Introduction
Falls among older people harm their independence and health. As the global population ages, falls have become a severe public health issue needing novel risk mitigation solutions. The technology addresses the complicated impacts of elderly falls in “Fall Detection and Prevention Systems,” a prominent research topic. This method was chosen to address fall health risks, mainly as the global elderly population develops. Falls affect public health and care. Fall detection systems can prevent falls; therefore, the research investigates them to improve elderly safety and quality of life. An extensive literature review was conducted in IEEE Access, BioMed Research International, and Frontiers in Robotics and AI. Searching for “fall detection,” “fall prevention systems,” “wearable fall detection,” and “elderly fall detection” covered existing technology. This research identified fall detection and prevention system trends, challenges, and creative solutions, laying the framework for a nuanced understanding of the technology’s potential influence on healthcare.
Ren, L., & Peng, Y. (2019). Research of fall detection and fall prevention technologies: A systematic review. IEEE Access, 7, 77702-77722.
Ren and Peng’s 2019 IEEE Access systematic review covers fall research globally. The writers explain fall-related technology jargon and industry issues and trends. Fall detection and prevention technologies, including sensing, low power, and sensor location, are the subject of the systematic review. Literature reviews synthesize and critique fall-related studies. The review integrates numerous subfields to include fall detection and prevention. Fusion-based techniques were trending, according to the analysis. Fall detection is improved via sensor integration, adaptive thresholds, and radio frequency-based systems. It shows the growing knowledge that merging technologies may improve fall-related systems. These trends help researchers and practitioners understand the field’s trajectory. The systematic review addresses fall-related issues and shows technology’s limits. Critical evaluation motivates researchers to fix problems and improve approaches. The report acknowledges challenges and outlines future research to enhance real-world technologies. Geriatric falls have serious health effects, making patient safety and care crucial. The extensive study helps healthcare practitioners understand fall detection technology improvements. Fall-related techniques, classification, and analysis increase system performance and reliability, increasing patient care. This article was chosen for its deep fall-related technology evaluation. The authors’ extensive research covers numerous technologies. The review’s depth and breadth will help healthcare practitioners, academics, and policymakers seeking fall detection and prevention updates. It is a crucial resource for fall-related patient safety since the systematic review educates the field and leads future research and implementation.
Ramachandran, A., & Karuppiah, A. (2020). A survey on recent advances in wearable fall detection systems. BioMed research international, 2020.
Ramachandran and Karuppiah’s 2020 survey on wearable fall detection devices and machine learning explores the evolution of fall detection technology. The paper begins by describing typical fall detection system needs due to the growing elderly population and the need for effective systems. The authors discuss recent improvements and wearable device integration in fall detection systems. Healthcare is adopting smartwatches and other sensor-equipped wearables for their non-intrusiveness and constant monitoring. According to the poll, wearables can detect and prevent falls, affecting patient safety and treatment quality. The article says that machine learning applications in geriatric healthcare improve fall detection technologies. Machine learning enhances fall detection efficiency and accuracy. By analyzing trends and data, machine learning algorithms can improve fall detection and reduce false alarms by adapting to each person. The poll suggests fall detection systems, especially wearable ones, for the aging population. Treatment requires early detection of dangerous elderly falls. The paper states that wearable fall detection devices can detect falls in real-time. Medical aid can be faster and fall injuries reduced. Machine-learning fall detection is better.
Broad datasets let these systems adapt to diverse circumstances and behaviors, boosting detection and lowering false positives. Rapid fall detection allows early interventions and better senior care, improving patient safety. Modern fall detection technology relies on wearable systems and machine learning; hence, this resource was picked. Authors help healthcare professionals understand these systems’ difficulties. A literature review evaluates recent wearable fall detection system advancements in the context of research. Wearables fit the non-invasive, continuous monitoring trend in healthcare. Wearable fall detection devices are simple and provide older adults’ schedules. The study’s focus on machine learning and advanced analytics helps healthcare practitioners keep up with fall detection system technology.
Wang, X., Ellul, J., & Azzopardi, G. (2020). Elderly fall detection systems: A literature survey. Frontiers in Robotics and AI, 7, 71.
Wang, X., Ellul, J., & Azzopardi, G.’s 2020 Frontiers in Robotics and AI literature survey, “Elderly fall detection systems: A literature survey,” discusses sensor network and IoT improvements in senior fall The work addresses sensor fusion and fall detection system sensor constraints, making it beneficial for healthcare professionals. The survey notes that falling is one of the most dangerous events for seniors, emphasizing the need for robust fall detection systems. It highlights the growing older population and the role of sensor networks and IoT in human-computer interaction, using sensor fusion to detect falls. The paper highlights IoT and sensor fusion. Many studies have used wearables and depth cameras to see falls, but false alarms render them ineffective. The survey found that fusing sensor signals improves fall detection system efficacy and reduces false alerts. The survey examines significant fall detection system developments: Senior fall data collection advances are discussed.
