# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 1 Introduction  
The world is facing demographic changes on an unprecedented scale. According to the world health organization (WHO), the number of people over 60 years old is expected to increase from 605 million to 2 billion, between \(11\%\) and \(22\%\) of the world's total population [1]. The elderly are the fastest-growing segment of the world population [2]. One estimate in Europe suggests that between 2004 and 2050 the elderly population will increase by 58 million [3]. In the United States, the population aged 65 and older is anticipated to reach 83.7 million by 2050, almost double its population of 43.1 million in 2012 [4]. In China, it is expected to be 487 million, or nearly 35% of China's total population [5]. Accordingly, the number of frail and dependent older adults will increase. Aging is characterized by a gradual deterioration of physical and mental skills. Among the risks inherent in the elderly, falling remains the most critical and complex [6]. Several factors increase the risk of falls in the elderly: (a) the state of a person's health (e.g., deterioration of the biological and physical state); (b) the environment (e.g., low light, poor accommodation); and (c) behaviors (e.g., alcohol abuse, forgetting to use aid instruments). Nevertheless, the age and fragility of the person remains the main risk factors for falls. Falls are the most important source of domestic accidents and the leading cause of death from unintentional injuries for the elderly. According to one study, 28-35% of peopleaged 65 and over fall each year, and this percentage increases to 32-42% for those aged 70 and over [7]. Falls impact seniors' physical and mental health, and have economic repercussions on both the family and society. The real situation of the fallen elderly becomes vulnerable, mainly because it results in fracture. The psychological impact results in loss of confidence, fear of falling, and occasionally withdrawal into oneself. The cost of falls for the elderly in healthcare spending is higher than that of epilepsy, and is on the same order as the direct costs of mental illnesses like depression, schizophrenia, and dementia [8]. Every year in Europe, fall care costs are estimated at 25 billion Euros [9] and more than USD 50 billion in America [10]. Faced with this major social, economic, and ethical issue for our society, many researchers have invented technical solutions to detect falls automatically. These solutions are based on sensors that can identify a fall and report it by sending alerts in needed cases. The critical step in fall detection is the measurement of the human activity parameters with precision and reliability. Various types of intelligent sensors allow for the acquisition of necessary data. These sensors are classified into wearable sensors (accelerometers [11], gyroscopes [12], magnetometers [13], and RFID [14]) and external sensors (piezoelectric [15], microphone [16], passive infrared [17], single-camera [18], and several cameras [19]). Each category has advantages and disadvantages related to constraints such as cost, operating zone, privacy, the conformability of older adults, and the system's accuracy (Figure 1). The actuation of wearable sensors depends on the subject and body position. Thus, older persons are advised to wear the sensors permanently, which can be particularly inconvenient at night. Most of these sensors are generally refused by the elderly due to their high false alarm rate and the difficulties involved in wearing them during activities of daily living (ADLs) [20]. Consequently, the camera option is the best in detecting falls in the home or other environments such as hospitals and accommodation establishments for dependent older adults thanks to its versatility [21]. Over the last ten years, developments in the field of microelectromechanical systems (MEMS) have immensely evolved the new interfaces of human-machine-nature communications. Since 2010, Microsoft Kinect has significantly evolved a way to communicate with a machine through a single device [22].