Yaşlılarda Düşme Tespiti ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı

Melda Başer Seçer

doi:10.47141/geriatrik.1552575

Derleme

Use of Artificial Intelligence Technologies in Fall Detection and Prevention in the Elderly

Yıl 2025, , 72 - 83, 30.04.2025

Melda Başer Seçer

https://doi.org/10.47141/geriatrik.1552575

Öz

The rapidly increasing older people population around the world brings with it many social difficulties and economic burdens. Falls, which are more common in individuals aged 65 and over, significantly affect mobility, general health, injury, death and admission to healthcare institutions in this population. A major contributing factor to falls is that many seniors live alone and it is difficult to supervise and monitor these seniors. Therefore, e-health technologies are of critical importance for elderly people living alone. Technological systems, such as artificial intelligence (AI) and the internet of things (loT), which emerged with the rapid development of technology and are increasingly used in health and many other areas, have opened an important page in monitoring the health status of the elderly, fall detection and rescue systems. These systems offer important solutions to prevent falls and reduce their impact by quickly transporting elderly people to emergency services or by detecting falls early and notifying the healthcare institution/patient’s relative/caregiver. Thus, it reduces the risk of death, injury and fall-related health expenses. To help the elderly live easier and better, cutting-edge studies using AI systems and the loT such as sensor, infrared, hardware technologies, machine learning and deep learning have shown that these technologies can be used to monitor falls in older adults in real-time and long-term without human intervention. It shows that it will be one of the best solutions for prevention.

