Abstract
Based on guidelines extracted from research and experiences with older adults, four individual applications incorporating Augmented Reality (AR) to improve Activities of Daily Living are presented. For the first use case, a combination of AR and Machine Learning is proposed to create a pill detector to support taking medication on their own. Thereby the neural network classifies the pills, while AR is used to precisely mark in 3D the correct pill to take. When difficulties in the accuracy of the detection arise, a video call streaming the camera feed can be initiated with a caregiver, who then can remotely mark the correct pill. Additionally, the app supports the user when shopping by providing advice when dealing with personal nutritional restrictions with an AR overlay on products. Secondly, a location-based AR game GramoGO is proposed which combines collecting music records with fitness by exploring the neighborhood. Cognitive health is trained by subsequently assembling the music pieces by listening to them on an AR music player. To support frail older adults when walking stairs at home, the app stAiRs for a glasses-like AR HMD has been developed. It utilizes fiducial markers placed on the shoes and pressure sensors inside of them to indicate the correct angle to walk up and down stairs. Additionally, the stairs have been modified with sensors that detect on which stair the person is walking on and LEDs are being lit to safeguard against overstepping. Lastly, an AR companion supporting navigation at unfamiliar places avoiding the need to match a virtual map with the real world has been developed. Through map APIs, it is possible to extract dangerous routes and the companion can also function as a personal trainer for healthy activities. Small-scale pilot studies have been performed for some of the features of the individual applications. One core finding is, that integrating AR into the daily routine of older adults will need daily active training, incentivized through a Serious Game concept. For that reason, we propose a framework for the use of AR with the target group in mind.
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References
Avouris, N.M., Yiannoutsou, N.: A review of mobile location-based games for learning across physical and virtual spaces. J. Univers. Comput. Sci. 18(15), 2120–2142 (2012)
Caban, J.J., Rosebrock, A., Yoo, T.S.: Automatic identification of prescription drugs using shape distribution models. In: 2012 19th IEEE International Conference on Image Processing, pp. 1005–1008. IEEE (2012)
Cunha, A., Adão, T., Trigueiros, P.: HelpmePills: a mobile pill recognition tool for elderly persons. Procedia Technol. 16, 1523–1532 (2014)
Dodd, C., Athauda, R., Adam, M.: Designing user interfaces for the elderly: a systematic literature review (2017)
Eichhorn, C., et al.: Combining motivating strategies with design concepts for mobile apps to increase usability for the elderly and Alzheimer patients. In: Gao, Q., Zhou, J. (eds.) HCII 2020. LNCS, vol. 12208, pp. 47–66. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50249-2_4
Eichhorn, C., Plecher, D.A., Trilk, A., Hiyama, A., Klinker, G.: GuessingCarbs-a serious game about healthy nutrition in old age combining virtual and tangible components. In: Stephanidis, C., Antona, M., Ntoa, S. (eds.) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol. 1583, pp. 407–415. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06394-7_51
Eichhorn, C., et al.: Innovative game concepts for Alzheimer patients. In: Zhou, J., Salvendy, G. (eds.) ITAP 2018. LNCS, vol. 10927, pp. 526–545. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92037-5_37
Eichhorn, C., et al.: THe innovative reminder in senior-focused technology (THIRST)—evaluation of serious games and gadgets for Alzheimer patients. In: Zhou, J., Salvendy, G. (eds.) HCII 2019. LNCS, vol. 11593, pp. 135–154. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22015-0_11
Eichhorn, C., Plecher, D.A., Golovnya, O., Volkert, D., Hiyama, A., Klinker, G.: Tangible chess for dementia patients – playing with conductive 3D printed figures on a touchscreen. In: Gao, Q., Zhou, J. (eds.) HCII 2021. LNCS, vol. 12787, pp. 38–57. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78111-8_3
Facer, K., Joiner, R., Stanton, D., Reid, J., Hull, R., Kirk, D.: Savannah: mobile gaming and learning? J. Comput. Assist. Learn. 20(6), 399–409 (2004)
