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Christiane Zimmermann, T. Brox (2017)
Learning to Estimate 3D Hand Pose from Single RGB Images2017 IEEE International Conference on Computer Vision (ICCV)
W. Poewe, K. Seppi, C. Tanner, G. Halliday, P. Brundin, J. Volkmann, A. Schrag, A. Lang (2017)
Parkinson diseaseNature Reviews Disease Primers, 3
Ji-Won Kim, Jae-Ho Lee, Y. Kwon, Chul-Seung Kim, G. Eom, S. Koh, D. Kwon, Kun-Woo Park (2011)
Quantification of bradykinesia during clinical finger taps using a gyrosensor in patients with Parkinson’s diseaseMedical & Biological Engineering & Computing, 49
E. R. l (2007)
Dorsey, RConstantinescu, 2005
V. Calabrese (2007)
Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030.Neurology, 69 2
K. Niazmand, K. Tonn, Y. Zhao, Urban Fietzek, F. Schroeteler, K. Ziegler, Andres Ceballos-Baumann, Tim Lueth (2011)
Freezing of Gait detection in Parkinson's disease using accelerometer based smart clothes2011 IEEE Biomedical Circuits and Systems Conference (BioCAS)
(2016)
Teus van Laar
Wenying Zhang, Huaguang Zhang, Jinhai Liu, K. Li, Dongsheng Yang, Hui Tian (2017)
Weather prediction with multiclass support vector machines in the fault detection of photovoltaic systemIEEE/CAA Journal of Automatica Sinica, 4
Jonathan Chung, Sarah Chau, N. Herrmann, K. Lanctôt, M. Eizenman (2018)
Detection of Apathy in Alzheimer Patients by Analysing Visual Scanning Behaviour with RNNsProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
(2011)
Martin - Gonzalez , Carmen Rodríguez - Blázquez , Jaime Kulisevsky , and ELEP Group Members
Martín Abadi, P. Barham, Jianmin Chen, Z. Chen, Andy Davis, J. Dean, Matthieu Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, Sherry Moore, D. Murray, Benoit Steiner, P. Tucker, Vijay Vasudevan, P. Warden, M. Wicke, Yuan Yu, Xiaoqiang Zhang (2016)
TensorFlow: A system for large-scale machine learning
P. Derkinderen, T. Rouaud, T. Lebouvier, S. Varannes, M. Neunlist, R. Giorgio (2011)
Parkinson diseaseNeurology, 77
A. Bandini, S. Orlandi, F. Giovannelli, A. Felici, M. Cincotta, D. Clemente, P. Vanni, G. Zaccara, C. Manfredi (2016)
Markerless Analysis of Articulatory Movements in Patients With Parkinson's Disease.Journal of voice : official journal of the Voice Foundation, 30 6
G. Rigas, A. Tzallas, M. Tsipouras, P. Bougia, E. Tripoliti, D. Baga, D. Fotiadis, S. Tsouli, S. Konitsiotis (2012)
Assessment of Tremor Activity in the Parkinson’s Disease Using a Set of Wearable SensorsIEEE Transactions on Information Technology in Biomedicine, 16
N. Tahir, H. Manap (2012)
Parkinson Disease gait classification based on machine learning approachJournal of Applied Sciences, 12
E. Bakštein, K. Warwick, Jonathan Burgess, Oyvind Stavdahl, T. Aziz (2010)
Features for detection of Parkinson's disease tremor from local field potentials of the subthalamic nucleus2010 IEEE 9th International Conference on Cyberntic Intelligent Systems
Peng Dai, F. Gwadry-Sridhar, Michael Bauer, M. Borrie (2016)
Bagging Ensembles for the Diagnosis and Prognostication of Alzheimer's Disease
(2015)
Keras. Retrieved from https://github.com/fchollet/keras
Alex Graves (2013)
Generating Sequences With Recurrent Neural NetworksArXiv, abs/1308.0850
