Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Cognitive Computation

07-08-2024 | Correspondence

Internet of Things for Emotion Care: Advances, Applications, and Challenges

Authors: Xu Xu, Chong Fu, David Camacho, Jong Hyuk Park, Junxin Chen

Published in: Cognitive Computation

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Excerpt

Emotion is a cognitive state that occurs in humans with external stimulation [1]. In recent years, psychological-related illnesses and mental health challenges have arisen due to various factors, such as social pressure and interpersonal communication. These issues may threaten social stability and human development. Many studies have determined that emotion care is one of the effective ways to promote psychological well-being and mental health [2]. Traditional approach to emotion care that involves discussing emotions and problems with a mental health professional [1]. It encompasses various techniques, like cognitive behavioral therapy (CBT), which helps individuals identify and change negative thinking patterns and behaviors. However, these methods have relied on subjective self-reporting or limited clinical observations resulting in less reliable for emotional care [3]. In addition, traditional methods might not capture moment-to-moment changes in emotional states, potentially missing critical insights into mental health. Therefore, this highlights the urgency for a more comprehensive, accessible, and objective emotion care approach. …

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Beck AT. Cognitive therapy and the emotional disorders. Penguin; 1979.
2.
Plageras AP, Psannis KE. IoT-based health and emotion care system. ICT Express. 2023;9(1):112–5.CrossRef
3.
Tawsif K, Aziz NAA, Raja JE, Hossen J, Jesmeen M. A systematic review on emotion recognition system using physiological signals: data acquisition and methodology. Emerg Sci J. 2022;6(5):1167–98.CrossRef
4.
Chen J, Sun S, Zhang L-B, Yang B, Wang W. Compressed sensing framework for heart sound acquisition in internet of medical things. IEEE Trans Industr Inf. 2022;18(3):2000–9.CrossRef
5.
Wang W, Kumar N, Chen J, Gong Z, Kong X, Wei W, Gao H. Realizing the potential of the Internet of Things for smart tourism with 5G and AI. IEEE Netw. 2020;34(6):295–301.CrossRef
6.
Baker S, Xiang W. Artificial Intelligence of Things for smarter healthcare: a survey of advancements, challenges, and opportunities. IEEE Commun Surv Tutor.
7.
Khudhair SA, Singh MJ. Review in FBMC to enhance the performance of 5G networks. J Commun. 2020;15(5):415–26.CrossRef
8.
Chen J, Guo Z, Xu X, Zhang L-B, Teng Y, Chen Y, Woźniak M, Wang W. A robust deep learning framework based on spectrograms for heart sound classification. IEEE/ACM Trans Comput Biol Bioinform.
9.
Aruna S, Sindhanaiselvan K, Madhusudhanan B. Computerized grading of brain tumors supplemented by artificial intelligence. Soft Comput. 2020;24(10):7827–33.CrossRef
10.
Kaushik H, Kumar T, Bhalla K. iSecurehome: a deep fusion framework for surveillance of smart homes using real-time emotion recognition. Appl Soft Comput. 2022;122:108788.CrossRef
11.
Rezaee K, Yang X, Khosravi MR, Zhang R, Lin W, Jeon G. Fusion-based learning for stress recognition in smart home: an IOMT framework. Build Environ. 2022;216:108988.CrossRef
12.
Chakraborty A, Banerjee JS, Bhadra R, Dutta A, Ganguly S, Das D, Kundu S, Mahmud M, Saha G. A framework of intelligent mental health monitoring in smart cities and societies. IETE J Res. 2023:1–14.
13.
Dzedzickis A, Kaklauskas A, Bucinskas V. Human emotion recognition: Review of sensors and methods. Sensors. 2020;20(3):592.
14.
Saganowski S, Perz B, Polak A, Kazienko P. Emotion recognition for everyday life using physiological signals from wearables: a systematic literature review. IEEE Trans Affect Comput.
15.
Cîrneanu A-L, Popescu D, Iordache D. New trends in emotion recognition using image analysis by neural networks, a systematic review. Sensors. 2023;23(16):7092.CrossRef
16.
