面向未来的智能家居前沿进展

doi:10.3981/j.issn.1000-7857.2023.08.004

Abstract
Figure/Table
References (0)
Related Citation (2)

Download: PDF (1451 KB) HTML^new
Export: BibTeX | EndNote (RIS)

Abstract: With the rapid development of artificial intelligence, the Internet of Things and big data, etc., the smart home enters the Era 3.0, which profoundly relies on user-based big data mining and deep learning. At present, smart interaction, intelligent perception, and complex decision-making in the smart home urgently need new supporting technologies and methods. This paper integrated a large amount of frontier research worldwide and summarized and refined the three elements required for the future smart home: "perception" "thinking" and "execution". This paper began with an overview of the frontier platforms for smart homes in China and abroad, followed by an elaboration and review of the research on data collection, feature extraction, perception in smart home scenarios, and multimodal human-computer interaction and innovation scenarios for smart homes, respectively. The authors presented a series of related research work of Future Lab in the smart home field for researchers in related fields.

Key words： smart home affective computing intelligent perception multimodal interaction

Received: 30 November 2021

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	FU Xinyi
	ZHANG He
	XUE Cheng
	SUN Tongxin

Cite this article:

FU Xinyi,ZHANG He,XUE Cheng, et al. A review of the frontier research on future smart home[J]. Science & Technology Review, 2023, 41(8): 36-52.

URL:

http://www.kjdb.org/EN/10.3981/j.issn.1000-7857.2023.08.004 OR http://www.kjdb.org/EN/Y2023/V41/I8/36

[1] Laspquety-Reyes J. Smart home report 2021[R]. Hamburg: Statista, 2021.
[2] Firouzi R, Rahmani R, Kanter T. An autonomic iot gateway for smart home using fuzzy logic reasoner[J]. Procedia Computer Science, 2020, 177, 102-111.
[3] Mpeis P. The Anyplace 4.0 IoT localization architecture[C]//2020 21st IEEE International Conference on Mobile Data Management (MDM). Versailles, France: IEEE, 2021: 218-225.
[4] Crisostomi E. Analytics for the sharing economy: Mathematics, engineering and business perspectives[M]. Cham: Springer, 2020: 309-333.
[5] Lueth K. IoT Platform companies landscape 2019/2020: 620 IoT platforms globally[EB/OL]. (2019-12-23) [2021-10-18]. https://iot-analytics.com/iot-platform-companies-landscape-2020/.
[6] Toshev A, Szegedy C. DeepPose: Human pose estimation via deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Columbus: IEEE, 2014: 1653-1660.
[7] 邓益侬, 罗健欣, 金凤林 . 基于深度学习的人体姿态估计方法综述[J]. 计算机工程与应用, 2019, 55(19): 22-42.
[8] 付心仪, 蔡天阳, 薛程, 等. 基于 BGRU-FUS-NN神经网络的姿态情感计算方法研究[J]. 计算机辅助设计与图形学学报, 2020, 32(7): 1070-1079.
[9] Dong J, Jiang W, Huang Q, et al. Fast and robust multiperson 3d pose estimation from multiple views[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019: 7792-7801.
[10] Kadkhodamohammadi A, Gangi A, de Mathelin M, et al. A multi-view RGB-D approach for human pose estimation in operating rooms[C]//2017 IEEE winter conference on applications of computer vision (WACV). Santa: IEEE, 2017: 363-372.
[11] Zhou Z, Chen X, Chung Y C, et al. Activity analysis, summarization, and visualization for indoor human activity monitoring[J]. IEEE transactions on circuits and systems for video technology, 2008, 18(11): 1489-1498.
[12] Chen S, Yang R R. Pose Trainer: correcting exercise posture using pose estimation[J]. CoRR, 2020, abs/2006. 11718.
[13] Huang Z, Liu Y, Fang Y, Horn B K. Video-based fall detection for seniors with human pose estimation[C]// 2018 4th International Conference on Universal Village(UV). Boston: IEEE, 2018: 1-4.
