IEIE SPC - IEIE Transactions on Smart Processing & Computing

1

Bijalwan, V., Semwal, V.B., Singh, G. and Mandal, T.K. (2022). HDL-PSR: Modelling Spatio-Temporal Features Using Hybrid Deep Learning Approach for Post-Stroke Rehabilitation. Neural Processing Letters.

2

Delvigne, V., Facchini, A., Wannous, H., Dutoit, T., Ris, L. and Vandeborre, J.-P. (2022). A Saliency based Feature Fusion Model for EEG Emotion Estimation. arXiv:2201.03891 [cs]. [online] Available at: \url{https://arxiv.org/abs/2201.03891} [Accessed 8 Mar. 2022].

3

Ertuğrul, ö.F. and Akıl, M.F. (2022). Detecting hemorrhage types and bounding box of hemorrhage by deep learning. Biomedical Signal Processing and Control, 71, p. 103085.

4

Gan, S., Zhuang, Q. and Gong, B. (2022). Human-computer interaction-based interface design of intelligent health detection using PCANet and multi-sensor information fusion. Computer Methods and Programs in Biomedicine, 216, p. 106637.

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Ghosh, A., Umer, S., Khan, M.K., Rout, R.K. and Dhara, B.C. (2022). Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework. Cluster Computing.

6

Heyse, J., Carlier, S., Verhelst, E., Vander Linden, C., De Backere, F. and De Turck, F. (2022). From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect. Applied Sciences, [online] 12(3), p. 1242.

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Kim, J.S., Kim, M.-G. and Pan, S.B. (2021). A study on implementation of real-time intelligent video surveillance system based on embedded module. EURASIP Journal on Image and Video Processing, 2021(1).

8

Liang, R., Yip, J., Fan, Y., Cheung, J.P.Y. and To, K.-T.M. (2022). Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using Machine Learning Approaches. International Journal of Environmental Research and Public Health, [online] 19(3), p. 1177.

9

Ma, W. and Xu, F. (2020). Study on computer vision target tracking algorithm based on sparse representation. Journal of Real-Time Image Processing, 18(2), pp. 407-418.

10

Mohamed, N.A., Zulkifley, M.A., Kamari, N.A.M. and Kadim, Z. (2022). Symmetrically Stacked Long Short-Term Memory Networks for Fall Event Recognition Using Compact Convolutional Neural Networks-Based Tracker. Symmetry, [online] 14(2), p. 293.

11

Mokri, C., Bamdad, M. and Abolghasemi, V. (2022). Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques. Medical & Biological Engineering & Computing.

12

Oh, S. and Kim, D.-K. (2022). Comparative Analysis of Emotion Classification Based on Facial Expression and Physiological Signals Using Deep Learning. Applied Sciences, [online] 12(3), p. 1286.

13

Park, C., An, Y., Yoon, H., Park, I., Kim, K., Kim, C. and Cha, Y. (2022). Comparative accuracy of a shoulder range motion measurement sensor and Vicon 3D motion capture for shoulder abduction in frozen shoulder. Technology and Health Care, [online] 30(S1), pp. 251-257.

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Qiu, Y., Wang, J., Jin, Z., Chen, H., Zhang, M. and Guo, L. (2022). Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training. Biomedical Signal Processing and Control, 72, p. 103323.

15

Rahman, Z.U., Ullah, S.I., Salam, A., Rahman, T., Khan, I. and Niazi, B. (2022). Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model. Journal of Healthcare Engineering, 2022, pp. 1-12.

16

Schlereth, M., Stromer, D., Breininger, K., Wagner, A., Tan, L., Maier, A. and Knieling, F. (2022). Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging. arXiv:2201.11630 [cs, eess]. [online] Available at: \url{https://arxiv.org/abs/2201.11630} [Accessed 8 Mar. 2022].

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Sun, W. and Mo, C. (2020). High-speed real-time augmented reality tracking algorithm model of camera based on mixed feature points. Journal of Real-Time Image Processing.

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Xiao, B., Chen, L., Zhang, X., Li, Z., Liu, X., Wu, X. and Hou, W. (2022). Design of a virtual reality rehabilitation system for upper limbs that inhibits compensatory movement. Medicine in Novel Technology and Devices, 13, p. 100110.