Hasnul MA, Aziz NAA, Alelyani S, Mohana M, Aziz AA. Electrocardiogram-based emotion recognition systems and their applications in healthcare—a review. Sensors. 2021;21(15):5015.CrossRef
17.
Bharadwaj HK, Agarwal A, Chamola V, Lakkaniga NR, Hassija V, Guizani M, Sikdar B. A review on the role of machine learning in enabling IoT based healthcare applications. IEEE Access. 2021;9:38859–90.CrossRef
18.
Ahad A, Tahir M, Aman Sheikh M, Ahmed KI, Mughees A, Numani A. Technologies trend towards 5G network for smart health-care using IoT: a review. Sensors. 2020;20(14):4047.CrossRef
19.
Soufineyestani M, Dowling D, Khan A. Electroencephalography (EEG) technology applications and available devices. Appl Sci. 2020;10(21):7453.CrossRef
20.
Mutlag AA, Ghani MKA, Mohammed MA. A healthcare resource management optimization framework for ECG biomedical sensors. In: Efficient Data Handling for Massive Internet of Medical Things: Healthcare Data Analytics. Springer; 2021. pp. 229–44.
21.
Dobbs WC, Fedewa MV, MacDonald HV, Holmes CJ, Cicone ZS, Plews DJ, Esco MR. The accuracy of acquiring heart rate variability from portable devices: a systematic review and meta-analysis. Sports Med. 2019;49:417–35.CrossRef
22.
Saad W, Rahman N, Karis M, Chia S, Karim S, Talib M, Msja R. Analysis on continuous wearable device for blood glucose detection using GSR sensor. Int J Nanoelectr Mater. 13(8).
23.
Fuentes del Toro S, Wei Y, Olmeda E, Ren L, Guowu W, Díaz V. Validation of a low-cost electromyography (EMG) system via a commercial and accurate EMG device: pilot study. Sensors. 2019;19(23):5214.CrossRef
24.
Su Y, Ma C, Chen J, Wu H, Luo W, Peng Y, Luo Z, Li L, Tan Y, Omisore OM, et al. Printable, highly sensitive flexible temperature sensors for human body temperature monitoring: a review. Nanoscale Res Lett. 2020;15:1–34.CrossRef
25.
Massaroni C, Nicolo A, Sacchetti M, Schena E. Contactless methods for measuring respiratory rate: a review. IEEE Sens J. 2020;21(11):12821–39.CrossRef
26.
Zheng W-L, Gao K, Li G, Liu W, Liu C, Liu J-Q, Wang G, Lu B-L. Vigilance estimation using a wearable EOG device in real driving environment. IEEE Trans Intell Transp Syst. 2019;21(1):170–84.CrossRef
27.
Mustafa MK, Allen T, Appiah K. A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition. Neural Comput Appl. 2019;31:891–9.CrossRef
28.
Adjabi I, Ouahabi A, Benzaoui A, Taleb-Ahmed A. Past, present, and future of face recognition: a review. Electronics. 2020;9(8):1188.CrossRef
29.
Asish SM, Kulshreshth AK, Borst C. Internal distraction detection utilizing EEG data in an educational VR environment. In: ACM Symposium on Applied Perception 2023. 2023. pp. 1–10.
30.
Asish SM, Kulshreshth A, Borst C. Detecting internal distraction in an educational VR environment using EEG data. In: Proceedings of the 2022 ACM Symposium on Spatial User Interaction. 2022. pp. 1–2.
31.
Schuurmans AA, de Looff P, Nijhof KS, Rosada C, Scholte RH, Popma A, Otten R. Validity of the Empatica E4 wristband to measure heart rate variability (HRV) parameters: a comparison to electrocardiography (ECG). J Med Syst. 2020;44:1–11.CrossRef
32.
Yu H, Sano A. Semi-supervised learning for wearable-based momentary stress detection in the wild. Proc ACM Interact Mobile Wearable Ubiquitous Technol. 2023;7(2):1–23.CrossRef
33.
