1. Economic and Financial Affairs (2017) The 2018 ageing report: underlying assumptions and projection methodologies. Technical report, European Commission

2. Colby S, Ortman JM (2015) Projections of the size and composition of the U.S. population 2014 to 2060. Department of Commerce, Economics and Statistics Administration, technical report, U.S., p 2015

3. Cardinaux F, Bhowmik D, Abhayaratne C, Hawley MS (2011) Video based technology for ambient assisted living: a review of the literature. J Ambient Intell Smart Environ 3:253–269

   Article  Google Scholar 

4. Nguyen T-H-C, Nebel J-C, Florez-Revuelta F (2016) Recognition of activities of daily living with egocentric vision: a review. Sensors 16(1):72

   Article  Google Scholar 

5. Hodges S, Berry E, Wood K (2011) Sensecam: a wearable camera that stimulates and rehabilitates autobiographical memory. Memory 19(7):685–696

   Article  Google Scholar 

6. Rodriguez M, Orrite C, Medrano C, Makris D (2016) A time flexible kernel framework for video-based activity recognition. Image Vis Comput 48:26–36

   Article  Google Scholar 

7. Queiros A, Dias A, Silva AG, Rocha NP (2017) Ambient assisted living and health-related outcomes: a systematic literature review. Informatics 4(3):19

   Article  Google Scholar 

8. Blackman S, Matlo C, Bobrovitskiy C, Waldoch A, Fang ML, Jackson P, Mihailidis A, Nygård L, Astell A, Sixsmith A (2016) Ambient assisted living technologies for aging well: a scoping review. J Intell Syst 25(1):55–69

   Article  Google Scholar 

9. Reissner L, Gabriella F, List R, Giovanoli P, Calcagni M (2019) Assessment of hand function during activities of daily living using motion tracking cameras: a systematic review. Proc Inst Mech Eng H J Eng Med 233:095441191985130

   Article  Google Scholar 

10. Martin SK, Jude HD (2017) Hand posture and gesture recognition techniques for virtual reality applications: a survey. Virtual Real 21(2):91–107

    Article  Google Scholar 

11. Rodríguez M, Orrite C, Medrano C, Makris D (2017) Fast simplex-hmm for one-shot learning activity recognition. In: 2017 IEEE conference on computer vision and pattern recognition workshops, CVPR workshops 2017, Honolulu, HI, USA, July 21–26, 2017. IEEE Computer Society, pp 1259–1266

12. Martin SK, Jude HD (2019) A probabilistic model for state sequence analysis in hidden Markov model for hand gesture recognition. Comput Intell 35(1):59–81

    Article MathSciNet  Google Scholar 

13. Pirsiavash H, Ramanan D (2012) Detecting activities of daily living in first-person camera views. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE

14. McCandless T, Grauman K (2013) Object-centric spatio-temporal pyramids for egocentric activity recognition. In: British machine vision conference, BMVC 2013, Bristol, UK, September 9–13, 2013

15. Matsuo K, Yamada K, Ueno S, Naito S (2014) An attention-based activity recognition for egocentric video. In: IEEE conference on computer vision and pattern recognition, CVPR workshops 2014, Columbus, OH, USA, June 23–28, 2014, pp 565–570

16. Wang H, Schmid C (2013) Action recognition with improved trajectories. In: 2013 IEEE international conference on computer vision, IEEE, Sydney, Australia, Dec 1–8 2013.https://doi.org/10.1109/ICCV.2013.441

17. Fathi A, Farhadi A, Rehg JM (2011) Understanding egocentric activities. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 407–414

18. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. IEEE Conf Comput Vis Pattern Recognit (CVPR) 2:2169–2178

    Google Scholar 

19. Zhou Y, Ni B, Hong R, Yang X, Tian Q (2016) Cascaded interactional targeting network for egocentric video analysis. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, USA, June 27–30, 2016. IEEE Computer Society, pp 1904–1913

20. Wang M, Luo C, Ni B, Yuan J, Wang J, Yan S (2018) First-person daily activity recognition with manipulated object proposals and non-linear feature fusion. IEEE Trans Circuits Syst Video Technol 28(10):2946–2955

    Article  Google Scholar 

21. Zou Z, Shi Z, Guo Y, Ye J (2019) Object detection in 20 years: a survey.CoRR.arXiv:1905.05055

22. Liu L, Ouyang W, Wang X, Fieguth PW, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128(2):261–318

    Article  Google Scholar 

23. Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Rong Q (2019) A survey of deep learning-based object detection. IEEE Access 7:128837–128868

    Article  Google Scholar 

24. Rodriguez M, Orrite C, Medrano C (2017) Space-time flexible kernel for recognizing activities from wearable cameras. In: Alexandre LA, Sánchez JS, Rodrigues JMF (eds) Pattern recognition and image analysis. Springer, Cham, pp 511–518

    Chapter  Google Scholar 

25. Jebara T, Kondor R, Howard A (2004) Probability product kernels. J Mach Learn Res 5:819–844

    MathSciNet MATH  Google Scholar 

26. Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, New York

    MATH  Google Scholar 

27. Choi J, Wang Z, Lee S-C, Jeon WJ (2013) A spatio-temporal pyramid matching for video retrieval. Comput Vis Image Underst 117(6):660–669

    Article  Google Scholar 

28. Zhang S, Nugent C, Lundström J, Sheng M (2018) Ambient assisted living for improvement of health and quality of life. Informatics 5(1):4

    Article  Google Scholar 

29. Damen D, Doughty H, Farinella GM, Fidler S, Furnari A, Kazakos E, Moltisanti D, Munro J, Perrett T, Price W, Wray M (2020) The EPIC-KITCHENS dataset: collection, challenges and baselines.CoRR.arXiv:2005.00343

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