## 1 Introduction  
Recently, the World Health Organization (WHO) reported that there are approximately 684,000 disastrous falls worldwide each year, with a majority of victims being individuals over the age of 60 [1]. This large percentage places it behind road traffic injuries as the leading cause of unintentional injury fatality. Falls are considered as a main public health concern for the elderly around the world, of which over 80% are in low-income and middle-income countries. Without a doubt, the injuries that elderly people sustain as a result of falls have far-reaching effects on their families, as well as for healthcare institutions and society in general [2]. Emergencies can occur without recognition and even without warning. That is why fall detection technology in medical warning systems is such a critical and life-saving feature. If, for any reason, you were not able to reach the assistance button after a fall (or in a medical emergency), a medical alert system's automated fall detection feature could give you a peace of mind that you would still get the care you need [3]. Fall detection devices employ alert systems technology to identify and provide emergency assistance to a senior who is prone to falls [4]. If the user falls, these systems will quickly activate the sensor. The built-in technology can be placed around the neck, around the wrist, or on the waist, depending on the device. For premium service charges, most medical alert companies incorporate the fall detection capability within their medical alert system [5]. Some firms sell fall detection gadgets that can be worn separately from one's medical alert button. The cost of the second device may be added to the monthly subscription plan. In the last few years, fall detection has garnered significant concern from both industry and academic research, as seen in Figure 1. It can be noticed that the number of research articles has increased dramatically. Furthermore, the topic of fall-likelihood prediction, which is based on modern applications concentrated on fall prevention, such as the Internet of Things and artificial intelligence, is also quite important [6]. Accelerometers, a sort of low-power radio wave technology sensor, are used in fall detection systems to continuously monitor the user's movements [6]. Three-axis accelerometers, such as those in smartwatches and smartphones, are employed in state-of-the-art fall detection devices. Some fall detection systems have a built-in tri-axial accelerometer that uses Biosensor's patented algorithms. By detecting unexpected changes in body motions, fall warning detectors can determine whether a user has fallen [7]. The device can assess a person's body position, physical activity, and the smoothness with which movements are being sped up. If these variables are in the danger zone and a fall has happened, the smart device will automatically activate an emergency fall alarm and contact emergency response agents for assistance. One commercial approach for preventing falls is the use of personal emergency response systems (PERS) [8]. Individuals who fall can use these clinical alarm devices to invoke an emergency department by pressing a button. While the PERS system is useful in many instances, it is rendered worthless if the user is completely unconscious or unable to reach the button. Even when the system is available, recent cohort research indicated that roughly 80% of older persons who were wearing a PERS did not use it to call for aid after falling. Passive monitoring approaches have been proposed to identify falls due effectively and precisely to the problems connected with PERS systems [9]. Several options are currently available, the majority of which are wearable devices, cameras, microphones, and pressure sensors implanted beneath the flooring that are incorporated in the domestic surroundings.