Rashid N, Demirel BU, Odema M, Al Faruque MA. Template matching based early exit CNN for energy-efficient myocardial infarction detection on low-power wearable devices. Proc ACM Interact Mobile Wearable Ubiquitous Technol. 2022;6(2):1–22.CrossRef
34.
Ekman P, Friesen W, Hager J. A technique for the measurement of facial action. Facial Action Coding Syst. 1978:22.
35.
Kiprijanovska I, Jakimovski B, Krstevski P, Stankoski S, Mavridou I, Nduka C, Gjoreski H, Gjoreski M. Monitoring valence with facial EMG sensors using machine learning. In: Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers. 2022. pp. 178–82.
36.
Al-Ayyad M, Owida HA, De Fazio R, Al-Naami B, Visconti P. Electromyography monitoring systems in rehabilitation: a review of clinical applications, wearable devices and signal acquisition methodologies. Electronics. 2023;12(7):1520.CrossRef
37.
Tian Y, Cao J. Fatigue driving detection based on electrooculography: a review. EURASIP J Image Video Process. 2021;2021(1):1–17.
38.
Hatipoglu Yilmaz B, Kose C. A novel signal to image transformation and feature level fusion for multimodal emotion recognition. Biomed Eng/Biomedizinische Technik. 2021;66(4):353–62.
39.
Akçay MB, Oğuz K. Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun. 2020;116:56–76.CrossRef
40.
Zhu J, Ji L, Liu C. Heart rate variability monitoring for emotion and disorders of emotion. Physiol Meas. 2019;40(6):064004.CrossRef
41.
Berwal D, Vandana C, Dewan S, Jiji C, Baghini MS. Motion artifact removal in ambulatory ECG signal for heart rate variability analysis. IEEE Sens J. 2019;19(24):12432–42.CrossRef
42.
Jeong DU, Lim KM. Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features. Sci Rep. 2021;11(1):13539.CrossRef
43.
Chan M, Zhu L, Vatanparvar K, Jung H, Kuang J, Gao A. Improving heart rate and heart rate variability estimation from video through a HR-RR-tuned filter. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2023. pp. 1–5.
44.
Machado ÁS, Priego-Quesada JI, Jimenez-Perez I, Gil-Calvo M, Carpes FP, Perez-Soriano P. Influence of infrared camera model and evaluator reproducibility in the assessment of skin temperature responses to physical exercise. J Therm Biol. 2021;98:102913.CrossRef
45.
Sharma P, Hui X, Zhou J, Conroy TB, Kan EC. Wearable radio-frequency sensing of respiratory rate, respiratory volume, and heart rate. NPJ Digit Med. 2020;3(1):98.
46.
Dolensek N, Gehrlach DA, Klein AS, Gogolla N. Facial expressions of emotion states and their neuronal correlates in mice. Science. 2020;368(6486):89–94.CrossRef
47.
Petrariu AI, Lavric A. SigFox wireless communication enhancement for Internet of Things: a study. In: 12th International Symposium on Advanced Topics in Electrical Engineering (ATEE). IEEE. 2021;2021:1–4.
48.
Alenezi M, Chai KK, Chen Y, Jimaa S. Ultra-dense Lorawan: reviews and challenges. IET Commun. 2020;14(9):1361–71.CrossRef
49.
Lee J, Park C, Roh H. Revisiting adaptive frequency hopping map prediction in Bluetooth with machine learning classifiers. Energies. 2021;14(4):928.CrossRef
50.
Szott S, Kosek-Szott K, Gawłowicz P, Gómez JT, Bellalta B, Zubow A, Dressler F. Wi-Fi meets ML: a survey on improving IEEE 802.11 performance with machine learning. IEEE Commun Surv Tutor. 2022;24(3):1843–93.
51.
Hu Z. Layered network protocols for secure communications in the Internet of Things. University of Oregon: Eugene, OR, USA.
52.
Alsabah M, Naser MA, Mahmmod BM, Abdulhussain SH, Eissa MR, Al-Baidhani A, Noordin NK, Sait SM, Al-Utaibi KA, Hashim F. 6G wireless communications networks: a comprehensive survey. IEEE Access. 2021;9:148191–243.CrossRef
53.
