{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:37:25Z","timestamp":1790102245568,"version":"4.0.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T00:00:00Z","timestamp":1581292800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T00:00:00Z","timestamp":1581292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2016-049988"],"award-info":[{"award-number":["RGPIN-2016-049988"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including Parkinson\u2019s Disease (PD) and Essential Tremor (ET). Pathological Hand Tremor (PHT), which is considered among the most common motor symptoms of such disorders, can severely affect patients\u2019 independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation\/prediction of nonstationary PHT is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.<\/jats:p>","DOI":"10.1038\/s41598-020-58912-9","type":"journal-article","created":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T11:02:57Z","timestamp":1581332577000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models"],"prefix":"10.1038","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7174-3253","authenticated-orcid":false,"given":"Soroosh","family":"Shahtalebi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Farokh","family":"Atashzar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivia","family":"Samotus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajni V.","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mandar S.","family":"Jog","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1972-7923","authenticated-orcid":false,"given":"Arash","family":"Mohammadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,2,10]]},"reference":[{"key":"58912_CR1","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.bspc.2016.08.003","volume":"31","author":"C Kotsavasiloglou","year":"2017","unstructured":"Kotsavasiloglou, C., Kostikis, N., Hristu-Varsakelis, D. & Arnaoutoglou, M. Machine learning-based classification of simple drawing movements in parkinson\u2019s disease. Biomed. Signal Process. Control. 31, 174\u2013180 (2017).","journal-title":"Biomed. Signal Process. Control."},{"key":"58912_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.bspc.2014.09.006","volume":"16","author":"C Camara","year":"2015","unstructured":"Camara, C. et al. Resting tremor classification and detection in parkinson\u2019s disease patients. Biomed. Signal Process. Control. 16, 88\u201397 (2015).","journal-title":"Biomed. Signal Process. Control."},{"key":"58912_CR3","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1109\/JBHI.2015.2503802","volume":"21","author":"EJ Smits","year":"2017","unstructured":"Smits, E. J. et al. Graphical tasks to measure upper limb function in patients with parkinson\u2019s disease: Validity and response to dopaminergic medication. IEEE J. Biomed. Heal. Informatics 21, 283\u2013289 (2017).","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"key":"58912_CR4","doi-asserted-by":"publisher","first-page":"2190","DOI":"10.1038\/s41598-017-02122-3","volume":"7","author":"JI Serrano","year":"2017","unstructured":"Serrano, J. I. et al. A data mining approach using cortical thickness for diagnosis and characterization of essential tremor. Sci. Reports 7, 2190, https:\/\/doi.org\/10.1038\/s41598-017-02122-3 (2017).","journal-title":"Sci. Reports"},{"key":"58912_CR5","unstructured":"Nations, U. World population prospects, the 2017 Revision, Volume I: comprehensive tables (Department of Economics and Social Affairs, Population Devision, 2017)."},{"key":"58912_CR6","doi-asserted-by":"crossref","unstructured":"Pahwa, R. & Lyons, K. E. Handbook of Essential Tremor and Other Tremor Disorders (CRC Press, 2005).","DOI":"10.1201\/b14115"},{"key":"58912_CR7","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1109\/TNSRE.2016.2535358","volume":"24","author":"C Liu","year":"2016","unstructured":"Liu, C. et al. Closed-loop control of tremor-predominant parkinsonian state based on parameter estimation. IEEE Transactions on Neural Syst. Rehabil. Eng. 24, 1109\u20131121 (2016).","journal-title":"IEEE Transactions on Neural Syst. Rehabil. Eng."},{"key":"58912_CR8","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TCYB.2014.2381495","volume":"45","author":"S Tatinati","year":"2015","unstructured":"Tatinati, S., Veluvolu, K. C. & Ang, W. T. Multistep prediction of physiological tremor based on machine learning for robotics assisted microsurgery. IEEE Transactions on Cybern. 