[1]          A. Amini; Banitsas, Konstantinos, “A Prototype System Using Microsoft Kinect to Recognize Freezing of Gait in Parkinson’s Disease Patients,” 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), August 26-30, 2014, Chicago, Illinois, USA, (2014).

 

[2]          A. Amini, “An Unobtrusive System For Detecting Parkinson’s FOG Episodes,” in 7th Annual Student Research Conference (ResCon’14), Brunel University London, London, UK, 2014.

 

[3]          A. Amini, K. Banitsas, A. Badii and J. Cosmas, "Recognition of postures and Freezing of Gait in Parkinson's disease patients using Microsoft Kinect sensor," 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), Montpellier, France, 2015, pp. 731-734, doi: 10.1109/NER.2015.7146727.

 

[4]          A. Amini, K. Banitsas and J. Cosmas, "A comparison between heuristic and machine learning techniques in fall detection using Kinect v2," 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Benevento, Italy, 2016, pp. 1-6, doi: 10.1109/MeMeA.2016.7533763.

 

[5]          A. Amini, K. Banitsas and S. Hosseinzadeh, "A new technique for foot-off and foot contact detection in a gait cycle based on the knee joint angle using microsoft kinect v2," 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, USA, 2017, pp. 153-156, doi: 10.1109/BHI.2017.7897228.

 

[6]          S. Hosseinzadeh, H. Larijani, K. Curtis, A. Wixted and A. Amini, "Empirical propagation performance evaluation of LoRa for indoor environment," 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Emden, Germany, 2017, pp. 26-31, doi: 10.1109/INDIN.2017.8104741.

 

[7]          Amin Amini, Konstantinos Banitsas & William R. Young (2019) Kinect4FOG: monitoring and improving mobility in people with Parkinson’s using a novel system incorporating the Microsoft Kinect v2, Disability and Rehabilitation: Assistive Technology, 14:6, 566-573, DOI: 10.1080/17483107.2018.1467975.

 

[8]          A. Amini, “Using 3D sensing and projecting technology to improve the mobility of Parkinson’s disease patients” Brunel University London, URI: http://bura.brunel.ac.uk/handle/2438/16216.

 

[9]          Amin Amini & Konstantinos Banitsas (2019) An improved technique for increasing the accuracy of joint-to-ground distance tracking in kinect v2 for foot-off and foot contact detection, Journal of Medical Engineering & Technology, 43:1, 8-18, DOI: 10.1080/03091902.2019.1595762.

 

[10]        Amini A, Banitsas K. Using Kinect v2 to Control a Laser Visual Cue System to Improve the Mobility during Freezing of Gait in Parkinson's Disease. J Healthc Eng. 2019 Feb 20;2019:3845462. doi: 10.1155/2019/3845462. PMID: 30915207; PMCID: PMC6402218.

 

[11]        A. Amini, “Using Microsoft Kinect to Recognise Postures of Parkinson’s Disease Patients” Brunel University London.

 

[12]        A. Amini, “Case Report: Using Unobtrusive Techniques to Improve the Mobility of People with Parkinson’s Experiencing Freezing of Gait” Journal of Healthcare Engineering” in 2019 International Forum of Neuroscience (FON 2019), Brussels, Belgium.

 

[13]        Amin Amini, Jamil Kanfound, and Tat-Hean Gan. 2020. An AI Driven Real-time 3-D Representation of an Off-shore WT for Fault Diagnosis and Monitoring. In Proceedings of the 3rd International Conference on Advances in Artificial Intelligence (ICAAI '19). Association for Computing Machinery, New York, NY, USA, 162–165. https://doi.org/10.1145/3369114.3369141.

 

[14]        Amini, A. (2020). On the Possibility of Using Virtual Reality to Improve the Mobility of People with Parkinson’s Disease. In Virtual Reality in Health and Rehabilitation (1st ed., p. 9). Taylor and Francis Group. https://doi.org/10.1201/9780429351365.

 

[15]        Gan, T.H., Kanfoud, J., Nedunuri, H., Amini, A., Feng, G. (2021). Industry 4.0: Why Machine Learning Matters?. In: Gelman, L., Martin, N., Malcolm, A.A., (Edmund) Liew, C.K. (eds) Advances in Condition Monitoring and Structural Health Monitoring. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9199-0_37.

 

[16]        Amini, Amin, Jamil Kanfoud, and Tat-Hean Gan. 2021. "An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS" Sensors 21, no. 18: 6141. https://doi.org/10.3390/s21186141.

 

[17]        Amin Amini, Jamil Kanfoud & Tat-Hean Gan (2022) An Artificial Intelligence Neural Network Predictive Model for Anomaly Detection and Monitoring of Wind Turbines Using SCADA Data, Applied Artificial Intelligence, 36:1, DOI: 10.1080/08839514.2022.2034718.

 

[18]        A. Amini and T. -H. Gan, "A Machine Learning Based Model for Monitoring of Composites’ Drilling-Induced Defects During Assembly Production Using Terahertz Imaging Data," 2022 IEEE Workshop on Complexity in Engineering (COMPENG), Florence, Italy, 2022, pp. 1-5, doi: 10.1109/COMPENG50184.2022.9905438.

 

[19]        Amini, A.; Gan, T.-H. A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates. Appl. Sci. 2023, 13, 1084. https://doi.org/10.3390/app13021084.

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[20]        A. Amini, "Kinect Station: Using Microsoft Kinect v2 as a Total Station Theodolite for Distance and Angle Determination in a 3D Cartesian Environment," 2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Istanbul, Turkiye, 2024, pp. 1-5, doi: 10.1109/HORA61326.2024.10550801.

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[21]        Moghimi, O.; Amini, A. A Novel Approach for Solving the N-Queen Problem Using a Non-Sequential Conflict Resolution Algorithm. Electronics 2024, 13, 4065. https://doi.org/10.3390/electronics13204065..