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

Abstract

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.

Survey on Fall Detection System CNN based Fall Detection and Health Monitoring System using IOT

“…If a fall occurred, when the smartphone has shut down due to battery drain then an alarm cannot be sent to the emergency centre. Fouzi Harrou [6] et al proposed an Integrated-vision based approach for human fall detection in home environment. This system uses camera which captures relevant pixels by variations in body shape by Generalized Likelihood Ratio (GLR) and then the falls are classified using support vector machine.…”

Efficient fall activity recognition by combining shape and motion features

“…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].…”

“…The human shape is used also in Ref. [16] for fall detection. The authors defined 5 occupancy regions.…”

“…Then, an NB classifier is used to separate fall actions from normal activities. The weakness of these methods [11,16,26] is that success can decrease if there is occlusion or incorrect segmentation of the human shape. Additionally, motion information is not used, yet it can help to improve the results.…”

“…A recent survey [2] presented a taxonomy of fall detection from the perspective of the available fall data. Fall detection systems may be divided into three categories, based on wearable sensors [6][7][8][9][10], computer-vision [11][12][13][14][15][16], and ambient systems [17]. Wearable sensors (e.g., accelerometers, gyroscopes, help buttons, and acoustic sensors) capture information such as human body movements or orientation, or sound; this information is used to determine a fall.…”