基于情绪驱动的适老性智慧家居方案研究Research on Emotion-Driven Aging Smart Home Scheme
穆明鑫,梁家振,姜大志
摘要(Abstract):
根据第七次全国人口普查公报显示,我国已经迈入人口老龄化社会,而且老龄化规模大,程度深,速度快.为了积极应对人口老龄化,优化城乡养老服务供给,推动老龄事业和产业高质量发展,设计一套科学,规范,操作性简便的适老性智能家居的方案是一个值得探索研究的课题.然而,现有的适老性智慧家居体系主要注重老龄化人群的生理和行为特点,忽视了老龄化人群的内心孤独、寂寞等消极情绪.我们以老龄化人群的居家养老问题为切入点,以多模态信息采集与情绪识别为基础,引导老年人心理积极向上为目标,结合5G、大数据、信息安全等互联网前沿技术对适老性智能家居进行再设计,构建智能感知、沉浸式、安全的智慧家庭应用,为新一代互联网智慧生活提供一个极具借鉴意义的范式.
关键词(KeyWords): 适老性智慧家居体系;情绪识别;老龄化;智慧家庭养老
基金项目(Foundation): 国家自然科学基金(62106136,62206163);; 广东省科技创新战略专项(STKJ2021005,STKJ202209002)
作者(Author): 穆明鑫,梁家振,姜大志
参考文献(References):
- [1]任娜.我国老年人居家养老分类分层研究[J].中国物价,2022(2):108-112.
- [2]韩璐.我国老龄化的空间分布及成因分析[D].大连:东北财经大学,2019.
- [3]周燕珉,王富青.“居家养老为主”模式下的老年住宅设计[J].现代城市研究,2011,26(10):68-74.
- [4]庞广风.基于用户体验的家庭助老服务机器人交互设计研究[D].扬州:扬州大学,2022.
- [5]逄亚彬. 5G时代适老性家庭健康产品设计研究[D].南京:南京艺术学院,2021.
- [6]嵇柔.智慧家庭背景下初老人群自然交互设计研究[D].无锡:江南大学,2019.
- [7]智能养老蓝皮书:中国智能养老产业发展报告(2018)[J].劳动保障世界,2019(2):8.
- [8] CHRISTINA A,GOLAM S,CAROLYN S,et al. Smart home technology to support older people's quality of life:A longitudinal pilot study[J]. International Journal of Older People Nursing,2023,18(1):e12489.
- [9]石元伍,陈旺.老年人助行机器人创新设计研究[J].包装工程,2017,38(16):97-101.
- [10]梁非凡,刘树老.老年人智能家居用具———智能床头灯设计研究[J].家具与室内装饰,2020(4):66-67.
- [11]郭弈妤,张凌浩.面向老年患者的疼痛评估工具界面信息可视化设计研究[J].设计,2019,32(19):28-30.
- [12] JUSTIN B,BIRTE R,THORSTEN J. A framework for learning event sequences and explaining detected anomalies in a smart home environment[J]. Kunstliche Intelligenz,2022,36(3):259-266.
- [13] BOGYEONG L,PRAKHAR M,THEODORA C,et al. Assessing daily activity routines using an unsupervised approach in a smart home environment[J]. Journal of Computing in Civil Engineering,2023,37(1):4895.
- [14] SOOJUNG C, KYEONGSOOK N. Exploring the sustainable values of smart homes to strengthen adoption[J]. Buildings,2022,12(11):1919.
- [15] RAMALINGAM S P,SHANMUGAM P K. Investigation on optimization algorithms for smart home energy management with different electricity pricing[J]. International Journal of Electrical and Electronic Engineering&Telecommunications,2022,11(6):435-449.
- [16] SOUMYAJIT G,ARUNAVA C,DEBASHIS C. Extraction of statistical features for type-2 fuzzy NILM with IoT enabled control in a smart home[J]. Expert Systems With Applications,2023,212:118750.
- [17]张璐.智能家居系统人机关系研究[D].无锡:江南大学,2008.
- [18]曾琼,朱文澜.重构想象:5G时代的智能广告图景[J].现代传播(中国传媒大学学报),2021,43(1):129-133.
- [19] GUO Y. Low-latency and high-concurrency 5g wireless sensor network assists innovation in ideological and political education in colleges and universities[J]. Journal of Sensors,2021:1-11.
- [20]汪思远.一种智能控制的家居照明系统[J].物联网技术,2021,11(9):105-109+113.
- [21]姚婧媛.基于“5G+AI”智能家居在室内设计中的应用[J].产业创新研究,2022(18):67-69.
- [22] WANG H,GUO F,DU M,et al. A novel method for drug-target interaction prediction based on graph transformers model[J]. BMC Bioinformatics,2022,23(1):459.
- [23] WEI Y,WANG X,NIE L,et al. MMGCN:multi-modal graph convolution network for personalized recommendation of micro-video[J]. Association for Computational Linguistics,2019:5666-5675.
- [24] KIMH S,LEEM H. A study of efficiency information filtering system using one-hot long short-term memory[J]. International Journal of Advanced Culture Technology,2017,5(1):83-89.
- [25] ZHONG P,WANG D,MIAO C. Knowledge-enriched transformer for emotion detection in textual conversations[C]//Conference on Empirical Methods in Natural Language Processing, 2019, 19(1):165-176.
- [26] KANAKAD B,RAJESH V. A ResNet deep learning based facial recognition design for future multimedia applications[J]. Computers and Electrical Engineering,2022,104:108384.
- [27] SONG B, WANG R, HE W, et al. Robustness learning via inference-softmax cross entropy in misaligned distribution of image[J]. Mathematics,2022,10(19):3716.