Bayes (NB), and k-nearest neighbors (KNN) are the most common machine learning techniques used in Refs. [11,[14][15][16][19][20][21][22][23].…”

“…While their experiment results show high accuracy, the processing time is excessive. Recently, Fan et al [21] extracted several features from an ellipse computed from a silhouette to describe human posture. They then developed an SVM model to classify human posture related to a fall in each frame.…”

“…Many approaches have been proposed for fall detection based on analysing normal and abnormal activities with computer vision, and a few datasets are publicly available for testing. These approaches can be classified as thresholding based [12,13,18] or machine learning based [11,14,15,[19][20][21][22][23][24]. In this paper, we propose a new fall recognition system based on machine learning using a single camera.…”