Development of an intrinsic health risk prediction model for camera-based monitoring of older adults living alone

  • Statistical Office. 2021 Older Adult Statistics. (2021).

  • Oh, H. Relationship between social capital, depression and quality of life in elderly people participating in physical activity. Korean J. Phys. Educ. 53, 535–547 (2014).

    Google Scholar 

  • Waern, M., Rubenowitz, E. & Wilhelmson, K. Predictors of suicide in the old elderly. Gerontology 49, 328–334 (2003).

    Article 
    PubMed 

    Google Scholar 

  • Dong, X. et al. Elder self-neglect and abuse and mortality risk in a community-dwelling population. JAMA 302, 517–526 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chan, A., Malhotra, C., Malhotra, R. & Østbye, T. Living arrangements, social networks and depressive symptoms among older men and women in Singapore. Int. J. Geriatr. Psychiatry 26, 630–639 (2011).

    Article 
    PubMed 

    Google Scholar 

  • Choi, S., Kim, C., Kang, Y. & Youm, S. Human behavioral pattern analysis-based anomaly detection system in residential space. J. Supercomput. 77, 9248–9265 (2021).

    Article 

    Google Scholar 

  • Camp, N. et al. Technology used to recognize activities of daily living in community-dwelling older adults. Int. J. Environ. Res. Public Health 18, 163 (2021).

    Article 

    Google Scholar 

  • Won, J., Kim, C., Choi, S., Youm, S. & Kang, Y. S. TensorFlow Object Detection API-Based Pose Identification Procedure for Elderly Living Alone Emergency Situation Detection. 726–728 (The Korean Institute of Information Scientists and Engineers, 2018).

  • Kim, G. & Park, S. Activity detection from electricity consumption and communication usage data for monitoring lonely deaths. Sensors 21, 3016 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Vermeulen, J., Neyens, J. C., van Rossum, E., Spreeuwenberg, M. D. & de Witte, L. P. Predicting ADL disability in community-dwelling elderly people using physical frailty indicators: A systematic review. BMC Geriatr. 11, 1–11 (2011).

    Article 

    Google Scholar 

  • Gold, D. A. An examination of instrumental activities of daily living assessment in older adults and mild cognitive impairment. J. Clin. Exp. Neuropsychol. 34, 11–34 (2012).

    Article 
    PubMed 

    Google Scholar 

  • Bavazzano, A. et al. Functional evaluation of Alzheimer patients during clinical trials: A review. Arch. Gerontol. Geriatr. 26, 27–32 (1998).

    Article 

    Google Scholar 

  • Yang, Y. et al. Activities of daily living and dementia. Dement. Neurocognit. Disord. 11, 29–37 (2012).

    Article 

    Google Scholar 

  • Jang, J. Comparison of activities of daily living differences with dementia stage. J. Korea Acad. Ind. Cooper. Soc. 18, 557–563 (2017).

    Google Scholar 

  • Morley, J. E. & Vellas, B. COVID-19 and older adult. J. Nutr. Health Aging 24, 364–365 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lim, W. S. et al. COVID-19 and older people in Asia: Asian Working Group for sarcopenia calls to action. Geriatr. Gerontol. Int. 20, 547–558 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kim, J., Kim, Y. & Ha, J. Changes in daily life during the COVID-19 pandemic among south korean older adults with chronic diseases: A qualitative study. Int. J. Environ. Res. Public Health 18, 6781 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Plagg, B., Engl, A., Piccoliori, G. & Eisendle, K. Prolonged social isolation of the elderly during COVID-19: Between benefit and damage. Arch. Gerontol. Geriatr. 89, 104086 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kim, M., Eo, Y. & Kim, S. A study of depression in the elderly by individual and community effects. Health Soc. Welfare Rev. 39, 192–221 (2019).

    Article 

    Google Scholar 

  • Chang, S. & Kim, S. The social network typology among elderly living alone in Busan, depression, and self-neglect. Korean J. Gerontol. Soc. Welfare 72, 245–273 (2017).

    Article 

    Google Scholar 

  • Schlenker, E. Nutrition in Aging. 2nd edn. 186–195 (WCB McGraw-Hill, 1993).