Rastogi E, Saxena N, Roy A, Shin DR. Narrowband Internet of Things: a comprehensive study. Comput Netw. 2020;173:107209.CrossRef
54.
Tafintsev N, Moltchanov D, Chiumento A, Valkama M, Andreev S. Airborne integrated access and backhaul systems: learning-aided modeling and optimization. IEEE Trans Veh Technol.
55.
Guan Y, Geng F, Saleh JH. Review of high throughput satellites: market disruptions, affordability-throughput map, and the cost per bit/second decision tree. IEEE Aerosp Electron Syst Mag. 2019;34(5):64–80.CrossRef
56.
Crisp NH, Roberts PC, Livadiotti S, Oiko VTA, Edmondson S, Haigh S, Huyton C, Sinpetru L, Smith K, Worrall S, et al. The benefits of very low earth orbit for earth observation missions. Prog Aerosp Sci. 2020;117:100619.CrossRef
57.
Jouhari M, Saeed N, Alouini M-S, Amhoud EM. A survey on scalable Lorawan for massive IoT: Recent advances, potentials, and challenges. IEEE Commun Surv Tutor.
58.
Dimitrievski A, Filiposka S, Melero FJ, Zdravevski E, Lameski P, Pires IM, Garcia NM, Lousado JP, Trajkovik V. Rural healthcare IoT architecture based on low-energy LoRa. Int J Environ Res Public Health. 2021;18(14):7660.CrossRef
59.
Migabo EM, Djouani KD, Kurien AM. The narrowband internet of things (NB-IoT) resources management performance state of art, challenges, and opportunities. IEEE Access. 2020;8:97658–75.
60.
Moloudi S, Mozaffari M, Veedu SNK, Kittichokechai K, Wang Y-PE, Bergman J, Höglund A. Coverage evaluation for 5G reduced capability new radio (NR-redcap). IEEE Access. 2021;9:45055–67.
61.
Jia M, Zhang X, Sun J, Gu X, Guo Q. Intelligent resource management for satellite and terrestrial spectrum shared networking toward b5g. IEEE Wirel Commun. 2020;27(1):54–61.CrossRef
62.
Garg RK, Bhola J, Soni SK. Healthcare monitoring of mountaineers by low power wireless sensor networks. Inform Med Unlocked. 2021;27:100775.CrossRef
63.
Liu Z, Tan Y, Li H, Jiang H, Liu J, Liao H. A 0.5-v 3.69-nw complementary source-follower-c based low-pass filter for wearable biomedical applications. IEEE Trans Circ Syst I: Regular Papers. 2020;67(12):4370–81.
64.
Barbosh M, Singh P, Sadhu A. Empirical mode decomposition and its variants: A review with applications in structural health monitoring. Smart Mater Struct. 2020;29(9):093001.CrossRef
65.
Zhang D, Zhang D. Wavelet transform, fundamentals of image data mining: Analysis, features, classification and retrieval. 2019. pp. 35–44.
66.
Agarwal S, Zubair M. Classification of alcoholic and non-alcoholic EEG signals based on sliding-SSA and independent component analysis. IEEE Sens J. 2021;21(23):26198–206.CrossRef
67.
Goumopoulos C, Potha N. Mental fatigue detection using a wearable commodity device and machine learning. J Ambient Intell Humaniz Comput. 2023;14(8):10103–21.CrossRef
68.
Wani TM, Gunawan TS, Qadri SAA, Kartiwi M, Ambikairajah E. A comprehensive review of speech emotion recognition systems. IEEE Access. 2021;9:47795–814.CrossRef
69.
Gaikwad P, Paithane A. Novel approach for stress recognition using EEG signal by SVM classifier. In: 2017 International Conference on Computing Methodologies and Communication (ICCMC). IEEE; 2017. pp. 967–71.
70.
Kaur J, Kumar A. Speech emotion recognition using CNN, k-NN, MLP and random forest. In: Computer Networks and Inventive Communication Technologies: Proceedings of Third ICCNCT 2020. Springer; 2021. pp. 499–509.