[14] Fu X, Xue C, Yin Q, et al. Gesture based fear recognition using nonperformance dataset from VR horror games[C]//2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII). Nara: IEEE, 2021: 1-8.
[15] Sun L, Fu S, Wang F. Decision tree SVM model with Fisher feature selection for speech emotion recognition[J]. EURASIP Journal on Audio, Speech, and Music Processing, 2019, 2019(1): 1-14.
[16] Zhao W, Chellappa R, Phillips P J, et al. Face recognition: A literature survey[J]. ACM computing surveys (CSUR), 2003, 35(4): 399-458.
[17] Wold S, Esbensen K, Geladi P. Principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 1987, 2(1-3): 37 52.
[18] Delac K, Grgic M. Face recognition, I-tech education and publishing[J]. Vienna, Austria, 2007.
[19] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[20] Agarwal S, Mukherjee D P. Facial expression recognition through adaptive learning of local motion descriptor [J]. Multimedia Tools and Applications, 2017, 76(1): 1073-1099.
[21] 陶娟 . 智能家居门锁技术综述[J]. 现代制造技术与装备, 2020(3): 162-164.
[22] Tan L, Zhang K, Wang K, et al. Group emotion recognition with individual facial emotion CNNs and global image based CNNs[C]//Proceedings of the 19th ACM International Conference on Multimodal Interaction. Glasgow: Association for Computing Machinery, 2017: 549-552.
[23] Özseven T. Investigation of the effect of spectrogram im⁃ages and different texture analysis methods on speech emotion recognition[J]. Applied acoustics, 2018, 142: 70-77.
[24] Zhang S, Zhang S, Huang T, et al. Speech emotion recognition using deep convolutional neural network and discriminant temporal pyramid matching[J]. IEEE transactions on multimedia, 2018, 20(6): 1576-1590.
[25] Jin Q, Li C, Chen S, et al. Speech emotion recognition with acoustic and lexical features[C]//Proceedings of
2015 IEEE International Conference on Acoustics, Speech and Signal Processing. South Brisbane: IEEE, 2015: 4749-4753.
[26] Mautz R. Tilch S. Survey of optical indoor positioning systems[C]//2011 international conference on indoor positioning and indoor navigation. Guimaraes: IEEE, 2011: 1-7.
[27] Want R, Hopper A, Falcao V, et al. The active badge location system[J]. ACM Transactions on Information Systems (TOIS), 1992, 10(1): 91-102.
[28] 吴雨航, 吴才聪, 陈秀万, 等 . 几种室内无线定位技术简介[N]. 中国测绘报, 2008-02-01.
[29] 赵永翔 . 基于无线局域网的室内定位系统研究[D]. 武汉: 武汉大学, 2010.
[30] 阮陵, 张翎, 许越, 等 . 室内定位: 分类, 方法与应用综述[J]. 地理信息世界, 2015(2): 8-14.
[31] 裴凌, 刘东辉, 钱久超 . 室内定位技术与应用综述[J].导航定位与授时, 2017, 4(3): 1-10.
[32] Valtonen M, Maentausta J, Vanhala J. Tiletrack: Capacitive human tracking using floor tiles[C]//2009 IEEE international conference on pervasive computing and communications. Galveston: IEEE, 2009: 1-10.
[33] Xiao L, Pan H. Human Activity Recognition System Based on WiFi Signal[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(3): 119-124.
[34] 陆永菊, 吴桂初, 吴自然. 基于灰色神经网络的剩余电流预测研究[J]. 软件导刊, 2020, 19(10): 61-65.
[35] Nie X, Fan T, Wang B, et al. Big data analytics and IoT in operation safety management in under water management[J]. Computer Communications, 2020, 154: 188-196.
[36] Ou Z, Song M, Hwang Z H. Is cloud storage ready? Performance comparison of representative IP-based storage systems[J]. Journal of Systems and Software, 2018, 138: 206-221.
