Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Physical activity and mental health in children and adolescents: a review of reviews

Biddle, S. J. H., & Asare, M. (2011) · British Journal of Sports Medicine · doi:10.1136/bjsports-2011-090185 · Cited by 2,447 · see citing works

Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0

Swan, M. (2012) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan1030217 · Cited by 934 · see citing works

Analysis of emotion recognition using facial expressions, speech and multimodal information

Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Lee, S., Neumann, U., & Narayanan, S. (2004) · Proceedings of the 6th international conference on Multimodal interfaces · doi:10.1145/1027933.1027968 · Cited by 840 · see citing works

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works

Transfer learning for activity recognition: a survey

Cook, D., Feuz, K. D., & Krishnan, N. C. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0665-3 · Cited by 447 · see citing works

Physical activity and cancer prevention–data from epidemiologic studies

LEE, I.-M. (2003) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000093620.27893.23 · Cited by 436 · see citing works

Facial emotion recognition using multi-modal information

De Silva, L. C., Miyasato, T., & Nakatsu, R. (2002) · Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat. No.97TH8237) · doi:10.1109/icics.1997.647126 · Cited by 319 · see citing works

Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients

Capela, N. A., Lemaire, E. D., & Baddour, N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124414 · Cited by 197 · see citing works

Multimodal human emotion/expression recognition

Chen, L. S., Huang, T. S., Miyasato, T., & Nakatsu, R. (2002) · Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1998.670976 · Cited by 183 · see citing works

Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data

Wagner, J., Andre, E., Lingenfelser, F., & Kim, J. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.12 · Cited by 156 · see citing works

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works

Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review

Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.011 · Cited by 806 · see citing works

Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671 · see citing works

InnoHAR: A Deep Neural Network for Complex Human Activity Recognition

Xu, C., Chai, D., He, J., Zhang, X., & Duan, S. (2019) · IEEE Access · doi:10.1109/access.2018.2890675 · Cited by 333 · see citing works

Imaging and fusing time series for wearable sensor-based human activity recognition

Qin, Z., Zhang, Y., Meng, S., Qin, Z., & Choo, K.-K. R. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.06.014 · Cited by 309 · see citing works

A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks

Muzammal, M., Talat, R., Sodhro, A. H., & Pirbhulal, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.06.021 · Cited by 311 · see citing works

A comprehensive survey on multimodal medical signals fusion for smart healthcare systems

Muhammad, G., Alshehri, F., Karray, F., Saddik, A. E., Alsulaiman, M., & Falk, T. H. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.007 · Cited by 287 · see citing works

Emotion-aware connected healthcare big data towards 5G

Hossain, M. S., & Muhammad, G. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2772959 · Cited by 273 · see citing works

Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition

Boulahia, S. Y., Amamra, A., Madi, M. R., & Daikh, S. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01249-8 · Cited by 236 · see citing works

Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

Mohamed Shakeel, P., Tobely, T. E. E., Al-Feel, H., Manogaran, G., & Baskar, S. (2019) · IEEE Access · doi:10.1109/access.2018.2883957 · Cited by 232 · see citing works

Voice Disorder Identification by Using Machine Learning Techniques

Verde, L., De Pietro, G., & Sannino, G. (2018) · IEEE Access · doi:10.1109/access.2018.2816338 · Cited by 156 · see citing works

A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data

Gumaei, A., Hassan, M. M., Alelaiwi, A., & Alsalman, H. (2019) · IEEE Access · doi:10.1109/access.2019.2927134 · Cited by 157 · see citing works

Use of wearable sensor technology in gait, balance, and range of motion analysis

Díaz, S., Stephenson, J. B., & Labrador, M. A. (2019) · Applied Sciences · doi:10.3390/app10010234 · Cited by 132 · see citing works

COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images

Muhammad, G., & Shamim Hossain, M. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.013 · Cited by 120 · see citing works

Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach

Kanjo, E., Younis, E. M. G., & Sherkat, N. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.05.005 · Cited by 118 · see citing works

Recent Advances in Multifunctional Sensing Technology on a Perspective of Multi-Sensor System: A Review

Deb Majumder, B., Roy, J. K., & Padhee, S. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2882239 · Cited by 104 · see citing works

A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva

Kammarchedu, V., Butler, D., & Ebrahimi, A. (2022) · Analytica Chimica Acta · doi:10.1016/j.aca.2022.340447 · Cited by 79 · see citing works

Multitask LSTM Model for Human Activity Recognition and Intensity Estimation Using Wearable Sensor Data

Barut, O., Zhou, L., & Luo, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2996578 · Cited by 70 · see citing works

A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems

Kartsch, V. J., Benatti, S., Schiavone, P. D., Rossi, D., & Benini, L. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.11.005 · Cited by 69 · see citing works

Geometrical kinematic modeling on human motion using method of multi-sensor fusion

Xu, C., He, J., Zhang, X., Yao, C., & Tseng, P.-H. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.09.014 · Cited by 58 · see citing works

Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia

Cui, J., Wang, L., He, X., De Albuquerque, V. H. C., AlQahtani, S. A., & Hassan, M. M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-021-06487-5 · Cited by 43 · see citing works

ANFIS fusion algorithm for eye movement recognition via soft multi-functional electronic skin

Dong, W., Yang, L., Gravina, R., & Fortino, G. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.003 · Cited by 37 · see citing works

Next-Generation Wearable Optical Sensors for Personalized Health and Point-of-Care Diagnostics—A Systematic Review

Duarah, R., Torné‐Morató, H., Zhang, G., Amin, Y., Pabast, M., Sharma, N., He, S., Das, M. R., & Pompa, P. P. (2026) · Advanced Healthcare Materials · doi:10.1002/adhm.202504419 · Cited by 24 · see citing works

Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol

Wang, H., Li, J., McDonald, B. E., Farrell, T. R., Huang, X., & Clancy, E. A. (2023) · Sensors · doi:10.3390/s23052465 · Cited by 18 · see citing works

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Liu, Q., Wu, X., Jiang, Y., Liu, X., Zhang, Y., Xu, X., & Qi, L. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22383 · Cited by 16 · see citing works

Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains

Molino-Minero-Re, E., Aguileta, A. A., Brena, R. F., & Garcia-Ceja, E. (2021) · Sensors · doi:10.3390/s21217007 · Cited by 11 · see citing works