71.
Samadiani N, Huang G, Luo W, Shu Y, Wang R, Kocaturk T. A novel video emotion recognition system in the wild using a random forest classifier. In: Data Science: 6th International Conference, ICDS 2019, Ningbo, China, May 15–20, 2019, Revised Selected Papers 6. Springer; 2020. pp. 275–84.
72.
Cacheda F, Fernandez D, Novoa FJ, Carneiro V. Early detection of depression: Social network analysis and random forest techniques. J Med Internet Res. 2019;21(6):e12554.CrossRef
73.
Nandi A, Xhafa F. A federated learning method for real-time emotion state classification from multi-modal streaming. Methods. 2022;204:340–7.CrossRef
74.
Fan J, Wang X, Guo Y, Hu X, Hu B. Federated learning driven secure internet of medical things. IEEE Wirel Commun. 2022;29(2):68–75.CrossRef
75.
Xu X, Li C, Fan X, Lan X, Lu X, Ye X, Wu T. Attention mask r-CNN with edge refinement algorithm for identifying circulating genetically abnormal cells. Cytometry A. 2023;103(3):227–39.CrossRef
76.
Fang B, Chen J, Liu Y, Wang W, Wang K, Singh AK, Lv Z. Dual-Channel neural network for atrial fibrillation detection from a single lead ECG wave. IEEE J Biomed Health Inform. 2023;27(5):2296–305.CrossRef
77.
Peng Z, Lu Y, Pan S, Liu Y. Efficient speech emotion recognition using multi-scale CNN and attention. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEEl; 2021. pp. 3020–4.
78.
Chand HV, Karthikeyan J. CNN based driver drowsiness detection system using emotion analysis. Intell Autom Soft Comput. 31(2).
79.
He L, Chan JC-W, Wang Z. Automatic depression recognition using CNN with attention mechanism from videos. Neurocomputing. 2021;422:165–75.CrossRef
80.
Li Z, Cui Z, Wu S, Zhang X, Wang L. Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019. pp. 539–48.
81.
Xu X, Ruan Z, Yang L. Facial expression recognition based on graph neural network. In: 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC). IEEE; 2020. pp. 211–4.
82.
Gong P, Wang P, Zhou Y, Zhang D. A spiking neural network with adaptive graph convolution and LSTM for EEG-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2023;31:1440–50.CrossRef
83.
Wang J, Xue M, Culhane R, Diao E, Ding J, Tarokh V. Speech emotion recognition with dual-sequence LSTM architecture. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2020. pp. 6474–8.
84.
Mahto D, Yadav SC. Emotion prediction for textual data using glove based HeBi-CuDNNLSTM model. Multimed Tools Appl. 2023:1–26.
85.
Rahul J, Sharma LD. Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG. Biomed Signal Process Control. 2022;71:103270.CrossRef
86.
Agrawal Y, Shanker RGR, Alluri V. Transformer-based approach towards music emotion recognition from lyrics. In: European Conference on Information Retrieval. Springer; 2021. pp. 167–75.
87.
Zou S, Huang X, Shen X, Liu H. Improving multimodal fusion with main modal transformer for emotion recognition in conversation. Knowl-Based Syst. 2022;258:109978.CrossRef
88.
Xu X, Chen J, Li C, Fu C, Yang L, Yan Y, Lyu Z. Robust gaze point estimation for metaverse with common mode features suppression network. IEEE Trans Consum Electron. 2024;70(1):2090–8.CrossRef
89.
Xu X, Lv W, Wang W, Zhang Y, Chen J. Empowering semantic segmentation with selective frequency enhancement and attention mechanism for tampering detection. IEEE Trans Artif Intell.
90.
Wang W, Yu X, Fang B, Zhao Y, Chen Y, Wei W, Chen J. Cross-modality LGE-CMR segmentation using image-to-image translation based data augmentation. IEEE/ACM Trans Comput Biol Bioinf. 2023;20(4):2367–75.CrossRef
91.