K. Niazmand, K. Tonn, Anastasios Kalaras, S. Kammermeier, K. Boetzel, J. Mehrkens, T. Lüth (2011)
A measurement device for motion analysis of patients with Parkinson's disease using sensor based smart clothes2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops
Shaohua Teng, N. Wu, Haibin Zhu, Luyao Teng, Wei Zhang (2018)
SVM-DT-based adaptive and collaborative intrusion detectionIEEE/CAA Journal of Automatica Sinica, 5
Bryan Cole, Serge Roy, S. Nawab (2011)
Detecting freezing-of-gait during unscripted and unconstrained activity2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Peiyun Zhang, Sheng Shu, Mengchu Zhou (2018)
An online fault detection model and strategies based on SVM-grid in cloudsIEEE/CAA Journal of Automatica Sinica, 5
S. Spasojevic, T. Ilić, I. Stojković, V. Potkonjak, A. Rodic, J. Santos-Victor (2017)
Quantitative Assessment of the Arm/Hand Movements in Parkinson’s Disease Using a Wireless Armband DeviceFrontiers in Neurology, 8
E. Cubo, P. Martín, J. Martín-González, C. Rodríguez-Blázquez, J. Kulisevsky (2010)
Motor laterality asymmetry and nonmotor symptoms in Parkinson's diseaseMovement Disorders, 25
A. Bonnet (2000)
[The Unified Parkinson's Disease Rating Scale].Revue neurologique, 156 5
S. Emrani, Anya McGuirk, Wei Xiao (2017)
Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task LearningProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
M. Pastorino, J. Cancela, M. Arredondo, Mario Pansera, Laura Pastor-Sanz, F. Villagra, M. Pastor, José Martín (2011)
Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
S. Fahn (1987)
Members of the UPDRS Development Committee. Unified Parkinson's Disease Rating Scale, 2
C. Goetz, B. Tilley, S. Shaftman, G. Stebbins, S. Fahn, P. Martínez-Martín, W. Poewe, C. Sampaio, M. Stern, R. Dodel, B. Dubois, R. Holloway, J. Jankovic, J. Kulisevsky, A. Lang, A. Lees, S. Leurgans, P. LeWitt, D. Nyenhuis, C. Olanow, O. Rascol, A. Schrag, J. Teresi, J. Hilten, Nancy Lapelle (2008)
Movement Disorder Society‐sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS‐UPDRS): Scale presentation and clinimetric testing resultsMovement Disorders, 23
K. Niazmand, K. Tonn, Anastasios Kalaras, U. Fietzek, J. Mehrkens, T. Lüth (2011)
Quantitative evaluation of Parkinson's disease using sensor based smart glove2011 24th International Symposium on Computer-Based Medical Systems (CBMS)
A. Bandini, S. Orlandi, H. Escalante, F. Giovannelli, M. Cincotta, C. Reyes-García, P. Vanni, G. Zaccara, C. Manfredi (2017)
Analysis of facial expressions in parkinson's disease through video-based automatic methodsJournal of Neuroscience Methods, 281
(1987)
and Members of the UPDRS Development Committee
W. R. Gibb, A. J. Lees (1988)
The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson’s diseaseThe relevance of the Lewy body to the pathogenesis of idiopathic Parkinson’s disease.Journal of Neurology, 8
Xiaoli Liu, Peng Cao, A. Gonçalves, Dazhe Zhao, A. Banerjee (2018)
Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group LassoACM Transactions on Knowledge Discovery from Data (TKDD), 12
Z. Nasreddine, N. Phillips, Valérie Bédirian, S. Charbonneau, V. Whitehead, Isabelle Collin, J. Cummings, H. Chertkow (2005)
The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive ImpairmentJournal of the American Geriatrics Society, 53
