[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.