Xu X, Li C, Lan X, Fan X, Lv X, Ye X, Wu T. A lightweight and robust framework for circulating genetically abnormal cells (CACs) identification using 4-color fluorescence in situ hybridization (FISH) image and deep refined learning. J Digit Imag. 2023:1–14.
92.
Zhang T, Liu M, Yuan T, Al-Nabhan N. Emotion-aware and intelligent internet of medical things toward emotion recognition during Covid-19 pandemic. IEEE Internet Things J. 2020;8(21):16002–13.
93.
Kasap B, Vali K, Qian W, Saffarpour M, Ghiasi S. Kubai: Sensor fusion for non-invasive fetal heart rate tracking. IEEE Trans Biomed Eng.
94.
Cai H, Qu Z, Li Z, Zhang Y, Hu X, Hu B. Feature-level fusion approaches based on multimodal EEG data for depression recognition. Inform Fusion. 2020;59:127–38.CrossRef
95.
Chen C, Jafari R, Kehtarnavaz N. A real-time human action recognition system using depth and inertial sensor fusion. IEEE Sens J. 2015;16(3):773–81.CrossRef
96.
Azghadi MR, Lammie C, Eshraghian JK, Payvand M, Donati E, Linares-Barranco B, Indiveri G. Hardware implementation of deep network accelerators towards healthcare and biomedical applications. IEEE Trans Biomed Circ Syst. 2020;14(6):1138–59.CrossRef
97.
Chandrasekaran ST, Bhanushali SP, Banerjee I, Sanyal A. Toward real-time, at-home patient health monitoring using reservoir computing CMOS IC. IEEE J Emerg Select Topics Circ Syst. 2021;11(4):829–39.CrossRef
98.
Rahman MA, Hossain MS. An internet-of-medical-things-enabled edge computing framework for tackling covid-19. IEEE Internet Things J. 2021;8(21):15847–54.CrossRef
99.
Park S-H, Bae B-C, Cheong Y-G. Emotion recognition from text stories using an emotion embedding model. In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE; 2020. pp. 579–83.
100.
Shakya S, Joby P. Heart disease prediction using fog computing based wireless body sensor networks (WSNS). IRO J Sustain Wirel Syst. 2021;3(1):49–58.CrossRef
101.
Qiu Y, Zhang H, Long K. Computation offloading and wireless resource management for healthcare monitoring in fog-computing-based internet of medical things. IEEE Internet Things J. 2021;8(21):15875–83.CrossRef
102.
Vasconcelos FF, Sarmento RM, Reboucas Filho PP, de Albuquerque VHC. Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis. Eng Appl Artif Intell. 2020;91:103585.
103.
Kim H, Ben-Othman J, Cho S, Mokdad L. A framework for IoT-enabled virtual emotion detection in advanced smart cities. IEEE Network. 2019;33(5):142–8.CrossRef
104.
Hossain MS, Muhammad G. Emotion recognition using deep learning approach from audio-visual emotional big data. Inform Fusion. 2019;49:69–78.CrossRef
105.
Park J-H, Salim MM, Jo JH, Sicato JCS, Rathore S, Park JH. CIoT-Net: a scalable cognitive IoT based smart city network architecture. HCIS. 2019;9(1):1–20.
106.
Ding N, Ma H, Zhao C, Ma Y, Ge H. Driver’s emotional state-based data anomaly detection for vehicular ad hoc networks. In: 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE; 2019. pp. 121–6.
107.
Gyrard A, Sheth A. IAMHAPPY: Towards an IoT knowledge-based cross-domain well-being recommendation system for everyday happiness. Smart Health. 2020;15:100083.CrossRef
108.
Yang J, Wang R, Guan X, Hassan MM, Almogren A, Alsanad A. Ai-enabled emotion-aware robot: The fusion of smart clothing, edge clouds and robotics. Futur Gener Comput Syst. 2020;102:701–9.CrossRef
109.
Chiu P-S, Chang J-W, Lee M-C, Chen C-H, Lee D-S. Enabling intelligent environment by the design of emotionally aware virtual assistant: a case of smart campus. IEEE Access. 2020;8:62032–41.
110.