Anahtar Kelimeler

Artificial Intelligence, Falls, Elderly

Kaynakça

1. Ferrer A, Formiga F, Sanz H, et al. Multifactorial assessment and targeted intervention to reduce falls among the oldest-old: a randomized controlled trial. Clin Interv Aging. 2014; 9: 383-394.
2. Choi M, Hector M. Effectiveness of intervention programs in preventing falls: a systematic review of recent 10 years and meta-analysis. J Am Med Dir Assoc. 2012; 13: 188.e13-.e21.
3. Burns E. Deaths from falls among persons aged ≥ 65 years-United States, 2007-2016. MMWR Morbidity and mortality weekly report. 2018; 67.
4. World Health Organization. (2021). Falls. Fact Sheets. Retrieved from: https://www.who.int/news-room/fact-sheets/detail/falls
5. Turner S, Kisser R, Rogmans W. Falls among older adults in the EU-28: Key facts from the available statistics. EuroSafe, Amsterdam. 2015; 1-5.
6. Burns ER, Stevens JA, Lee R. The direct costs of fatal and non-fatal falls among older adults-United States. J Safety Res. 2016; 58: 99-103.
7. O'Connor S, Gasteiger N, Stanmore E, et al. Artificial intelligence for falls management in older adult care: A scoping review of nurses' role. J Nurs Manag. 2022; 30: 3787-3801.
8. Kim J, Lee W, Lee SH. A systematic review of the guidelines and Delphi study for the multifactorial fall risk assessment of community-dwelling elderly. Int J Environ Res Public Health. 2020; 17: 6097.
9. Kenny RA, Romero-Ortuno R, Kumar P. Falls in older adults. Medicine. 2017; 45: 28-33.
10. Schoene D, Heller C, Aung YN, et al. A systematic review on the influence of fear of falling on quality of life in older people: is there a role for falls? Clin Interv Aging. 2019; 14: 701-719.
11. American Family Physician. U.S. Preventive Services Task Force: interventions to prevent falls in community-dwelling older adults: recommendation statement. Published August 15, 2018. Accessed November 1, 2021. https://www.aafp.org/afp/2018/0815/od1.html
12. Montero-Odasso M, Almeida QJ, Bherer L, et al. Consensus on shared measures of mobility and cognition: from the Canadian Consortium on Neurodegeneration in Aging (CCNA). J Gerontol A Biol Sci Med Sci. 2019; 74: 897-909.
13. Montero-Odasso M, van der Velde N, Martin FC, et al. World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing. 2022; 51: afac205.
14. Spoelstra SL, Given BA, Given CW. Fall prevention in hospitals: an integrative review. Clin Nurs Res. 2012; 21: 92-112.
15. Graham BC. Examining evidence-based interventions to prevent inpatient falls. Medsurg Nurs. 2012; 21: 267-270.
16. Mamum K, Lim J. Association between falls and high-risk medication use in hospitalized Asian elderly patients. Geriatr Gerontol Int. 2009; 9: 276-281.
17. Mohan D, Al-Hamid DZ, Chong PHJ, et al. Artificial Intelligence and IoT in Elderly Fall Prevention: A Review. IEEE Sensors Journal. 2024.
18. Mouha RARA. Internet of things (IoT). Journal of Data Analysis and Information Processing. 2021; 9: 77-101.
19. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020; 92: 807-812.
20. Mukaetova-Ladinska EB, Harwood T, Maltby J. Artificial Intelligence in the healthcare of older people. Archives of Psychiatry and Mental Health. 2020; 4: 007-013.
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39. Gawrońska K, Lorkowski J. Smart homes for the older population: particularly important during the COVID-19 outbreak. Reumatologia. 2021; 2021: 41-46.
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42. Fallis WM, Silverthorne D, Franklin J, et al. Client and responder perceptions of a personal emergency response system: Lifeline. Home Health Care Serv Q. 2007; 26: 1-21.
43. Numata K, Tanaka T, Matsumoto J. A Case Study of Non-specialist Disease Management Using Teladoc Health Support: A Case Report. Cureus. 2024; 16: e60401.
44. Costanzo M, Smeriglio R, Nuovo SD. New technologies and assistive robotics for elderly: A review on psychological variables. Archives of Gerontology and Geriatrics Plus. 2024; 1: 100056.
45. Koh IS, Kang HS. Effects of intervention using PARO on the cognition, emotion, problem behavior, and social interaction of elderly people with dementia. Journal of Korean Academy of Community Health Nursing. 2018; 29: 300-309.
46. Carreira J, Zisserman A. Quo vadis, action recognition? A new model and the kinetics dataset. CoRR abs/1705.07750 (2017). arXiv preprint arXiv:170507750. 2017.
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49. Van Kasteren T, Englebienne G, Kröse BJ. An activity monitoring system for elderly care using generative and discriminative models. Personal and ubiquitous computing. 2010; 14: 489-498.
50. Kaye JA, Maxwell SA, Mattek N, et al. Intelligent systems for assessing aging changes: home-based, unobtrusive, and continuous assessment of aging. J Gerontol B Psychol Sci Soc Sci. 2011; 66(suppl 1): i180-i190.
51. Oh-Park M, Doan T, Dohle C, et al. Technology utilization in fall prevention. Am J Phys Med Rehabil. 2021; 100: 92-99.
52. Choi SD, Guo L, Kang D, et al. Exergame technology and interactive interventions for elderly fall prevention: A systematic literature review. Appl Ergon. 2017; 65: 570-581.
53. Rajagopalan R, Litvan I, Jung T-P. Fall prediction and prevention systems: recent trends, challenges, and future research directions. Sensors. 2017; 17: 2509.
54. Tsukiyama T. In-home health monitoring system for solitary elderly. Procedia Computer Science. 2015; 63: 229-235.
55. Chaccour K, Darazi R, El Hassani AH, et al. From fall detection to fall prevention: A generic classification of fall-related systems. IEEE Sensors Journal. 2016; 17: 812-822.
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Yaşlılarda Düşme Tespiti ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı

