Downloads: 8 | Views: 371 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1
Review Papers | Computer Science and Information Technology | United States of America | Volume 13 Issue 10, October 2024 | Rating: 5.6 / 10
Transformation in Elderly Care: Role of Machine Learning and Artificial Intelligence to Improve Fall Detection and Prevention in Long - Term Care Facilities
Bhanu Prakash Manjappasetty Masagali
Abstract: Falls are the leading cause of injury for adults 65 years and older [1]. Over 14 million, or 1 in 4 older adults, report falling every year [2]. Fall incidents result in significant physical, emotional, and economic impacts, with increased hospitalizations and loss of independence. Falls cause over 90% of hip fractures [3] and are a leading cause of trauma - related hospitalizations and a top ten cause of death [4, 5]. Older adults often limit their mobility and physical activity due to fear of falling, which can compromise their health and well - being [6, 7]. Approximately 30% of older adults living independently and up to 60% living in long - term care (LTC) will fall at least once per year, and many will fall repeatedly [8, 9]. Traditional fall prevention strategies, such as physical monitoring by staff, environmental modifications, and exercise - based interventions, are often insufficient due to limitations in staffing and constant supervision. Machine Learning (ML) and Artificial Intelligence (AI) technologies provide advanced solutions to improve fall detection and prevention in LTC facilities. By leveraging real - time data from wearable devices, environmental sensors, and camera - based systems, ML and AI algorithms can offer accurate fall detection, predict high - risk situations, and recommend proactive interventions. This white paper reviews the role of ML and AI in fall detection and prevention, highlighting the potential benefits, challenges, and future directions for integrating these technologies into long - term care environments.
Keywords: Falls, Older Adults, Long - Term Care (LTC), Injury Prevention, Machine Learning (ML), Artificial Intelligence (AI), Fall Detection, Predictive Modeling, Wearable Sensors, Environmental Sensors, Video Surveillance, Data Privacy, Caregiver Burden, Real - Time Monitoring, Personalization, System Integration
Edition: Volume 13 Issue 10, October 2024,
Pages: 563 - 566