AiCarePWP: : Deep learning-based novel research for Freezing of Gait forecasting in Parkinson
Authors: 
Hemant Ghayvat, Muhammad Awais, Rebakah Geddam, Muhammad Ahmed Khan, Lewis Nkenyereye, Giancarlo Fortino, Kapal DevAuthors Info & Claims
References
[1]
Zhou Y., Jenkins M.E., Naish M.D., Trejos A.L., Design and validation of a high-order weighted-frequency fourier linear combiner-based Kalman filter for Parkinsonian tremor estimation, in: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, IEEE, 2016, pp. 5893–5896.
[2]
Michmizos K.P., Frangou P., Stathis P., Sakas D., Nikita K.S., Beta-band frequency peaks inside the subthalamic nucleus as a biomarker for motor improvement after deep brain stimulation in Parkinson’s disease, IEEE J. Biomed. Health Inf. 19 (1) (2015) 174–180,.
[3]
Kraus P., Lemke M., Reichmann H., Kinetic tremor in Parkinson’s disease–an underrated symptom, J. Neural Transm. 113 (2006) 845–853.
[4]
Williams-Gray C.H., Worth P.F., Parkinson’s disease and related conditions, Medicine 51 (9) (2023) 645–651,. URL https://www.sciencedirect.com/science/article/pii/S1357303923001366.
[5]
Guo M., Ren Y., Yu H., Yang H., Cao C., Li Y., Fan G., Alterations in degree centrality and functional connectivity in Parkinson’s disease patients with freezing of gait: a resting-state functional magnetic resonance imaging study, Front. Neurosci. 14 (2020).
[6]
Mancini M., Bloem B.R., Horak F.B., Lewis S.J., Nieuwboer A., Nonnekes J., Clinical and methodological challenges for assessing freezing of gait: future perspectives, Movem. Disord. 34 (6) (2019) 783–790.
[7]
Thompson P.D., Nutt J.G., Gait disorders, in: Bradley’s Neurology in Clinical Practice E-Book, Vol. 334, Elsevier Health Sciences, 2021.
[8]
Zheng W., Yan L., Gou C., Wang F.-Y., Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition, Inf. Fusion 80 (2022) 1–22.
[9]
Yin Z., Geraedts V.J., Wang Z., Contarino M.F., Dibeklioglu H., van Gemert J., Assessment of Parkinson’s disease severity from videos using deep architectures, IEEE J. Biomed. Health Inf. 26 (3) (2022) 1164–1176,.
[10]
Qiu S., Zhao H., Jiang N., Wang Z., Liu L., An Y., Zhao H., Miao X., Liu R., Fortino G., Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges, Inf. Fusion 80 (2022) 241–265.
[11]
Celik Y., Stuart S., Woo W.L., Sejdic E., Godfrey A., Multi-modal gait: A wearable, algorithm and data fusion approach for clinical and free-living assessment, Inf. Fusion 78 (2022) 57–70.
[12]
Pardoel S., Shalin G., Lemaire E.D., Kofman J., Nantel J., Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson’s disease, PLoS One 16 (10) (2021).
[13]
Subramanian S.K., Baniña M.C., Sambasivan K., Haentjens K., Finestone H.M., Sveistrup H., Levin M.F., Motor-equivalent intersegmental coordination is impaired in chronic stroke, Neurorehabil. Neural Repair 34 (3) (2020) 210–221.
[14]
Mazilu S., Calatroni A., Gazit E., Mirelman A., Hausdorff J.M., Tröster G., Prediction of freezing of gait in Parkinson’s from physiological wearables: An exploratory study, IEEE J. Biomed. Health Inf. 19 (6) (2015) 1843–1854,.
[15]
Sweeney D., Quinlan L.R., Browne P., Counihan T., Rodriguez-Molinero A., ÓLaighin G., Applicability and tolerability of electrical stimulation applied to the upper and lower leg skin surface for cueing applications in Parkinson’s disease, Med. Eng. Phys. 87 (2021) 73–81,. URL https://www.sciencedirect.com/science/article/pii/S1350453320301715.
[16]
Pierleoni P., Belli A., Bazgir O., Maurizi L., Paniccia M., Palma L., A smart inertial system for 24h monitoring and classification of tremor and freezing of gait in Parkinson’s disease, IEEE Sens. J. 19 (23) (2019) 11612–11623,.
[17]
Sweeney D., Quinlan L.R., Browne P., Richardson M., Meskell P., ÓLaighin G., A technological review of wearable cueing devices addressing freezing of gait in Parkinson’s disease, Sensors 19 (6) (2019) 1277.
[18]
Sun F., Zang W., Gravina R., Fortino G., Li Y., Gait-based identification for elderly users in wearable healthcare systems, Inf. Fusion 53 (2020) 134–144.
[19]
Cousin C.A., Rouse C.A., Dixon W.E., Split-crank functional electrical stimulation cycling: An adapting admitting rehabilitation robot, IEEE Trans. Control Syst. Technol. 29 (5) (2021) 2153–2165,.
[20]
Faraji B., Gheisarnejad M., Yalsavar M., Khooban M.-H., An adaptive ADRC control for parkinson’s patients using machine learning, IEEE Sens. J. 21 (6) (2021) 8670–8678,.
[21]
Kim S., Kang S., Kim J., Lee D., Kim S., Lee J., Jang K.-I., Oh Y.-S., Rah J.-C., Huh M.S., Paek S.H., Choi J.-W., Closed-loop neuromodulation for Parkinson’s disease: Current state and future directions, IEEE Trans. Mol., Biol. Multi-Scale Commun. 7 (4) (2021) 209–223,.
[22]
Pasluosta C.F., Gassner H., Winkler J., Klucken J., Eskofier B.M., An emerging era in the management of Parkinson’s disease: Wearable technologies and the Internet of Things, IEEE J. Biomed. Health Inf. 19 (6) (2015) 1873–1881,.
[23]
Walton C.C., Shine J.M., Hall J.M., O’Callaghan C., Mowszowski L., Gilat M., Szeto J.Y., Naismith S.L., Lewis S.J., The major impact of freezing of gait on quality of life in Parkinson’s disease, J. Neurol. 262 (2015) 108–115.
[24]
Lu C., Ge R., Tang Z., Fu X., Zhang L., Yang K., Xu X., Multi-channel FES gait rehabilitation assistance system based on adaptive sEMG modulation, IEEE Trans. Neural Syst. Rehabil. Eng. (2023).
[25]
Chatzaki C., Skaramagkas V., Kefalopoulou Z., Tachos N., Kostikis N., Kanellos F., Triantafyllou E., Chroni E., Fotiadis D.I., Tsiknakis M., Can gait features help in differentiating Parkinson’s disease medication states and severity levels? A machine learning approach, Sensors 22 (24) (2022) 9937.
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Elsevier B.V.
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Elsevier North-Holland, Inc.
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Published: 25 September 2024
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