Interaction Design of Fall Detection Camera in Smart Home Care Scenario

被引:0
|
作者
Zhang W. [1 ,2 ]
Jin T. [3 ]
Sun T. [1 ,2 ]
Huang Y. [4 ]
Gao X. [5 ]
Jeung J. [2 ]
机构
[1] Academy of Arts & Design, Tsinghua University, Beijing
[2] The Future Laboratory, Tsinghua University, Beijing
[3] School of Mechanical Engineering and Automation, Beihang University, Beijing
[4] Information Hub, The Hong Kong University of Science and Technology (GZ), Guangzhou
[5] School of Innovation Design, Guangzhou Academy of Fine Arts, Guangzhou
关键词
fall detection; interaction design; older adults; smart home camera;
D O I
10.3724/SP.J.1089.2023.20050
中图分类号
学科分类号
摘要
Indoor falls and related injuries have been major health threats for older adults. To better support seniors living independently at home, the study proposed an interaction design solution for fall detection cameras in smart homecare scenarios. In this paper, we conducted an offline survey of older adults (n=422) in six cities across China and a scenario-based participatory design workshop (n=6), in order to explore their needs and preferences for fall detection camera functionality and interaction. The research concluded that older adults prefer multi-modal interaction design combining voice and GUI through the thematic analysis method. Based on optimization suggestions for the rescue process and functional demands from older participants, we enriched the features of smart home cameras, such as preset basic disease information and data sharing with doctors. Finally, according to the research results, we designed the interaction process and GUI interface of the camera based on the platform of the WeChat mini program, to provide reference for related studies. © 2023 Institute of Computing Technology. All rights reserved.
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页码:238 / 247
页数:9
相关论文
共 32 条
  • [11] Abdul Rahman K, Ahmad S A, Che Soh A, Et al., The association of falls with instability: an analysis of perceptions and expectations toward the use of fall detection devices among older adults in Malaysia, Frontiers in Public Health, 9, (2021)
  • [12] Nooruddin S, Islam M M, Sharna F A, Et al., Sensor-based fall detection systems: a review, Journal of Ambient Intelligence and Humanized Computing, 13, 5, pp. 2735-2751, (2022)
  • [13] Boye N D A, van Lieshout E M M, van Beeck E F, Et al., The impact of falls in the elderly, Trauma, 15, 1, pp. 29-35, (2013)
  • [14] Yu X G., Approaches and principles of fall detection for elderly and patient, Proceedings of the HealthCom 10th International Conference on e-health Networking, Applications and Services, pp. 42-47, (2008)
  • [15] Ogonowski C, Aal K, Vaziri D, Et al., ICT-based fall prevention system for older adults: qualitative results from a long-term field study, ACM Transactions on Computer-Human Interaction, 23, 5, pp. 1-33, (2016)
  • [16] Ejupi A, Gschwind Y J, Valenzuela T, Et al., A Kinect and inertial sensor-based system for the self-assessment of fall risk: A home-based study in older people, Human-Computer Interaction, 31, 3, pp. 261-293, (2016)
  • [17] Wang X Y, Ellul J, Azzopardi G., Elderly fall detection systems: a literature survey, Frontiers in Robotics and AI, 7, (2020)
  • [18] MacKenzie L, Clifford A., Perceptions of older people in Ireland and Australia about the use of technology to address falls prevention, Ageing & Society, 40, 2, pp. 369-388, (2020)
  • [19] de Miguel K, Brunete A, Hernando M, Et al., Home camera-based fall detection system for the elderly, Sensors, 17, 12, (2017)
  • [20] Demiris G, Rantz M J, Aud M A, Et al., Older adults’ attitudes towards and perceptions of “smart home” technologies: a pilot study, Medical Informatics and the Internet in Medicine, 29, 2, pp. 87-94, (2004)