[37] Shirmarz A, Ghaffari A. Performance issues and solutions in SDN-based data center: A survey[J]. The Journal of Supercomputing, 2020, 76(10): 7545-7593.
[38] Mao B, Tang F, Fadlullah Z M, et al. An intelligent route computation approach based on real-time deep learning strategy for software defined communication systems[J]. IEEE Transactions on Emerging Topics in Computing, 2019, 9(3): 1554-1565.
[39] Fu Q, Sun E, Meng K, et al. Deep Q-learning for routing schemes in SDN-based data center networks[J]. IEEE Access, 2020, 8: 103491-103499.
[40] Omer S, Azizi S, Shojafar M, et al. A priority, power and traffic-aware virtual machine placement of IoT applications in cloud data centers[J]. Journal of Systems Architecture, 2021, 115: 101996.
[41] Keller G, Tighe M, Lutfiyya H. An analysis of first fit heuristics for the virtual machine relocation problem[C]// 2012 8th International Conference on Network and Service Management(CNSM) and 2012 Workshop on Systems Virtualiztion Management(SVM). Las Vegas: IEEE, 2012: 406-413.
[42] Wang S H, Huang P P W, Wen C H P, et al. EQVMP: Energy-efficient and QoS-aware virtual machine placement for software defined datacenter networks[C]//The International Conference on Information Networking 2014 (ICOIN2014). Phuket: IEEE, 2014: 220-225.
[43] Son J Buyya R. Priority-aware VM allocation and network bandwidth provisioning in software-defined networking (SDN)-enabled clouds[J]. IEEE Transactions on Sustainable Computing, 2018, 4(1): 17-28.
[44] Rani R, Kumar N, Khurana M, et al. Storage as a service in fog computing: A systematic review[J]. Journal of Systems Architecture, 2021, 116: 102033.
[45] Li T, Sahu A K, Talwalkar A, et al. Federated learning: Challenges, methods, and future directions[J]. IEEE Signal Processing Magazine, 2020, 37(3): 50-60.
[46] Shi W, Cao J, Zhang Q, et al. Edge computing: Vision and challenges[J]. IEEE internet of things journal, 2016, 3(5): 637-646.
[47] Li B, Yu J. Research and application on the smart home based on component technologies and Internet of Things[J]. Procedia Engineering, 2021, 15: 2087-2092.
[48] Chang C, Srirama S N, Buyya R. Indie fog: An efficient fog-computing infrastructure for the internet of things[J]. Computer, 2017, 50(9): 92-98.
[49] Yosuf B A, Musa M, Elgorashi T, et al. Energy efficient distributed processing for IoT[J]. IEEE Access, 2020, 8: 161080-161108
[50] Devarakonda S, Halgamuge M N, Mohammad A. Research anthology on privatizing and securing data[M]. USA: IGI Global, 2021: 1672-1694.
[51] Kouicem D E, Bouabdallah A, Lakhlef H. Internet of things security: A top-down survey[J]. Computer Networks, 2018, 141: 199-221.
[52] Sicari S, Rizzardi A, Miorandi D, et al. Internet of things: Security in the keys[C]//Proceedings of the 12th ACM Symposium on QoS and Security for Wireless and Mobile Networks. Malta: Association for Computing Machinery, 2016: 129-133.
[53] Chien H. Group-oriented range-bound key agreement for internet of things scenarios[J]. IEEE Internet of Things Journal, 2018, 5(3): 1890-1903.
[54] Roman R, Alcaraz C, Lopez J, et al. Key management systems for sensor networks in the context of the Internet of Things[J]. Computers & Electrical Engineering, 2011, 37(2): 147-159.
[55] Raza A, Hardy L, Roehrer E, et al. GPSPiChain-Blockchain and AI based self-contained anomaly detection family security system in smart home[J]. Journal of Systems Science and Systems Engineering, 2021, 30: 433-449.
[56] Vigoya L, Fernandez D, Carneiro V, et al. Annotated dataset for anomaly detection in a data center with IoT sensors[J]. Sensors, 2020, 20(13): 3745.
