Fall detection based on shape deformation
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Fall detection based on shape deformation Fairouz Merrouche 1 & Nadia Baha 1 Received: 31 January 2019 / Revised: 3 October 2019 / Accepted: 1 November 2019 # Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
Older people living alone are facing serious risks. Falls are the main risk that menace their lives. In this paper, a new vision-based method for fall detection is proposed to allow older people to live independently and safely. The proposed method uses shape deformation and motion information to distinguish between normal activity and fall. The main contribution of this paper consists on the proposition of a new descriptor based on silhouette deformation, as well as, a new image sequence representation is proposed to capture the change between different postures, which is discriminant information for action classification. Experimental results are conducted on two states-of-the art datasets (SDU fall and UR Fall dataset) and a comparative study is presented. The results obtained show the performance of the proposed method to differentiate between fall events and normal activity. The accuracy achieved is up to 98.41% with the SDU fall dataset and 95.45% with URFall dataset. Keywords Fall detection . Human activity recognition . Image processing . Kinect . Video monitoring . Depth information
1 Introduction In the last century, the elderly population is increasing and falls are the major health risks that debilitate this growing community and even threaten its life. According to the study by [1], from 28 to 35% of people aged over 65 fall each year and this percentage increases for people aged over 70. Falls lead to severe consequences such as hospitalization due to hip fractures,
* Fairouz Merrouche [email protected] Nadia Baha [email protected]
1
Computer Science Department, University of Science and Technology USTHB, Algiers, Algeria
Multimedia Tools and Applications
upper limb injuries and traumatic brain injuries. They cause fear of falling and reduction of normal activities [29]. Falls also affect the family, society and economy. Many researches have been conducted around the world to develop efficient systems to prevent fall incidents and most marketing systems use portable sensors such as accelerometers, gyroscopes that force the user to wear them. Therefore wearing these sensors is a major disadvantage of these systems, because the elderly usually forget to wear them or to recharge them. To deal with this problem, other researches have used sensors mounted in the environment, usually based on vibration, sound, vision and infrared sensors [7]. In the last years, computer vision area has known a fast evolution, which interests more developers. Video monitoring systems use cameras to detect fall. These sensors yield information about the daily activity of a person. The main advantage of such systems is that the person does not need to wear any device. However, such systems do not respect the privacy of older people. Kinect sensor is a camera that has gained great i
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