Bibliografia

1. Istat. Invecchiamento attivo e condizioni di vita degli anziani in Italia. 2020. Disponibile a: https://www.istat.it/it/files//2020/08/Invecchiamento-attivo-e-condizioni-di-vita-degli-anziani-in-Italia.pdf. Consultato il 20 ottobre 2020.
2. Istat. Il futuro demografico del paese. 2018. Disponibile a: https://www.istat.it/it/files//2018/05/previsioni_demografiche.pdf. Consultato il 20 ottobre 2020.
3. NICE. Falls in older people: assessing risk and prevention. Guidelines CG161. 2013.
4. Rajagopalan R, Litvan I, Jung T-P. Fall prediction and prevention systems: recent trends, challenges, and future research directions. Sensors. 2017;17(11):2509.
5. Molina KI, Ricci NA, de Moraes SA, Perracini MR. Virtual reality using games for improving physical functioning in older adults: a systematic review. J Neuroeng Rehabil. 2014; 11:1 56.
6. Chase CA, Mann K, Wasek S, Arbesman M. Systematic review of the effect of home modification and fall prevention programs on falls and the performance of community-dwelling older adults. Am J Occup Ther. 2012;66(3):284-91.
7. de Amorim JS, Leite R, Brizola R, Yonamine C. Virtual reality therapy for rehabilitation of balance in the elderly: a systematic review and META-analysis. Adv Rheumatol. 2018;58(1):18.
8. Cella A, Luca AD, Squeri V, et al. Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults. PLOS ONE. 2020;15(6): e0234904.
9. Wang K, Redmond J, Lovell N. Monitoring for Elderly Care: The Role of Wearable Sensors in Fall Detection and Fall Prediction Research. In: Eren H, Webster JG. TeleMedicine and electronic medicine. CRC press, 2015; pp. 619-51.
10. Howcroft J, Kofman J, Lemaire ED. Review of fall risk assessment in geriatric populations using inertial sensors. J Neuroeng Rehabil. 2013;10(1):91.
11. Pierleoni P, Belli A, Palma L, et al. A high reliability wearable device for elderly fall detection. IEEE Sensors Journal. 2015;15(8):4544-53.