  • Solomons, N. W. Nutrition and aging: Potentials and problems for research in developing countries. Nutr. Rev. 50, 224–229 (1992).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Yao, G., Lei, T. & Zhong, J. A review of convolutional-neural-network-based action recognition. Pattern Recogn. Lett. 118, 14–22 (2019).

    Article 
    ADS 

    Google Scholar 

  • Moon, J., Kim, H. & Park, J. Trends in temporal action detection in untrimmed videos. Electron. Telecommun. Trends 35, 20–33 (2020).

    Google Scholar 

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

  • Feichtenhofer, C., Fan, H., Malik, J. & He, K. SlowFast networks for video recognition. in Proceedings of the IEEE/CVF International Conference on Computer Vision. 6202–6211.

  • Carreira, J. & Zisserman, A. Quo Vadis, action recognition? A new model and the kinetics dataset. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6299–6308.

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

  • Yan, S., Xiong, Y. & Lin, D. Spatial temporal graph convolutional networks ofr skeleton-based action recognition. in Thirty-Second AAAI Conference on Artificial Intelligence (2018).

  • Vemulapalli, R., Arrate, F. & Chellappa, R. Human action recognition by representing 3D skeleton based action recognition. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 588–595.

  • Du, Y., Wang, W. & Wang, L. Hierarchical recurrent neural network for skeleton based action recognition. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1110–1118.

  • Jang, J. et al. ETRI-activity 3D: A large-scale RGB-D dataset for robots to recognize daily activities of the elderly. in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 10990–10997 (IEEE, 2020).

  • Skeleton Based Action Recognition on NTU RGB+D State of the Art” Papers with Code. Assessed 7 Oct 2021 (2021).

  • Sun, Z. et al. Human action recognition from various data modalities: A review. arXiv Preprint arXiv:2012.11866 (2020).

  • Sun, K., Xiao, B., Liu, D. & Wang, J. Deep high-resolution representation learning for human pose estimation. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5693–5703.

  • Dang, Q., Yin, J., Wang, B. & Zheng, W. Deep learning based 2D human pose estimation: A survey. Tsinghua Sci. Technol. 24, 663–676 (2019).

    Article 

    Google Scholar 

  • Ren, S., He, K., Girshick, R. & Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 2015, 28 (2015).

    Google Scholar 

  • JTBC News. “I can’t even say a few words a day”… older adults living alone ‘shade of non-face-to-face’. in Youtube. (2021).

  • Suryadevara, N. K. & Mukhopadhyay, S. C. Wireless sensor network based home monitoring system for wellness determination of elderly. IEEE Sens. J. 12, 1965–1972 (2012).

    Article 
    ADS 

    Google Scholar 

  • Wagner, F., Basran, J. & Bello-Haas, V. D. A review of monitoring technology for use with older adults. J. Geriatr. Phys. Ther. 35, 28–34 (2012).

    Article 
    PubMed 

    Google Scholar 

  • Yang, C.-C. & Hsu, Y.-L. Remote monitoring and assessment of daily activities in the home environment. J. Clin. Gerontol. Geriatr. 3, 97–104 (2012).

    Article 
    CAS 

    Google Scholar 

  • Awais, M., Chiari, L., Ihlen, E. A., Helbostad, J. L. & Palmerini, L. Physical activity classification for elderly people in free-living conditions. IEEE J. Biomed. Health Inform. 23, 197–207 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Matsui, T. et al. Salon: Simplified sensing system for activity of daily living in ordinary home. Sensors 20, 4895 (2020).

    Article 
    ADS 
    PubMed Central 

    Google Scholar 

  • Fernando, Y. P. N. et al. Computer vision based privacy protected fall detection and behavior monitoring system for the care of the elderly. in 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (2021).

  • Suzuki, R. et al. Rhythm of daily living and detection of atypical days for elderly people living alone as determined with a monitoring system. J. Telemed. Telecare 12, 208–214 (2006).

    Article 
    PubMed 

    Google Scholar 

  • Leonardi, C. et al. Knocking on elders’ door. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2009).

  • Hine, C., Nilforooshan, R. & Barnaghi, P. Ethical considerations in design and implementation of home-based Smart Care for dementia. Nurs. Ethics 29, 1035–1046 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 



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