Data from sensors provides a more complete picture of falls. Data Transmission: The authors discuss fall-related data transmission advances and fall detection systems’ need for fast and timely data transfer. According to the report, tensor fusion mixes data from many sensors to improve fall detection; Sensor restrictions must be overcome using this strategy. Data Analysis: The survey assesses fall-related data analysis algorithms and approaches for interpretation and identification. Security and Privacy: The authors address concerns about using sensitive health information by securing fall detection system data. The report stresses sensor integration for patient safety and care quality. By reducing false alarms, sensor fusion enhances fall detection. Rapid and effective fall detection reduces fall complications and improves elderly patient outcomes. This study was chosen for its comprehensive assessment of the progress of senior fall detection systems on sensor fusion and IoT. The survey’s sensor limitations can help healthcare practitioners build reliable fall detection systems.
Yacchirema, D., de Puga, J. S., Palau, C., & Esteve, M. (2019). Fall detection system for older adults using IoT and ensemble machine learning algorithm. Personal and Ubiquitous Computing, 23, 801-817.
The intelligent IoTE-Fall system detects elderly indoor falls, according to Yacchirema, de Puga, Palau, and Esteve’s 2019 study. IoT and ensemble machine learning are used in a novel fall detection approach. The IoTE-Fall system’s four machine-learning algorithms promise to improve older patients’ safety and care. The elderly fall risk is a significant public health issue; hence, IoTE-Fall was designed. Falls can cause seniors to lose mobility, function, and quality of life if left untreated. Given these catastrophic consequences, the study creates a complicated system that uses IoT and ensemble machine learning to detect falls quickly. Decision trees, ensemble methods, logistic regression, and deepness analysis IoTE-Fall. These algorithms improve fall detection accuracy and efficiency, making them crucial. The paper compares algorithms by AUC ROC, training time, and testing time. According to the article, ioTE-Fall meets 94% accuracy, precision, sensitivity, and specificity standards. IoTE-Fall substantially impacts patient safety and care. The system stresses fall detection and management to prevent geriatric functional impairment. IoT lets 6LowPAN wearables collect real-time 3D accelerometer data. It enables the device to follow senior participants’ movements in real time for reliable fall detection. Innovative fall detection employing cutting-edge technologies was chosen. IoT and ensemble machine learning improve fall detection. Healthcare practitioners seeking advanced fall prevention technologies should consider IoTE-Fall’s performance KPIs. This research benefits healthcare professionals. First, the IoTE-Fall system’s focus on IoT and ensemble machine learning stresses the need to develop fall detection systems with several technologies. Second, the study’s comprehensive machine learning algorithm evaluation helps fall detection system makers choose and use them. Rapid fall detection reduces unfavorable geriatric outcomes and may affect patient safety. Creative methods and appealing outcomes make this information beneficial for healthcare practitioners deploying advanced fall prevention devices.
Conclusion
Combining critical data from each study reveals that sensor networks, wearable devices, and machine-learning algorithms improve fall detection and prevention. Rapid sensor network growth, the Internet of Things, and aging populations affect healthcare technology selection. Patient safety requires organizational policy, resources, and technology. The literature supports fall detection technology. Fusing sensor inputs, machine learning, and IoT improves fall-related system accuracy, false alarms, and robustness. This device helps hospitals satisfy geriatric fall detection needs. Fall consequences are reduced, and help is faster, enhancing patient care and satisfaction. Advanced technologies improve patient care, interdisciplinary team productivity, satisfaction, and retention. According to the annotated bibliography, fall detection and prevention technologies enhance patient safety and healthcare quality. Healthcare practitioners can improve geriatric fall detection and patient well-being via sensor networks, wearables, and machine learning algorithms.
References
Ramachandran, A., & Karuppiah, A. (2020). A survey on recent advances in wearable fall detection systems. BioMed research international, 2020.
Ren, L., & Peng, Y. (2019). Research of fall detection and fall prevention technologies: A systematic review. IEEE Access, 7, 77702-77722.
Wang, X., Ellul, J., & Azzopardi, G. (2020). Elderly fall detection systems: A literature survey. Frontiers in Robotics and AI, 7, 71.
Yacchirema, D., de Puga, J. S., Palau, C., & Esteve, M. (2019). Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Personal and Ubiquitous Computing, 23, 801-817.
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