Sallah A, Sundaravadivel P. Tot-mon: A real-time internet of things based affective framework for monitoring infants. In: 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). IEEE; 2020. pp. 600–1.
111.
Li J, Shi D, Tumnark P, Xu H. A system for real-time intervention in negative emotional contagion in a smart classroom deployed under edge computing service infrastructure. Peer-to-Peer Netw Appl. 2020;13:1706–19.CrossRef
112.
Yu H, Zhou Z. Optimization of IoT-based artificial intelligence assisted telemedicine health analysis system. IEEE Access. 2021;9:85034–48.CrossRef
113.
Rathi VK, Rajput NK, Mishra S, Grover BA, Tiwari P, Jaiswal AK, Hossain MS. An edge AI-enabled IoT healthcare monitoring system for smart cities. Comput Electr Eng. 2021;96:107524.CrossRef
114.
Li W, Zeng G, Zhang J, Xu Y, Xing Y, Zhou R, Guo G, Shen Y, Cao D, Wang F-Y. Cogemonet: A cognitive-feature-augmented driver emotion recognition model for smart cockpit. IEEE Trans Comput Social Syst. 2021;9(3):667–78.CrossRef
115.
Tan L, Yu K, Lin L, Cheng X, Srivastava G, Lin JC-W, Wei W. Speech emotion recognition enhanced traffic efficiency solution for autonomous vehicles in a 5G-enabled space-air-ground integrated intelligent transportation system. IEEE Trans Intell Transp Syst. 2021;23(3):2830–42.CrossRef
116.
Lai YH, Chang YC, Tsai CW, Lin CH, Chen MY. Data fusion analysis for attention-deficit hyperactivity disorder emotion recognition with thermal image and internet of things devices. Softw: Pract Exp. 2021;51(3):595–606.
117.
Elayan H, Aloqaily M, Guizani M. Digital twin for intelligent context-aware IoT healthcare systems. IEEE Internet Things J. 2021;8(23):16749–57.CrossRef
118.
Ferdousi R, Hossain MA, El Saddik A. IoT-enabled model for digital twin of mental stress (DTMS). In: 2021 IEEE Globecom Workshops (GC Wkshps). IEEE; 2021. pp. 1–6.
119.
Wu X, Zhang Q. Intelligent aging home control method and system for internet of things emotion recognition. Front Psychol. 2022;13:882699.CrossRef
120.
Huang L, Wang N, Yang Z, Guo J. Emotional computing at the edge to support effective IOE applications in future classroom. In: 2022 International Conference on Advanced Learning Technologies (ICALT). IEEE; 2022. pp. 400–2.
121.
Ejaz J, Shah MA. Real-time analysis of student’s behavioural engagement in digital smart classrooms using fog computing and IoT devices. 2022. pp. 49–54.
122.
Chen Z, Feng X, Zhang S. Emotion detection and face recognition of drivers in autonomous vehicles in IoT platform. Image Vis Comput. 2022;128:104569.CrossRef
123.
Subramanian B, Kim J, Maray M, Paul A. Digital twin model: A real-time emotion recognition system for personalized healthcare. IEEE Access. 2022;10:81155–65.CrossRef
124.
Dewalegama M, de Zoysa A, Kodikara L, Dissanayake D, Kuruppu TA, Rupasinghe S, Deep learning-based smart infotainment system for taxi vehicles. In: 2022 International Congress on Human-computer Interaction, Optimization and Robotic Applications (HORA). IEEE; 2022. pp. 1–6.
125.
Dai Z, Zhang Q, Zhao L, Zhu X, Zhou D. Cloud-edge computing technology-based internet of things system for smart classroom environment. Int J Emerg Technol Learn (Online). 2023;18(8):79.CrossRef
126.
Olatinwo DD, Abu-Mahfouz A, Hancke G, Myburgh H. IoT-enabled WBAN and machine learning for speech emotion recognition in patients. Sensors. 2023;23(6):2948.CrossRef
127.
Amara K, Kerdjidj O, Ramzan N. Emotion recognition for affective human digital twin by means of virtual reality enabling technologies. IEEE Access.
128.
