Development of an intrinsic health risk prediction model for camera surveillance of elderly people living alone

  • Statistical workplace. Statistics on the aged of 2021. (2021).

  • Oh, H. Relationship between social capital, despair, and high quality of life in older individuals collaborating in bodily exercise. Korean J. Phys. Educ. 53535–547 (2014).

    Google Scholar

  • Waern, M., Rubenowitz, E. & Wilhelmson, Ok. Predictors of suicide within the aged. Gerontology 49328–334 (2003).

    Article
    PubMed

    Google Scholar

  • Dong, X. et al. Self-neglect and abuse and mortality threat in older adults in a community-dwelling inhabitants. JAMA 302517–526 (2009).

    Article
    CASE
    PubMed
    PubMed Center

    Google Scholar

  • Chan, A., Malhotra, C., Malhotra, R. & Østbye, T. Dwelling circumstances, social networks and depressive signs amongst aged women and men in Singapore. Int. J. Geriatr. Psychiatry 26630–639 (2011).

    Article
    PubMed

    Google Scholar

  • Choi, S., Kim, C., Kang, Y. & Youm, S. Anomaly detection system primarily based on the evaluation of human habits patterns within the residential house. J. Supercomputer. 779248–9265 (2021).

    Article

    Google Scholar

  • Camp, N et al. Expertise used to acknowledge actions of each day dwelling in community-dwelling older adults. Int. J. About. Res. Public well being 18163 (2021).

    Article

    Google Scholar

  • Received, J., Kim, C., Choi, S., Youm, S., and Kang, YS Pose identification process primarily based on TensorFlow object detection API for emergency scenario detection for aged individuals dwelling alone. 726–728 (Korea Institute of Data Scientists and Engineers, 2018).

  • Kim, G. & Park, S. Exercise detection from electrical energy consumption and communications utilization information to observe lonely deaths. Sensors 213016 (2021).

    Article
    ADS
    PubMed
    PubMed Center

    Google Scholar

  • Vermeulen, J., Neyens, JC, van Rossum, E., Spreeuwenberg, MD & de Witte, LP BMC Geriatr. 111–11 (2011).

    Article

    Google Scholar

  • Gold, DA A assessment of instrumental evaluation actions of each day dwelling in older adults with gentle cognitive impairment. J. Clin. Exp. Neuropsychol. 3411–34 (2012).

    Article
    PubMed

    Google Scholar

  • Bavazzano, A. et al. Purposeful evaluation of Alzheimer’s sufferers throughout scientific trials: a assessment. Camber. Gerontol. Geriatrics. 2627–32 (1998).

    Article

    Google Scholar

  • Yang, Y. et al. Actions of each day dwelling and dementia. Demented. Neurocognitive. Dysfunction. 1129-37 (2012).

    Article

    Google Scholar

  • Jang, J. Comparability of actions of each day dwelling variations with stage of dementia. J. Korea Acad. Ind. Cooper. Soc. 18557–563 (2017).

    Google Scholar

  • Morley, JE & Vellas, B. COVID-19 and older grownup. J. Nutr. Well being Ageing 24364–365 (2020).

    Article
    CASE
    PubMed
    PubMed Center

    Google Scholar

  • Lim, WS et al. COVID-19 and the aged in Asia: the Asian Sarcopenia Activity Power requires motion. Geriatrics. Gerontol. Int. 20547-558 (2020).

    Article
    PubMed
    PubMed Center

    Google Scholar

  • Kim, J., Kim, Y. & Ha, J. Modifications in each day life through the COVID-19 pandemic amongst South Korean aged individuals with power sicknesses: a qualitative examine. Int. J. About. Res. Public well being 186781 (2021).

    Article
    CASE
    PubMed
    PubMed Center

    Google Scholar

  • Plagg, B., Engl, A., Piccoliori, G. & Eisendle, Ok. Extended social isolation of older individuals throughout COVID-19: between advantages and harms. Camber. Gerontol. Geriatrics. 89104086 (2020).

    Article
    CASE
    PubMed
    PubMed Center

    Google Scholar

  • Kim, M., Eo, Y. & Kim, S. A examine of despair within the aged by particular person and neighborhood results. Well being Soc. Wellness Rev. 39192–221 (2019).

    Article

    Google Scholar

  • Chang, S. & Kim, S. The typology of social networks amongst aged individuals dwelling alone in Busan, despair and self-neglect. Korean J. Gerontol. Soc. Welfare 72245-273 (2017).

    Article

    Google Scholar

  • Schlenker, E. Diet and ageing. 2nd ed. 186–195 (WCB McGraw-Hill, 1993).

  • Solomons, NW Diet and Ageing: Potentials and Challenges for Analysis in Growing International locations. Nutr. Spherical. 50224-229 (1992).

    Article
    CASE
    PubMed

    Google Scholar

  • Yao, G., Lei, T. & Zhong, J. A assessment of motion recognition primarily based on a convolutional neural community. Sample recognition. Lett. 11814–22 (2019).

    Article
    ADS

    Google Scholar

  • Moon, J., Kim, H., and Park, J. Developments in temporal motion detection in untrimmed movies. Electron. Telecommun. Tendencies 3520–33 (2020).