Hanser, S.B., Thompson, L.W.: Effects of a music therapy strategy on depressed older adults. J. Gerontol. 49(6), P265–P269 (1994)
Havukainen, M., Laine, T.H., Martikainen, T., Sutinen, E.: A case study on co-designing digital games with older adults and children: game elements, assets, and challenges. Comput. Games J. 9, 163–188 (2020)
Hervás, R., Bravo, J., Fontecha, J.: An assistive navigation system based on augmented reality and context awareness for people with mild cognitive impairments. IEEE J. Biomed. Health Inform. 18(1), 368–374 (2013)
Jaul, E., Barron, J.: Age-related diseases and clinical and public health implications for the 85 years old and over population. Front. Public Health 5, 335 (2017)
Kim, S., Dey, A.K.: Simulated augmented reality windshield display as a cognitive mapping aid for elder driver navigation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 133–142 (2009)
Kruse, L., Karaosmanoglu, S., Rings, S., Ellinger, B., Steinicke, F.: Enabling immersive exercise activities for older adults: a comparison of virtual reality exergames and traditional video exercises. Societies 11(4), 134 (2021)
Larios Delgado, N., et al.: Fast and accurate medication identification. NPJ Digit. Med. 2(1), 10 (2019)
Lee, L.N., Kim, M.J., Hwang, W.J.: Potential of augmented reality and virtual reality technologies to promote wellbeing in older adults. Appl. Sci. 9(17), 3556 (2019)
Lee, S., Oh, H., Shi, C.K., Doh, Y.Y.: Mobile game design guide to improve gaming experience for the middle-aged and older adult population: user-centered design approach. JMIR Serious Games 9(2), e24449 (2021)
Lee, Y.B., Park, U., Jain, A.K., Lee, S.W.: Pill-id: matching and retrieval of drug pill images. Pattern Recogn. Lett. 33(7), 904–910 (2012)
Marchesi, G., Eichhorn, C., Plecher, D.A., Itoh, Y., Klinker, G.: EnvSLAM: combining slam systems and neural networks to improve the environment fusion in AR applications. ISPRS Int. J. Geo Inf. 10(11), 772 (2021)
Marsh, T.: Serious games continuum: between games for purpose and experiential environments for purpose. Entertain. Comput. 2(2), 61–68 (2011)
Muratet, M., Torguet, P., Viallet, F., Jessel, J.P.: Experimental feedback on prog &play: a serious game for programming practice. Comput. Graph. Forum 30, 61–73 (2011). Wiley Online Library (2011)
Nouchi, R., et al.: Brain training game improves executive functions and processing speed in the elderly: a randomized controlled trial. PLoS ONE 7(1), e29676 (2012)
Platoni, K.: Virtual reality aimed at the elderly finds new fans. Health News from NPR, Shots (2016)
Plecher, D., Eichhorn, C., Klinker, G.: Roar-role of augmented reality in serious games and superhuman sports (2022)
Plecher, D.A., et al.: Interactive drinking gadget for the elderly and Alzheimer patients. In: Zhou, J., Salvendy, G. (eds.) HCII 2019. LNCS, vol. 11593, pp. 444–463. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22015-0_35
Rot, A., Kutera, R., Gryncewicz, W.: Design and assessment of user interface optimized for elderly people. a case study of Actgo-Gate platform. In: ICT4AgeingWell, pp. 157–163 (2017)
Takahashi, M., Kawasaki, H., Maeda, A., Nakamura, M.: Mobile walking game and group-walking program to enhance going out for older adults. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pp. 1372–1380 (2016)
Ting, H.W., Chung, S.L., Chen, C.F., Chiu, H.Y., Hsieh, Y.W.: A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan. BMC Health Serv. Res. 20(1), 1–9 (2020)
Yaniv, Z., et al.: The national library of medicine pill image recognition challenge: an initial report. In: 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–9. IEEE (2016)
Zeng, X., Cao, K., Zhang, M.: Mobiledeeppill: A small-footprint mobile deep learning system for recognizing unconstrained pill images. In: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, pp. 56–67 (2017)
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Eichhorn, C. et al. (2023). A Framework to Incentivize the Use of Augmented Reality in Daily Lives of Older Adults. In: Gao, Q., Zhou, J. (eds) Human Aspects of IT for the Aged Population. HCII 2023. Lecture Notes in Computer Science, vol 14042. Springer, Cham. https://doi.org/10.1007/978-3-031-34866-2_38
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