[57] Yamauchi M, Ohsita Y, Murata M, et al. Anomaly detection in smart home operation from user behaviors and home conditions[J]. IEEE Transactions on Consumer Electronics, 2020, 66(2): 183-192.
[58] Sicari S, Rizzardi A, Miorandi D, et al. Security policy enforcement for networked smart objects[J]. Computer Networks, 2016, 108: 133-147.
[59] Samanta D, Alahmadi A H, Karthikeyan M P, et al. Cipher block chaining support vector machine for secured decentralized cloud enabled intelligent IoT architecture[J]. IEEE Access, 2021, 9: 98013-98025.
[60] Lu R, Heung K, Lashkari A H, Ghorbani A A. A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT[J]. IEEE Access, 2017, 5: 3302-3312.
[61] Guanghui C Xiaoping Z. Multi-modal emotion recognition by fusing correlation features of speech-visual[J]. IEEE Signal Processing Letters, 2021, 28: 533-537.
[62] Singh P, Srivastava R, Rana K P S, et al. A multimodal hierarchical approach to speech emotion recognition from audio and text[J]. Knowledge-Based Systems, 2021, 229: 107316.
[63] Jiang Y, Li W, Hossain M S, et al. A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition[J]. Information Fusion, 2020, 53: 209-221.
[64] Zhang J, Yin Z, Chen P, et al. Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review[J]. Information Fusion, 2020, 59:103-126.
[65] Imani M, Montazer G A. A survey of emotion recognition methods with emphasis on E-learning environments[J]. Journal of Network and Computer Applications, 2019, 147: 102423.
[66] Jia Z, Lin Y, Wang J, et al. HetEmotionNet: Two-stream heterogeneous graph recurrent neural network for multi-modal emotion recognition[C]//Proceedings of the 29th ACM International Conference on Multimedia. Virtual: Association for Computing Machinery, 2021: 1047-1056.
[67] Lv F, Chen X, Huang Y, et al. Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE 2021: 2554-2562.
[68] Kang D, Seo S. Personalized smart home audio system with automatic music selection based on emotion[J]. Multimedia Tools and Applications, 2019, 78(3): 3267-3276.
[69] Soheilian M, Fischl G, Aries M. Smart lighting application for energy saving and user well-being in the residential environment[J]. Sustainability, 2021, 13(11):6198.
[70] Mano L Y, Faiçal B S, Nakamura L H, et al. Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition[J].Computer Communications, 2016, 89-90: 178-190.
[71] Fernandes E, Jung J, Prakash A. Security analysis of emerging smart home applications[C]//2016 IEEE symposium on security and privacy (SP). San Jose: IEEE, 2016: 636-654.
[72] Sikder A K, Babun L, Uluagac A S. Aegis+ a contextaware platform-independent security framework for smart home systems[J]. Digital Threats: Research and Practice, 2021, 2(1): 1-33.
[73] Bhatt D, Hariharasudan A, Lis M, et al. Forecasting of energy demands for smart home applications[J]. Ener⁃gies, 2021, 14(4): 1045.
[74] Salhi L, Silverston T, Yamazaki T, et al. Early detection system for gas leakage and fire in smart home using machine learning[C]//2019 IEEE International Conference on Consumer Electronics (ICCE). Las Vegas: IEEE, 2019: 1-6.
[75] Wang J, Spicher N, Warnecke J M, et al. Unobtrusive health monitoring in private spaces: The smart home[J]. Sensors, 2021, 21(3): 864.
[76] Abreu J, Oliveira R, Garcia-Crespo A, et al. TV interaction as a non-invasive sensor for monitoring elderly well-being at home[J]. Sensors, 2021, 21(20): 6897.
[77] Taufeeque M, Koita S, Spicher N, et al. Multi-camera, multi-person, and real-time fall detection using long short term memory[C]//Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications. California: International Society for Optics and Photonics, 2021: 1160109.
[78] Nadafa R A, Hatturea S M, Bonala V M, et al. Home security against human intrusion using Raspberry Pi[J]. Procedia Computer Science, 2020, 167: 1811-1820.