Yıl 2025, , 72 - 83, 30.04.2025

Melda Başer Seçer

https://doi.org/10.47141/geriatrik.1552575

Öz

Dünya genelinde, hızla artan yaşlı nüfus, birçok sosyal zorluğu ve ekonomik yükü beraberinde getirmektedir. Altmış beş yaş ve üzeri bireylerde daha sık görülen düşme, bu popülasyonda mobiliteyi, genel sağlığı, yaralanma, ölüm ve sağlık kurumuna başvuru oranını önemli ölçüde etkilemektedir. Düşmelere katkıda bulunan önemli unsurlardan biri, birçok yaşlının yalnız yaşaması ve bu yaşlıları denetleme ve takip etme zorluğudur. Bu nedenle e-sağlık teknolojileri yalnız yaşayan yaşlılar için kritik önem arz etmektedir. Teknolojinin hızlı gelişimiyle ortaya çıkan, yapay zeka (YZ) ve nesnelerin interneti (loT) gibi sağlıkta ve birçok alanda kullanımı artan teknolojik sistemler, yaşlılarda sağlık durumlarının takibi, düşme algılama ve kurtarma sistemlerinde önemli bir sayfa açmıştır. Bu sistemler, acil servislere yaşlıları hızla ulaştırarak ya da düşmenin erken tespitini yapıp sağlık kurumu/hasta yakını/bakıcıya haber vererek düşmelerin önlenmesi ve etkisini azaltmak için önemli çözümler sunmaktadır. Böylece can kaybı, yaralanma ve düşme ile ilişkili sağlık harcamaları riskini azaltmaktadır. Yaşlıların daha kolay ve daha iyi yaşamalarına yardımcı olmak için, sensör, kızılötesi, donanım teknolojileri, makine öğrenimi ve derin öğrenme gibi YZ sistemlerini ve loT’yi kullanan son teknoloji çalışmaları, insan müdahalesi olmadan gerçek zamanlı ve uzun vadeli izlemede bu teknolojilerin yaşlı yetişkinlerde düşme önleme için en iyi çözümlerden biri olacağını göstermektedir.

