Nowadays, fall detection systems are attracting attention in the literature due to the pressing need to alert caregivers promptly when an older adult or individual experiences a fall. Real‐time detection of falls may significantly reduce the risks of severe consequences. Falls among elderly individuals carry substantial health implications and can place a significant strain on the healthcare system in developing countries. While fall detection methods such as context‐aware and wearable devices offer promising solutions, they have limitations in accuracy and usability. However, recent advancements integrate sensor technologies and machine learning algorithms that led to promising developments in fall detection systems, with innovative approaches to improve accuracy and response time. This study examines the key challenges, sensors, and general architecture of fall detection. The study reviewed recent literature about the advancements and ongoing research issues in fall detection. The study discusses the data collection mechanism, feature extraction method, and various machine learning algorithms to consider for developing a fall detection system. The results show that there is no effect in choosing multiple sensors over single sensor. This review offers insight into advancements in fall detection technologies and highlights recommendations for future research and developments.
Sediela et al. (Thu,) studied this question.