O. Manzanera, E. Roosma, M. Beudel, R. Borgemeester, T. Laar, N. Maurits (2016)
A Method for Automatic and Objective Scoring of Bradykinesia Using Orientation Sensors and Classification AlgorithmsIEEE Transactions on Biomedical Engineering, 63
V. Feigin, A. Abajobir, K. Abate, F. Abd-Allah, A. Abdulle, S. Abera, G. Abyu, M. Ahmed, Amani Aichour, Ibtihel Aichour, Miloud Aichour, R. Akinyemi, S. Alabed, Rajaa Al-Raddadi, N. Alvis-Guzmán, A. Amare, Hossein Ansari, P. Anwari, J. Ärnlöv, H. Asayesh, S. Asgedom, T. Atey, L. Ávila-Burgos, Euripide Frinel, G. Avokpaho, M. Azarpazhooh, A. Barać, Miguel Barboza, S. Barker-Collo, T. Bärnighausen, Neeraj Bedi, E. Beghi, Derrick Bennett, I. Benseñor, A. Berhane, B. Betsu, Soumyadeep Bhaumik, Sait Birlik, S. Biryukov, D. Boneya, Lemma Bulto, H. Carabin, Daniel Casey, C. Castañeda-Orjuela, Ferrán Catalá-López, Honglei Chen, Abdulaal Chitheer, Rajiv Chowdhury, H. Christensen, L. Dandona, R. Dandona, Gabrielle Veber, S. Dharmaratne, H. Do, K. Dokova, E. Dorsey, R. Ellenbogen, S. Eskandarieh, M. Farvid, S. Fereshtehnejad, F. Fischer, Kyle Foreman, J. Geleijnse, R. Gillum, G. Giussani, Ellen Goldberg, P. Gona, A. Goulart, H. Gugnani, Rahul Gupta, V. Hachinski, R. Gupta, R. Hamadeh, M. Hambisa, Graeme Hankey, H. Hareri, Rasmus Havmoeller, S. Hay, P. Heydarpour, P. Hotez, Mihajlo Jakovljevic, Mehdi Javanbakht, P. Jeemon, J. Jonas, Y. Kalkonde, Amit Kandel, A. Karch, A. Kasaeian, A. Kastor, P. Keiyoro, Yousef Khader, I. Khalil, E. Khan, Y. Khang, Abdullah Tawfih, Abdullah Khoja, J. Khubchandani, Chanda Kulkarni, Daniel Kim, Y. Kim, M. Kivimaki, Yoshihiro Kokubo, S. Kosen, M. Kravchenko, R. Krishnamurthi, B. Defo, G. Kumar, Rashmi Kumar, H. Kyu, Anders Larsson, P. Lavados, Yongmei Li, Xiaofeng Liang, Misgan Liben, Warren Lo, G. Logroscino, P. Lotufo, Clement Loy, Mark Mackay, Hassan Razek, Mohammed Razek, Azeem Majeed, R. Malekzadeh, Treh Manhertz, L. Mantovani, J. Massano, Mohsen Mazidi, C. McAlinden, S. Mehata, M. Mehndiratta, Ziad Memish, W. Mendoza, Mubarek Mengistie, G. Mensah, A. Meretoja, H. Mezgebe, Ted Miller, Shiva Mishra, Norlinah Ibrahim, Alireza Mohammadi, K. Mohammed, S. Mohammed, A. Mokdad, M. Moradi-Lakeh, I. Velásquez, K. Musa, M. Naghavi, J. Ngunjiri, Cuong Nguyen, Grant Nguyen, Q. Nguyen, T. Nguyen, E. Nichols, D. Ningrum, V. Nong, B. Norrving, J. Noubiap, F. Ogbo, M. Owolabi, J. Pandian, P. Parmar, David Pereira, M. Petzold, Michael Phillips, M. Piradov, Richie Poulton, F. Pourmalek, M. Qorbani, Anwar Rafay, Mahfuzar Rahman, Mohammad Rahman, R. Rai, S. Rajšić, Annemarei Ranta, S. Rawaf, Andre Renzaho, M. Rezai, Gregory Roth, G. Roshandel, Enrico Rubagotti, Perminder Sachdev, Saeid Safiri, R. Sahathevan, M. Sahraian, A. Samy, Paula Santalucia, I. Santos, B. Sartorius, Maheswar Satpathy, M. Sawhney, Mete Saylan, S. Sepanlou, M. Shaikh, Raad Shakir, M. Shamsizadeh, Kevin Sheth, M. Shigematsu, H. Shoman, D. Silva, Mari Smith, E. Sobngwi, L. Sposato, J. Stanaway, Dan Stein, T. Steiner, L. Stovner, R. Abdulkader, Cassandra Szoeke, R. Tabarés-Seisdedos, D. Tanné, A. Theadom, A. Thrift, D. Tirschwell, R. Topor-Madry, Bach Tran, T. Truelsen, K. Tuem, K. Ukwaja, O. Uthman, Yuri Varakin, T. Vasankari, N. Venketasubramanian, V. Vlassov, F. Wadilo, Tolassa Wakayo, Mitchell Wallin, E. Weiderpass, R. Westerman, T. Wijeratne, C. Wiysonge, M. Woldu, Charles Wolfe, Denis Xavier, Gelin Xu, Yuichiro Yano, H. Yimam, N. Yonemoto, Chuanhua Yu, Z. Zaidi, M. Zaki, J. Zunt, C. Murray, T. Vos (2017)