Anahtar Kelimeler

Yapay Zeka, Düşmeler, Yaşlı

Etik Beyan

Gerekmemektedir

Destekleyen Kurum

yoktur

Teşekkür

yoktur

Kaynakça

1. Ferrer A, Formiga F, Sanz H, et al. Multifactorial assessment and targeted intervention to reduce falls among the oldest-old: a randomized controlled trial. Clin Interv Aging. 2014; 9: 383-394.
2. Choi M, Hector M. Effectiveness of intervention programs in preventing falls: a systematic review of recent 10 years and meta-analysis. J Am Med Dir Assoc. 2012; 13: 188.e13-.e21.
3. Burns E. Deaths from falls among persons aged ≥ 65 years-United States, 2007-2016. MMWR Morbidity and mortality weekly report. 2018; 67.
4. World Health Organization. (2021). Falls. Fact Sheets. Retrieved from: https://www.who.int/news-room/fact-sheets/detail/falls
5. Turner S, Kisser R, Rogmans W. Falls among older adults in the EU-28: Key facts from the available statistics. EuroSafe, Amsterdam. 2015; 1-5.
6. Burns ER, Stevens JA, Lee R. The direct costs of fatal and non-fatal falls among older adults-United States. J Safety Res. 2016; 58: 99-103.
7. O'Connor S, Gasteiger N, Stanmore E, et al. Artificial intelligence for falls management in older adult care: A scoping review of nurses' role. J Nurs Manag. 2022; 30: 3787-3801.
8. Kim J, Lee W, Lee SH. A systematic review of the guidelines and Delphi study for the multifactorial fall risk assessment of community-dwelling elderly. Int J Environ Res Public Health. 2020; 17: 6097.
9. Kenny RA, Romero-Ortuno R, Kumar P. Falls in older adults. Medicine. 2017; 45: 28-33.
10. Schoene D, Heller C, Aung YN, et al. A systematic review on the influence of fear of falling on quality of life in older people: is there a role for falls? Clin Interv Aging. 2019; 14: 701-719.
11. American Family Physician. U.S. Preventive Services Task Force: interventions to prevent falls in community-dwelling older adults: recommendation statement. Published August 15, 2018. Accessed November 1, 2021. https://www.aafp.org/afp/2018/0815/od1.html
12. Montero-Odasso M, Almeida QJ, Bherer L, et al. Consensus on shared measures of mobility and cognition: from the Canadian Consortium on Neurodegeneration in Aging (CCNA). J Gerontol A Biol Sci Med Sci. 2019; 74: 897-909.
13. Montero-Odasso M, van der Velde N, Martin FC, et al. World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing. 2022; 51: afac205.
14. Spoelstra SL, Given BA, Given CW. Fall prevention in hospitals: an integrative review. Clin Nurs Res. 2012; 21: 92-112.
15. Graham BC. Examining evidence-based interventions to prevent inpatient falls. Medsurg Nurs. 2012; 21: 267-270.
16. Mamum K, Lim J. Association between falls and high-risk medication use in hospitalized Asian elderly patients. Geriatr Gerontol Int. 2009; 9: 276-281.
17. Mohan D, Al-Hamid DZ, Chong PHJ, et al. Artificial Intelligence and IoT in Elderly Fall Prevention: A Review. IEEE Sensors Journal. 2024.
18. Mouha RARA. Internet of things (IoT). Journal of Data Analysis and Information Processing. 2021; 9: 77-101.
19. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020; 92: 807-812.
20. Mukaetova-Ladinska EB, Harwood T, Maltby J. Artificial Intelligence in the healthcare of older people. Archives of Psychiatry and Mental Health. 2020; 4: 007-013.
21. Oakden-Rayner L. Reply to 'Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists' by Haenssle et al. Annals of oncology : official journal of the European Society for Medical Oncology. 2019; 30: 854.
22. Yang X, Wang Y, Byrne R, et al. Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev. 2019; 119: 10520-10594.
23. Wu N, Phang J, Park J, et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging. 2019; 39: 1184-1194.
24. Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019; 25: 1054-1056.
25. Huang P, Lin CT, Li Y, et al. Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method. The Lancet Digit Health. 2019; 1: e353-e362.
26. Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020; 580: 252-256.
27. Rajpurkar P, Chen E, Banerjee O, et al. AI in health and medicine. Nat Med. 2022; 28: 31-38.
28. Gainza P, Sverrisson F, Monti F, et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat Methods. 2020; 17: 184-192.
29. Cristiano S, Leal A, Phallen J, et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature. 2019; 570: 385-389.
30. Stokes J, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell. 2020; 180: 688-702.e13.
31. Claassen J, Doyle K, Matory A, et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med. 2019; 380: 2497-2505.
32. Porumb M, Stranges S, Pescapè A, et al. Precision medicine and artificial intelligence: a pilot study on deep learning for hypoglycemic events detection based on ECG. Sci Rep. 2020; 10: 170.
33. Chan J, Raju S, Nandakumar R, et al. Detecting middle ear fluid using smartphones. Sci Transl Med. 2019; 11: eaav1102.
34. Willett FR, Avansino DT, Hochberg LR, et al. High-performance brain-to-text communication via handwriting. Nature. 2021; 593: 249-254.
35. Kehl KL, Elmarakeby H, Nishino M, et al. Assessment of deep natural language processing in ascertaining oncologic outcomes from radiology reports. JAMA Oncol. 2019; 5: 1421-1429.
36. Weck M, Afanassieva M. Toward the adoption of digital assistive technology: Factors affecting older people's initial trust formation. Telecommunications Policy. 2023; 47: 102483.
37. Sapci AH, Sapci HA. Innovative assisted living tools, remote monitoring technologies, artificial intelligence-driven solutions, and robotic systems for aging societies: systematic review. JMIR Aging. 2019; 2: e15429.
38. Marikyan D, Papagiannidis S, Alamanos E. A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change. 2019; 138: 139-154.
39. Gawrońska K, Lorkowski J. Smart homes for the older population: particularly important during the COVID-19 outbreak. Reumatologia. 2021; 2021: 41-46.
40. Lunardini F, Luperto M, Romeo M, et al., editors. The movecare project: Home-based monitoring of frailty. 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI); 2019: IEEE.
41. Terleph TA, Malaivijitnond S, Reichard UH. Age related decline in female lar gibbon great call performance suggests that call features correlate with physical condition. BMC Evol Biol. 2016; 16: 1-13.
