Efficient fall activity recognition by combining shape and motion features
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Vol. 6, No. 3, September 2020, 247–263
Research Article
Efficient fall activity recognition by combining shape and motion features Abderrazak Iazzi1 (
), Mohammed Rziza1 , and Rachid Oulad Haj Thami2
c The Author(s) 2020.
Abstract This paper presents a vision-based system for recognizing when elderly adults fall. A fall is characterized by shape deformation and high motion. We represent shape variation using three features, the aspect ratio of the bounding box, the orientation of an ellipse representing the body, and the aspect ratio of the projection histogram. For motion variation, we extract several features from three blocks corresponding to the head, center of the body, and feet using optical flow. For each block, we compute the speed and the direction of motion. Each activity is represented by a feature vector constructed from variations in shape and motion features for a set of frames. A support vector machine is used to classify fall and non-fall activities. Experiments on three different datasets show the effectiveness of our proposed method. Keywords fall detection; elderly people; shape features; motion features; classification
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Introduction
According to a report [1], each year, millions of elderly people (65 years old and over) fall. More than one in four elderly people fall each year, but less than half tell their doctors. Falling once doubles an elderly adult’s chances of falling again. Falls are the leading cause of injury in adults aged 65 or older. Falls can also have an impact on the person both economically and psychologically [2]. A serious fall 1 LRIT, RABAT IT CENTER, Faculty of Sciences, University of Mohammed 5 in Rabat, Morocco. E-mail: A. Iazzi, [email protected] ( ); M. Rziza, [email protected]. 2 ADMIR LAB, IRDA, RABAT IT CENTER, ENSIAS, University of Mohammed 5 in Rabat, Morocco. E-mail: [email protected]. Manuscript received: 2020-02-16; accepted: 2020-06-08 247
can result in decreased functional independence and quality of life. Fear of falling and loss of mobility and independence are frequent and often serious consequences of a fall [2]. The risk of falling increases with age for many reasons, including overall weakness and frailty, balance problems, cognitive problems, vision problems, medication, acute illness, and other environmental hazards. As most elderly adults live alone at home or in nursing homes [3], falls can prove fatal if the elderly person does not get timely assistance. Effective detection and prevention of falls could substantially reduce disability among the elderly. Hence, there is an urgent need to develop an efficient fall detection and prevention system for monitoring elderly people. When an elderly person falls, a fall detection system can send an alarm signal to caregivers (e.g., hospitals, health centers, and family members). Such systems have recently become a significant research topic for many scientists worldwide, and many systems have been proposed for fall detection and fall prevention to help elderly people live in a sec
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