[79] Malche T, Maheshwary P. Internet of Things (IoT) for building smart home system[C]//2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). Palladam: IEEE, 2017: 65-70.
[80] Demongivert C, Bouchard K, Gaboury S, et al. A distributable event-oriented architecture for activity recognition in smart homes[J]. Journal of Reliable Intelligent Environments, 2021(7): 215-231.
[81] Lesani F S, Fotouhi Ghazvini F, Amirkhani H. Smart home resident identification based on behavioral patterns using ambient sensors[J]. Personal and Ubiquitous Computing, 2021, 25(1): 151-162.
[82] Mukhopadhyay B, Anchal S, Kar S. Person identification using structural vibrations via footfalls for smart home applications[J]. IEEE Internet of Things Journal, 2021, 8(17): 13384-13396.
[83] Hu Z, Zhang Y, Pan S. Vibration-based indoor occupant gait monitoring with robot vacuum cleaners[C]//International Conference on Internet-of-Things Design and Implementation(IoTDI’21). Charlottesvle: Association for Computing Machinery, 2021: 247-248.
[84] Chapron K, Bouchard K, Gaboury S. Real-time gait speed evaluation at home in a multi residents context[J]. Multimedia Tools and Applications, 2021, 80: 12931-12949.
[85] Nguyen M S, Vo T L. Resident identification in smart home by voice biometrics[C]//International Conference on Future Data and Security Engineering. Ho Chi Minh City: Springer, 2018: 433-448.
[86] Fang S, Islam T, Munir S, et al. EyeFi: Fast human identification through vision and WiFi-based trajectory matching[C]//2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS). Marina del Rey: IEEE, 2020: 59-68.
[87] Mo H, Kim S. A deep learning-based human identification system with wi-fi csi data augmentation[J]. IEEE Access, 2021, 9: 91913-91920.
[88] Mokhtari G, Zhang Q, Nourbakhsh G, et al. BLUESOUND: A new resident identification sensor—Using ultrasound array and BLE technology for smart home platform[J]. IEEE Sensors Journal, 2017, 17(5): 1503-1512.
[89] Kim K, Li S, Heydariaan M, et al. Feasibility of LoRa for smart home indoor localization[J]. Applied Sciences, 2021, 11(1): 415.
[90] Guinea A S, Boytsov A, Mouline L, et al. Continuous identification in smart environments using wrist-worn inertial sensors[C]//Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. New York: Association for Computing Machinery, 2018: 87-96.
[91] Nweke H F, Teh Y W, Al-Garadi M A, et al. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges[J]. Expert Systems with Applications, 2018, 105: 233-261.
[92] Hassan M M, Uddin M Z, Mohamed A, et al. A robust human activity recognition system using smartphone sensors and deep learning[J]. Future Generation Computer Systems, 2018, 81: 307-313.
[93] Kim K, Jalal A, Mahmood M. Vision-based human activity recognition system using depth silhouettes: A smart home system for monitoring the residents[J]. Journal of Electrical Engineering & Technology, 2019, 14(6): 2567-2573.
[94] Dang L M, Min K, Wang H, et al. Sensor-based and vision-based human activity recognition: A comprehensive survey[J]. Pattern Recognition, 2020, 108: 107561.
[95] Ranieri C M, MacLeod S, Dragone M, et al. Activity recognition for ambient assisted living with videos, inertial units and ambient sensors[J]. Sensors, 2021, 21(3): 768.
[96] Wang D, Yang J, Cui W, et al. Multimodal CSI-based human activity recognition using GANs[J]. IEEE Internet of Things Journal(Early Access). 2021, 8(24): 17345-17355.
[97] Yadav S K, Tiwari K, Pandey H M, et al. A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions[J]. Knowledge-Based Systems, 2021, 223: 106970.
[98] Zhang M, Sawchuk A A. USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors[C]//Proceedings of the 2012 ACM conference on ubiquitous computing. Pittsburgh Pennsylvania: Association for Computing Machinery, 2012: 1036-1043.