42. Fallis WM, Silverthorne D, Franklin J, et al. Client and responder perceptions of a personal emergency response system: Lifeline. Home Health Care Serv Q. 2007; 26: 1-21.
43. Numata K, Tanaka T, Matsumoto J. A Case Study of Non-specialist Disease Management Using Teladoc Health Support: A Case Report. Cureus. 2024; 16: e60401.
44. Costanzo M, Smeriglio R, Nuovo SD. New technologies and assistive robotics for elderly: A review on psychological variables. Archives of Gerontology and Geriatrics Plus. 2024; 1: 100056.
45. Koh IS, Kang HS. Effects of intervention using PARO on the cognition, emotion, problem behavior, and social interaction of elderly people with dementia. Journal of Korean Academy of Community Health Nursing. 2018; 29: 300-309.
46. Carreira J, Zisserman A. Quo vadis, action recognition? A new model and the kinetics dataset. CoRR abs/1705.07750 (2017). arXiv preprint arXiv:170507750. 2017.
47. Yeung S, Rinaldo F, Jopling J, et al. A computer vision system for deep learning-based detection of patient mobilization activities in the ICU. NPJ Digit Med. 2019; 2: 11.
48. Luo Z, Hsieh J-T, Balachandar N, et al., editors. Vision-Based Descriptive Analytics of Seniors’ Daily Activities for Long-Term Health Monitoring. Machine Learning for Healthcare (MLHC) Conference 2018; 2018.
49. Van Kasteren T, Englebienne G, Kröse BJ. An activity monitoring system for elderly care using generative and discriminative models. Personal and ubiquitous computing. 2010; 14: 489-498.
50. Kaye JA, Maxwell SA, Mattek N, et al. Intelligent systems for assessing aging changes: home-based, unobtrusive, and continuous assessment of aging. J Gerontol B Psychol Sci Soc Sci. 2011; 66(suppl 1): i180-i190.
51. Oh-Park M, Doan T, Dohle C, et al. Technology utilization in fall prevention. Am J Phys Med Rehabil. 2021; 100: 92-99.
52. Choi SD, Guo L, Kang D, et al. Exergame technology and interactive interventions for elderly fall prevention: A systematic literature review. Appl Ergon. 2017; 65: 570-581.
53. Rajagopalan R, Litvan I, Jung T-P. Fall prediction and prevention systems: recent trends, challenges, and future research directions. Sensors. 2017; 17: 2509.
54. Tsukiyama T. In-home health monitoring system for solitary elderly. Procedia Computer Science. 2015; 63: 229-235.
55. Chaccour K, Darazi R, El Hassani AH, et al. From fall detection to fall prevention: A generic classification of fall-related systems. IEEE Sensors Journal. 2016; 17: 812-822.
56. Mastorakis G, Makris D. Fall detection system using Kinect’s infrared sensor. Journal of Real-Time Image Processing. 2014; 9: 635-646.
57. Yang L, Ren Y, Zhang W. 3D depth image analysis for indoor fall detection of elderly people. Digital Communications and Networks. 2016; 2: 24-34.
58. Yacchirema D, de Puga JS, Palau C, et al. Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Personal and Ubiquitous Computing. 2019; 23: 801-817.
59. de la Concepción MÁÁ, Morillo LMS, García JAÁ, et al. Mobile activity recognition and fall detection system for elderly people using Ameva algorithm. Pervasive and Mobile Computing. 2017; 34: 3-13.
60. He J, Bai S, Wang X. An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network classifier. Sensors. 2017; 17: 1393.
61. Santoyo-Ramón JA, Casilari E, Cano-García JM. Analysis of a smartphone-based architecture with multiple mobility sensors for fall detection with supervised learning. Sensors. 2018; 18: 1155.
62. Casilari E, Oviedo-Jiménez MA. Automatic fall detection system based on the combined use of a smartphone and a smartwatch. PloS One. 2015; 10: e0140929.
63. Weiss A, Brozgol M, Dorfman M, et al. Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings. Neurorehabil Neural Repair. 2013; 27: 742-752.
64. Mancini M, Schlueter H, El-Gohary M, et al. Continuous monitoring of turning mobility and its association to falls and cognitive function: a pilot study. J Gerontol A Biol Sci Med Sci. 2016; 71: 1102-1108.
65. Najafi B, Aminian K, Loew F, et al. Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Trans Biomed Eng. 2002; 49: 843-851.
66. Nait Aicha A, Englebienne G, Van Schooten KS, et al. Deep learning to predict falls in older adults based on daily-life trunk accelerometry. Sensors. 2018; 18: 1654.
67. Speiser JL, Callahan KE, Houston DK, et al. Machine learning in aging: an example of developing prediction models for serious fall injury in older adults. J Gerontol A Biol Sci Med Sci. 2021; 76: 647-654.
68. Vaiyapuri T, Lydia EL, Sikkandar MY, et al. Internet of things and deep learning enabled elderly fall detection model for smart homecare. IEEE Access. 2021; 9: 113879-113888.
69. Hsieh C-Y, Shi W-T, Huang H-Y, et al., editors. Machine learning-based fall characteristics monitoring system for strategic plan of falls prevention. 2018 IEEE International Conference on Applied System Invention (ICASI); 2018: IEEE.
70. Arnold CM, Faulkner RA. The history of falls and the association of the timed up and go test to falls and near-falls in older adults with hip osteoarthritis. BMC Geriatr. 2007; 7: 1-9.
71. Greene BR, O’Donovan A, Romero-Ortuno R, et al. Quantitative falls risk assessment using the timed up and go test. EEE Trans Biomed Eng. 2010; 57: 2918-2926.
72. Weiss A, Herman T, Plotnik M, et al. An instrumented timed up and go: the added value of an accelerometer for identifying fall risk in idiopathic fallers. Physiol Meas. 2011; 32: 2003-2018.
73. Roshdibenam V, Jogerst GJ, Butler NR, et al. Machine learning prediction of fall risk in older adults using timed up and go test kinematics. Sensors. 2021; 21: 3481.
74. WU C, LAM CH, XHAFA F, et al. Case study in fall prevention in indoor environments. IoT for Elderly, Aging and eHealth: Quality of Life and Independent Living for the Elderly: Springer; 2022. p. 87-98.
75. Mostafa SA, Mustapha A, Mohammed MA, et al. A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application. Int J Med Inform. 2018; 112: 173-184.
76. Arcelus A, Jones MH, Goubran R, et al., editors. Integration of smart home technologies in a health monitoring system for the elderly. 21st international conference on advanced information networking and applications workshops (AINAW'07); 2007: IEEE.
77. Zhang Q, Li M, Wu Y. Smart home for elderly care: development and challenges in China. BMC Geriatr. 2020; 20: 1-8.