Yassine A, Singh S, Hossain MS, Muhammad G. IoT big data analytics for smart homes with fog and cloud computing. Futur Gener Comput Syst. 2019;91:563–73.CrossRef
129.
Abdulkareem KH, Mohammed MA, Salim A, Arif M, Geman O, Gupta D, Khanna A. Realizing an effective Covid-19 diagnosis system based on machine learning and IoT in smart hospital environment. IEEE Internet Things J. 2021;8(21):15919–28.CrossRef
130.
Saini MK, Goel N. How smart are smart classrooms? A review of smart classroom technologies. ACM Comput Surv. 2019;52(6):1–28.
131.
Sisinni E, Saifullah A, Han S, Jennehag U, Gidlund M. Industrial internet of things: Challenges, opportunities, and directions. IEEE Trans Industr Inf. 2018;14(11):4724–34.CrossRef
132.
Nai R. The design of smart classroom for modern college english teaching under internet of things. PLoS ONE. 2022;17(2):e0264176.
133.
Li G, Yang Y, Qu X, Cao D, Li K. A deep learning based image enhancement approach for autonomous driving at night. Knowl-Based Syst. 2021;213:106617.CrossRef
134.
Li W, Zhang B, Wang P, Sun C, Zeng G, Tang Q, Guo G, Cao D. Visual-attribute-based emotion regulation of angry driving behaviors. IEEE Intell Transp Syst Mag. 2021;14(3):10–28.
135.
Li W, Cui Y, Ma Y, Chen X, Li G, Zeng G, Guo G, Cao D. A spontaneous driver emotion facial expression (DEFE) dataset for intelligent vehicles: Emotions triggered by video-audio clips in driving scenarios. IEEE Trans Affect Comput.
136.
McDuff D, Czerwinski M. Designing emotionally sentient agents. Commun ACM. 2018;61(12):74–83.CrossRef
137.
Chen J, Wang W, Fang B, Liu Y, Yu K, Leung VCM, Hu X. Digital twin empowered wireless healthcare monitoring for smart home. IEEE J Sel Areas Commun. 2023;41(11):3662–76.CrossRef
138.
Barricelli BR, Fogli D. Digital twins in human-computer interaction: A systematic review. Int J Human–Comput Interact. 2022:1–19.
139.
Hassan MU, Rehmani MH, Chen J. Differential privacy in blockchain technology: A futuristic approach. J Parallel Distrib Comput. 2020;145:50–74.CrossRef
140.
Rabbi M, Ali S, Choudhury T, Berke E. Passive and in-situ assessment of mental and physical well-being using mobile sensors. In: Proceedings of the 13th International Conference on Ubiquitous Computing. 2011. pp. 385–94.
141.
Shen S, Zhu T, Wu D, Wang W, Zhou W. From distributed machine learning to federated learning: In the view of data privacy and security. Concurr Comput: Pract Exp. 2022;34(16):e6002.CrossRef
142.
Baig MM, Afifi S, GholamHosseini H, Mirza F. A systematic review of wearable sensors and IoT-based monitoring applications for older adults-a focus on ageing population and independent living. J Med Syst. 2019;43:1–11.CrossRef
143.
Kumra S, et al. A survey of acceptability and use of IoT for patient monitoring. In: 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) (vol. 1). IEEE; 2022. pp. 30–6.
144.
Deng C, Fang X, Han X, Wang X, Yan L, He R, Long Y, Guo Y. IEEE 802.11 be Wi-Fi 7: New challenges and opportunities. IEEE Commun Surv Tutor. 2020;22(4):2136–66.
145.
Parween S, Hussain SZ. A review on cross-layer design approach in WSN by different techniques. Adv Sci Technol Eng Syst J. 2020;5(4):741–54.CrossRef
Metadata
Title
Internet of Things for Emotion Care: Advances, Applications, and Challenges
Authors
Xu Xu
Chong Fu
David Camacho
Jong Hyuk Park
Junxin Chen
Publication date
07-08-2024
Publisher
Springer US
Published in
Cognitive Computation
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-024-10327-8

Premium Partner

    Image Credits