[99] Yordanova K, Whitehouse S, Paiement A, et al. What's cooking and why? Behaviour recognition during unscripted cooking tasks for health monitoring[C]//2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). Kona: IEEE, 2017: 18-21.
[100] Mihailidis A, Boger J N, Craig T, et al. The COACH prompting system to assist older adults with dementia through handwashing: An efficacy study[J]. BMC geriatrics, 2008, 8(1): 1-18.
[101] Kim E, Helal S, Cook D. Human activity recognition and pattern discovery[J]. IEEE pervasive computing, 2009, 9(1): 48-53.
[102] Dang C T, Seiderer A, André E. Theodor: A step towards smart home applications with electronic noses[C]//Proceedings of the 5th international Workshop on Sensor-based Activity Recognition and Interaction. Berlin: Association for Computing Machinery, 2018: 1-7.
[103] 路奇, 吴昊, 梁婉, 等. 基于气味采集的嗅觉输入界面探索[J]. 计算机辅助设计与图形学学报, 2020, 32(7): 1018-1025.
[104] Constantinou A C, Fenton N. Things to know about Bayesian networks: Decisions under uncertainty, part 2[J]. Significance, 2018, 15(2): 19-23.
[105] Yao L, Damiran Z, Lim W H. Energy management optimization scheme for smart home considering different types of appliances[C]//2017 IEEE international conference on environment and electrical engineering and 2017 IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe). Milan: IEEE, 2017: 1-6.
[106] Bergeron F, Bouchard K, Gaboury S, et al. Tracking objects within a smart home[J]. Expert Systems With Applications, 2018, 113: 428-442.
[107] Aheleroff S, Xu X, Lu Y, et al. IoT-enabled smart appliances under industry 4.0: A case study[J]. Advanced engineering informatics, 2020, 43: 101043.
[108] Wieland G, Zeiner H. A survey on criteria for smart home systems with integration into the analytic hierarchy process[C]//International Conference on Decision Support System Technology. Loughborough: Springer, 2021: 55-66.
[109] Dos Santos P H, Neves S M, Sant'Anna D O, et al. The analytic hierarchy process supporting decision making for sustainable development: An overview of applications[J]. Journal of cleaner production, 2019, 212: 119-138.
[110] IFTTT[EB/OL]. [2021-10-27]. https://ifttt.com.
[111] OpenJS Foundation. Node-RED[EB/OL]. [2021-10-27]. https://nodered.org.
[112] Li T J J, Labutov I, Myers B A, et al. Studies in conversational UX design[M]. Cham: Springer, 2018: 119-137.
[113] Catania V, Delfa G C L, Monteleone S, et al. GOOSE: Goal oriented orchestration for smart environments[J]. International Journal of Ad Hoc and Ubiquitous Computing, 2019, 32(3): 159-170.
[114] Noura M, Heil S, Gaedke M. Natural language goal understanding for smart home environments[C]//Proceedings of the 10th International Conference on the Internet of Things. Malmö Sweden: Association for Computing Machinery, 2020: 1-8.
[115] Jordan P W. Human factors for pleasure in product use[J]. Applied ergonomics, 1998, 29(1): 25-33.
[116] Hassenzahl M. Experiences before things: A primer for the (yet) unconvinced[C]//CHI'13 Extended Abstracts on Human Factors in Computing Systems. Paris: Association for Computing Machinery, 2013: 2059-2068.
[117] Holtzblatt K, What makes things cool? Intentional design for innovation[J]. Interactions, 2011, 18(6): 40-47.
[118] Raptis D, Bruun A, Kjeldskov J, et al. Converging coolness and investigating its relation to user experience[J]. Behaviour & Information Technology, 2017, 36(4): 333-350.
[119] Jensen R H, Strengers Y, Kjeldskov J, et al. Designing the desirable smart home: A study of household experiences and energy consumption impacts[C]//Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. New York: ACM Digital Library, 2018: 1-14.