Toplam 77 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	Türkçe
Konular	Geriatri ve Gerontoloji
Bölüm	Derleme
Yazarlar	Melda Başer Seçer 0000-0003-3943-2727
Yayımlanma Tarihi	30 Nisan 2025
Gönderilme Tarihi	18 Eylül 2024
Kabul Tarihi	13 Nisan 2025
Yayımlandığı Sayı	Yıl 2025

Kaynak Göster

APA	Başer Seçer, M. (2025). Yaşlılarda Düşme Tespiti ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı. Geriatrik Bilimler Dergisi, 8(1), 72-83. https://doi.org/10.47141/geriatrik.1552575
AMA	Başer Seçer M. Yaşlılarda Düşme Tespiti ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı. GBD. Nisan 2025;8(1):72-83. doi:10.47141/geriatrik.1552575
Chicago	Başer Seçer, Melda. “Yaşlılarda Düşme Tespiti Ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı”. Geriatrik Bilimler Dergisi 8, sy. 1 (Nisan 2025): 72-83. https://doi.org/10.47141/geriatrik.1552575.
EndNote	Başer Seçer M (01 Nisan 2025) Yaşlılarda Düşme Tespiti ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı. Geriatrik Bilimler Dergisi 8 1 72–83.
IEEE	M. Başer Seçer, “Yaşlılarda Düşme Tespiti ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı”, GBD, c. 8, sy. 1, ss. 72–83, 2025, doi: 10.47141/geriatrik.1552575.
ISNAD	Başer Seçer, Melda. “Yaşlılarda Düşme Tespiti Ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı”. Geriatrik Bilimler Dergisi 8/1 (Nisan 2025), 72-83. https://doi.org/10.47141/geriatrik.1552575.
JAMA	Başer Seçer M. Yaşlılarda Düşme Tespiti ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı. GBD. 2025;8:72–83.
MLA	Başer Seçer, Melda. “Yaşlılarda Düşme Tespiti Ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı”. Geriatrik Bilimler Dergisi, c. 8, sy. 1, 2025, ss. 72-83, doi:10.47141/geriatrik.1552575.
Vancouver	Başer Seçer M. Yaşlılarda Düşme Tespiti ve Önlemede Yapay Zeka Teknolojilerinin Kullanımı. GBD. 2025;8(1):72-83.

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