    Google Scholar

  • Wu, D., Sharma, N. & Blumenstein, M. in 2017 Worldwide Joint Convention on Neural Networks (IJCNN). 2865–2872 (IEEE, 2017).

  • Feichtenhofer, C., Fan, H., Malik, J. & He, Ok. SlowFast Networks for Video Recognition. in Proceedings of the IEEE/CVF Worldwide Convention on Laptop Imaginative and prescient. 6202–6211.

  • Carreira, J. & Zisserman, A. Quo Vadis, motion recognition? A brand new mannequin and kinetic dataset. in Proceedings of the IEEE Convention on Laptop Imaginative and prescient and Sample Recognition. 6299–6308.

  • Duan, H. et al. Revisiting skeleton-based motion recognition. arXiv preprint arXiv:2104.13586 (2021).

  • Yan, S., Xiong, Y. & Lin, D. Skeleton-based motion recognition spatial time-graph convolutional networks. in Thirty-Second AAAI Convention on Synthetic Intelligence (2018).

  • Vemulapalli, R., Arrate, F. & Chellappa, R. Human motion recognition by representing 3D skeleton-based motion recognition. in Proceedings of the IEEE Convention on Laptop Imaginative and prescient and Sample Recognition. 588–595.

  • Du, Y., Wang, W. & Wang, L. Hierarchical recurrent neural community for skeletal-based motion recognition. in Proceedings of the IEEE Convention on Laptop Imaginative and prescient and Sample Recognition. 1110-1118.

  • Jan, J. et al. ETRI-activity 3D: a large-scale RGB-D dataset permitting robots to acknowledge the each day actions of older individuals. in IEEE/RSJ 2020 Worldwide Convention on Clever Robots and Programs (IROS). 10990–10997 (IEEE, 2020).

  • Skeleton-Based mostly Motion Recognition on NTU RGB+D State of the Artwork Papers” with Code. https://paperswithcode.com/sota/skeleton-based-action-recognition-on-ntu-rgbd. Reviewed 7 October 2021 (2021).

  • Solar, Z et al. Human motion recognition from numerous information modalities: a assessment. arXiv preprint arXiv:2012.11866 (2020).

  • Solar, Ok., Xiao, B., Liu, D., and Wang, J. Deep studying of high-resolution illustration for human pose estimation. in Proceedings of the IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition. 5693–5703.

  • Dang, Q., Yin, J., Wang, B., and Zheng, W. 2D Human Pose Estimation Based mostly on Deep Studying: A Survey. Tsinghua Sci. Expertise. 24663–676 (2019).

    Article

    Google Scholar

  • Ren, S., He, Ok., Girshick, R. & Solar, J. Quicker r-cnn: in the direction of real-time object detection with area proposition networks. Adv. Neural data. Deal with. System 201528 (2015).

    Google Scholar

  • JTBC Information. “I am unable to even say a number of phrases a day”… the aged dwelling alone “within the shadow of a non-face to face”. in Youtube. https://www.youtube.com/watch?v=6VzVSW7olWM (2021).

  • Suryadevara, NK & Mukhopadhyay, SC Dwelling monitoring system primarily based on a wi-fi sensor community for the willpower of the well-being of the aged. IEEE Sense. J. 121965-1972 (2012).

    Article
    ADS

    Google Scholar

  • Wagner, F., Basran, J. & Bello-Haas, VD A assessment of monitoring know-how to be used with the aged. J. Geriatr. Phys. The. 3528–34 (2012).

    Article
    PubMed

    Google Scholar

  • Yang, C.-C. & Hsu, Y.-L. Distant monitoring and analysis of each day actions within the dwelling setting. J. Clin. Gerontol. Geriatrics. 397-104 (2012).

    Article
    CASE

    Google Scholar

  • Awais, M., Chiari, L., Ihlen, EA, Helbostad, JL, and Palmerini, L. Bodily exercise classification for free-living older adults. IEEE J. Biomed. Inform about well being. 23197-207 (2019).

    Article
    PubMed

    Google Scholar

  • Matsui, T. et al. Lounge: simplified detection system for the exercise of each day dwelling in an odd dwelling. Sensors 204895 (2020).

    Article
    ADS
    PubMed Center

    Google Scholar

  • Fernando, YPN et al. Laptop imaginative and prescient protected habits monitoring and fall detection system for aged care. in 2021 twenty sixth IEEE Worldwide Convention on Rising Applied sciences and Manufacturing facility Automation (ETFA) (2021).

  • Suzuki, R. et al. Rhythm of each day life and detection of atypical days for aged individuals dwelling alone decided with a monitoring system. J. Telemed. Teleassistance 12208–214 (2006).

    Article
    PubMed

    Google Scholar

  • Leonardi, C. et al. Knock on the door of the elders. in Proceedings of the SIGCHI Convention on Human Elements in Computing Programs (2009).

  • Hine, C., Nilforooshan, R. & Barnaghi, P. Moral concerns within the design and implementation of sensible dwelling take care of dementia. Nurses. Ethics 291035-1046 (2022).

    Article
    PubMed
    PubMed Center

    Google Scholar

  • #Improvement #intrinsic #well being #threat #prediction #mannequin #digicam #surveillance #aged #individuals #dwelling

    Leave a Comment

    Your email address will not be published.