[120] Dang C T, Aslan I, Lingenfelser F, et al. Towards somaesthetic smarthome designs: Exploring potentials and limitations of an affective mirror[C]//Proceedings of the 9th International Conference on the Internet of Things. Bilbao: Association for Computing Machinery, 2019: 1-8.
[121] Beneteau E, Boone A, Wu Y, et al. Parenting with Alexa: exploring the introduction of smart speakers on family dynamics[C]//Proceedings of the 2020 CHI conference on human factors in computing systems. Honolulu: Association for Computing Machinery, 2020: 1-13.
[122] Bowles N. Thermostats, locks and lights: Digital tools of domestic abuse[N]. The New York Times, 2018: 23.
[123] He W, Golla M, Padhi R. Rethinking access control and authentication for the home internet of things (IoT) [C]//27th {USENIX} Security Symposium ({USENIX} Security 18). Baltimore: {USENIX} Association, 2018: 255-272.
[124] Kanchi S, Karlapalem K. A multi perspective access control in a smart home[C]//Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy. Virtual: Association for Computing Machinery, 2021: 321-323.
[125] Park S, Lim Y K. Investigating user expectations on the roles of family-shared AI speakers[C]//Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu: Association for Computing Machinery, 2020: 1-13.
[126] Geeng C, Roesner F. Who's in control? Interactions in multi-user smart homes[C]//Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Glasgow Scotland: Association for Computing Machinery, 2019: 1-13.
[127] Bempong J, Stainslow J, Behm G. Accessible smart home system for the deaf and hard-of-hearing[EB/OL]. [2021-10-28]. https://www.rit.edu/ntid/nyseta/sites/rit.edu.ntid.nyseta/files/docs/fullpapers_PDFs/StanislowJoeFullPaper.
[128] Satish R P, Chidrawar S K. Real time implementation of accessible display design to control home area network[Z]. 2015.
[129] Almeida N, Silva S, Teixeira A. Multimodal interaction for accessible smart homes[C]//Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion. Thessaloniki: Association for Computing Machinery, 2018: 63-70.
[130] Dahl D A. Multimodal interaction with W3C standards [M]. Cham: Springer International Publishing, 2016.
[131] Almeida N, Teixeira A, Silva S, et al. The am4i architecture and framework for multimodal interaction and its application to smart environments[J]. Sensors, 2019 19(11): 2587.
[132] Spinsante S, Cippitelli E, De Santis A, et al. Multimodal interaction in a elderly-friendly smart home: A case study[C]//International Conference on Mobile Networks and Management. Würzburg: Springer, 2014: 373-386.
[133] Rus S, Helfmann S, Kirchbuchner F, et al. Designing smart home controls for elderly[C]//Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments. Corfu: Association for Computing Machinery, 2020: 1-10.
[134] Demirkan H, Olguntürk N. A priority-based "design for all" approach to guide home designers for independent living[J]. Architectural Science Review, 2014, 57(2): 90-104.
[135] de Oliveira G A A, de Bettio R W, Freire A P. Accessibility of the smart home for users with visual disabilities: an evaluation of open source mobile applications for home automation[C]//Proceedings of the 15th Brazilian symposium on human factors in computing systems. São Paulo: Association for Computing Machinery, 2016: 1-10.
[136] Kon B, Lam A, Chan J. Evolution of smart homes for the elderly[C]//Proceedings of the 26th International Conference on World Wide Web Companion. Perth: International World Wide Web Conferences Steering Committee, 2017: 1095-1101.
[137] Ahire S, Rohs M. Tired of wake words? Moving towards seamless conversations with intelligent personal assistants[C]//Proceedings of the 2nd Conference on Conversational User Interfaces. Bilbao, Spain: Association for Computing Machinery, 2020: 1-3.
[138] Pradhan A, Lazar A, Findlater L. Use of intelligent voice assistants by older adults with low technology use [J]. ACM Transactions on Computer-Human Interaction (TOCHI), 2020, 27(4): 1-27.