45, 328\u2013339 (2015).","journal-title":"IEEE Transactions on Cybern."},{"key":"58912_CR9","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1109\/TRA.2003.817506","volume":"19","author":"CN Riviere","year":"2003","unstructured":"Riviere, C. N., Ang, W. T. & Khosla, P. K. Toward active tremor canceling in handheld microsurgical instruments. IEEE Transactions on Robotics Autom. 19, 793\u2013800 (2003).","journal-title":"IEEE Transactions on Robotics Autom."},{"key":"58912_CR10","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.bspc.2013.10.004","volume":"10","author":"S Wang","year":"2014","unstructured":"Wang, S., Gao, Y., Zhao, J. & Cai, H. Adaptive sliding bandlimited multiple fourier linear combiner for estimation of pathological tremor. Biomed. Signal Process. Control. 10, 260\u2013274 (2014).","journal-title":"Biomed. Signal Process. Control."},{"key":"58912_CR11","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1136\/pgmj.2005.032979","volume":"81","author":"R Bhidayasiri","year":"2005","unstructured":"Bhidayasiri, R. Differential diagnosis of common tremor syndromes. Postgrad. Med. J. 81, 756\u2013762 (2005).","journal-title":"Postgrad. Med. J."},{"key":"58912_CR12","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1002\/mus.1063","volume":"24","author":"G Deuschl","year":"2001","unstructured":"Deuschl, G., Raethjen, J., Lindemann, M. & Krack, P. The pathophysiology of tremor. Muscle & nerve 24, 716\u2013735 (2001).","journal-title":"Muscle & nerve"},{"key":"58912_CR13","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/JSTSP.2016.2530632","volume":"10","author":"SF Atashzar","year":"2016","unstructured":"Atashzar, S. F. et al. Characterization of upper-limb pathological tremors: Application to design of an augmented haptic rehabilitation system. IEEE J. Sel. Top. Signal Process. 10, 888\u2013903, https:\/\/doi.org\/10.1109\/JSTSP.2016.2530632 (2016).","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"58912_CR14","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.bspc.2018.10.007","volume":"48","author":"S Shahtalebi","year":"2019","unstructured":"Shahtalebi, S., Atashzar, S. F., Patel, R. V. & Mohammadi, A. Wake: Wavelet decomposition coupled with adaptive kalman filtering for pathological tremor extraction. Biomed. Signal Process. Control. 48, 179\u2013188 (2019).","journal-title":"Biomed. Signal Process. Control."},{"key":"58912_CR15","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1002\/mds.870131303","volume":"13","author":"G Deuschl","year":"1998","unstructured":"Deuschl, G., Bain, P. & Brin, M. & Committee, A. H. S. Consensus statement of the movement disorder society on tremor. Mov. Disord. 13, 2\u201323 (1998).","journal-title":"Mov. Disord."},{"key":"58912_CR16","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1097\/00004691-199603000-00002","volume":"13","author":"G Deuschl","year":"1996","unstructured":"Deuschl, G., Krack, P., Lauk, M. & Timmer, J. Clinical neurophysiology of tremor. J. Clin. Neurophysiol. 13, 110\u2013121 (1996).","journal-title":"J. Clin. Neurophysiol."},{"key":"58912_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7717\/peerj.5308","volume":"6","author":"B Molparia","year":"2018","unstructured":"Molparia, B. et al. Combined accelerometer and genetic analysis to differentiate essential tremor from parkinson\u2019s disease. PeerJ 6, 1\u201317 (2018).","journal-title":"PeerJ"},{"key":"58912_CR18","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1109\/TMECH.2014.2317948","volume":"20","author":"B Taheri","year":"2015","unstructured":"Taheri, B., Case, D. & Richer, E. Adaptive suppression of severe pathological tremor by torque estimation method. IEEE\/ASME Transactions on Mechatronics 20, 717\u2013727 (2015).","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"key":"58912_CR19","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/BF02368521","volume":"20","author":"A Prochazka","year":"1992","unstructured":"Prochazka, A., Elek, J. & Javidan, M. Attenuation of pathological tremors by functional electrical stimulation i: Method. Annals Biomed. Eng. 20, 205\u2013224 (1992).","journal-title":"Annals Biomed. Eng."