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Yue, S. (2020). Human motion tracking and positioning for augmented reality. Journal of Real-Time Image Processing. J Real-Time Image Proc 18, 357-368 (2021)\colorbox{color-5}{\textcolor{color-8}{.}}

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Zhang, X., Xu, Z. and Liao, H. (2022). Human motion tracking and 3D motion track detection technology based on visual information features and machine learning. Neural Computing and Applications.

21

Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J. and McDonald-Maier, K.D. (2022). Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control, 71, p. 103197.

22

Costa, F., Janela, D., Molinos, M., Lains, J., Francisco, G.E., Bento, V. and Dias Correia, F. (2022). Telerehabilitation of acute musculoskeletal multi-disorders: prospective, single-arm, interventional study. BMC Musculoskeletal Disorders, 23(1).

23

Igwesi-Chidobe, C.N., Bishop, A., Humphreys, K., Hughes, E., Protheroe, J., Maddison, J. and Bartlam, B. (2020). Implementing patient direct access to musculoskeletal physiotherapy in primary care: views of patients, general practitioners, physiotherapists and clinical commissioners in England. Physiotherapy.

24

Ratcliffe, J., Soave, F., Bryan-Kinns, N., Tokarchuk, L. and Farkhatdinov, I. (2021). Extended Reality (XR) Remote Research: a Survey of Drawbacks and Opportunities. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems.

25

Tack, C. (2019). Artificial intelligence and machine learning{\textbar} applications in musculoskeletal physiotherapy. Musculoskeletal Science and Practice, 39, 164-169.

26

Li, Y., Chen, R., Niu, X., Zhuang, Y., Gao, Z., Hu, X., & El-Sheimy, N. (2021). Inertial Sensing Meets Machine Learning: Opportunity or Challenge?. IEEE Transactions on Intelligent Transportation Systems.

27

Panicker, S. S., & Gayathri, P. (2019). A survey of machine learning techniques in physiology based mental stress detection systems. Biocybernetics and Biomedical Engineering, 39(2), 444-469.

28

Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., & Fortino, G. (2022). Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges. Information Fusion, 80, 241-265.

29

Zad, S., Heidari, M., Jones, J.H.J. and Uzuner, O. (2021). Emotion Detection of Textual Data: An Interdisciplinary Survey. \textit{2021 IEEE World AI IoT Congress(AIIoT)}.doi:10.1109/aiiot52608.2021.9454192.

30

Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 1-19.

31

Lim, J. Z., Mountstephens, J., & Teo, J. (2020). Emotion recognition using eye-tracking: taxonomy, review and current challenges. Sensors, 20(8), 2384.

32

Saxena, A., Khanna, A., & Gupta, D. (2020). Emotion recognition and detection methods: A comprehensive survey. Journal of Artificial Intelligence and Systems, 2(1), 53-79.

33

Davoli, A., Guerzoni, G., & Vitetta, G. M. (2021). Machine learning and deep learning techniques for colocated MIMO radars: A tutorial overview. IEEE Access,9,33704-33755.

34

Ali, O., Ishak, M. K., & Bhatti, M. K. L. (2021). Early COVID-19 symptoms identification using hybrid unsupervised machine learning techniques. Computers, Materials, and Continua, 747-766.

35

Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F. X., ... & Holmes, A. H. (2020). Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clinical Microbiology and Infection, 26(5), 584-595.

36

Kelly, D., Hoang, T. N., Reinoso, M., Joukhadar, Z., Clements, T., & Vetere, F. (2018). Augmented reality learning environment for physiotherapy education. Physical Therapy Reviews, 23(1), 21-28.

37

Fahle, S., Prinz, C., & Kuhlenkötter, B. (2020). Systematic review on machine learning (ML) methods for manufacturing processes-Identifying artificial intelligence (AI) methods for field application. Procedia CIRP, 93, 413-418.

38

De Filippis, R., Carbone, E. A., Gaetano, R., Bruni, A., Pugliese, V., Segura-Garcia, C., & De Fazio, P. (2019). Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review. Neuropsychiatric disease and treatment, 15, 1605.

39

Ngiam, K. Y., & Khor, W. (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, 20(5), e262-e273.

40

Angehrn, Z., Haldna, L., Zandvliet, A. S., Gil Berglund, E., Zeeuw, J., Amzal, B., ... & Heckman, N. M. (2020). Artificial intelligence and machine learning applied at the point of care. Frontiers in Pharmacology, 11, 759.

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IEIE Transactions on Smart Processing & Computing

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IEIE SPC

IEIE Transactions on Smart Processing & Computing