Global, regional, and national burden of neurological disorders during 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015The Lancet. Neurology, 16
J. Cancela, Mario Pansera, M. Arredondo, Juan Estrada, M. Pastorino, Laura Pastor-Sanz, J. Villalar (2010)
A comprehensive motor symptom monitoring and management system: The bradykinesia case2010 Annual International Conference of the IEEE Engineering in Medicine and Biology
S. Fahn (1987)
Unified Parkinson's Disease Rating Scale, 2
M. Hoehn, M. Yahr (1967)
Parkinsonism: Onset, progression, and mortalityNeurology, 77
A. Salarian, H. Russmann, C. Wider, P. Burkhard, F. Vingerhoets, K. Aminian (2007)
Quantification of Tremor and Bradykinesia in Parkinson's Disease Using a Novel Ambulatory Monitoring SystemIEEE Transactions on Biomedical Engineering, 54
Hoehn Mm, Yahr (1998)
Parkinsonism: onset, progression, and mortality. 1967.Neurology, 57 10 Suppl 3
R. Monie, A. Hunter, K. Rocchiccioli, J. White, I. Campbell, G. Kilpatrick, S. Glamorgan, Cf Lxx, David Davies
Occasional Review
Daphne Zwartjes, T. Heida, J. Vugt, J. Geelen, P. Veltink (2010)
Ambulatory Monitoring of Activities and Motor Symptoms in Parkinson's DiseaseIEEE Transactions on Biomedical Engineering, 57
François Chollet (2015)
KerasRetrieved from https://github.com/fchollet/keras/.
Parkinson’s disease is a progressive nervous system disorder afflicting millions of patients. Among its motor symptoms, bradykinesia is one of the cardinal manifestations. Experienced doctors are required for the clinical diagnosis of bradykinesia, but sometimes they also miss subtle changes, especially in early stages of such disease. Therefore, developing auxiliary diagnostic methods that can automatically detect bradykinesia has received more and more attention. In this article, we employ a two-stage framework for bradykinesia recognition based on the video of patient movement. First, convolution neural networks are trained to localize keypoints in each video frame. These time-varying coordinates form motion trajectories that represent the whole movement. From the trajectory, we then propose novel measurements, namely stability, completeness, and self-similarity, to quantify different motor behaviors. We also propose a periodic motion model called PMNet. An encoder--decoder structure is applied to learn a low dimensional representation of a motion process. The compressed motion process and quantified motor behaviors are combined as inputs to a fully-connected neural network. Different from the traditional means, our solution extends the application scenario outside the hospital and can be easily transplanted to conduct similar tasks. A commonly used clinical assessment is served as a case study. Experimental results based on real-world data validate the effectiveness of our approach for bradykinesia recognition.
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Feb 10, 2020
Keywords: Bradykinesia
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