},{"key":"58912_CR20","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1109\/86.788474","volume":"7","author":"DM Gillard","year":"1999","unstructured":"Gillard, D. M., Cameron, T., Prochazka, A. & Gauthier, M. J. Tremor suppression using functional electrical stimulation: A comparison between digital and analog controllers. IEEE transactions on Rehabil. Eng. 7, 385\u2013388 (1999).","journal-title":"IEEE transactions on Rehabil. Eng."},{"key":"58912_CR21","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1007\/s11517-011-0803-6","volume":"49","author":"LP Maneski","year":"2011","unstructured":"Maneski, L. P. et al. Electrical stimulation for the suppression of pathological tremor. Med. & biological engineering & computing 49, 1187 (2011).","journal-title":"Med. & biological engineering & computing"},{"key":"58912_CR22","first-page":"CMT\u2013S2999","volume":"2","author":"G Grimaldi","year":"2010","unstructured":"Grimaldi, G. & Manto, M. \u201cold\u201d and emerging therapies of human tremor. Clin. Med. Insi.: Ther. 2, CMT\u2013S2999 (2010).","journal-title":"Clin. Med. Insi.: Ther."},{"key":"58912_CR23","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/S1474-4422(10)70322-7","volume":"10","author":"G Deuschl","year":"2011","unstructured":"Deuschl, G., Raethjen, J., Hellriegel, H. & Elble, R. Treatment of patients with essential tremor. The Lancet Neurol. 10, 148\u2013161 (2011).","journal-title":"The Lancet Neurol."},{"key":"58912_CR24","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, L. & Wang, X. Tremor detection using smartphone-based acoustic sensing. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, 309\u2013312 (ACM, 2017).","DOI":"10.1145\/3123024.3123168"},{"key":"58912_CR25","doi-asserted-by":"crossref","unstructured":"Zeng, W. et al. An optimized method for the limb tremor measurement. In Proceedings of the 2nd International Conference on Medical and Health Informatics, 65\u201368 (ACM, 2018).","DOI":"10.1145\/3239438.3239478"},{"key":"58912_CR26","doi-asserted-by":"crossref","unstructured":"Shah, S. A., Tinkhauser, G., Chen, C. C., Little, S. & Brown, P. Parkinsonian tremor detection from subthalamic nucleus local field potentials for closed-loop deep brain stimulation. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2320\u20132324 (IEEE, 2018).","DOI":"10.1109\/EMBC.2018.8512741"},{"key":"58912_CR27","first-page":"247","volume":"9","author":"D Impedovo","year":"2018","unstructured":"Impedovo, D., Pirlo, G. & Vessio, G. Dynamic handwriting analysis for supporting earlier parkinson\u2019s disease diagnosis. Inf. 9, 247 (2018).","journal-title":"Inf."},{"key":"58912_CR28","doi-asserted-by":"crossref","unstructured":"Lang, M. et al. A multi-layer gaussian process for motor symptom estimation in people with parkinson\u2019s disease. IEEE Transactions on Biomed. Eng (2019).","DOI":"10.1109\/TBME.2019.2900002"},{"key":"58912_CR29","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.eswa.2018.06.003","volume":"110","author":"L Parisi","year":"2018","unstructured":"Parisi, L., RaviChandran, N. & Manaog, M. L. Feature-driven machine learning to improve early diagnosis of parkinson\u2019s disease. Expert. Syst. with Appl. 110, 182\u2013190 (2018).","journal-title":"Expert. Syst. with Appl."},{"key":"58912_CR30","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.bspc.2018.08.029","volume":"49","author":"S Lahmiri","year":"2019","unstructured":"Lahmiri, S. & Shmuel, A. Detection of parkinson\u2019s disease based on voice patterns ranking and optimized support vector machine. Biomed. Signal Process. Control. 49, 427\u2013433 (2019).","journal-title":"Biomed. Signal Process. Control."},{"key":"58912_CR31","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.asoc.2018.10.022","volume":"74","author":"CO Sakar","year":"2019","unstructured":"Sakar, C. O. et al. A comparative analysis of speech signal processing algorithms for parkinson\u2019s disease classification and the use of the tunable q-factor wavelet transform. Appl. Soft Comput. 74, 255\u2013263 (2019).","journal-title":"Appl. Soft Comput."},{"key":"58912_CR32","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1016\/j.procs.2018.05.154","volume":"132","author":"S Grover","year":"2018","unstructured":"Grover, S. et al. Predicting severity of parkinson\u2019s disease using deep learning. Procedia Comput. Sci. 132, 1788\u20131794 (2018).","journal-title":"Procedia Comput. Sci."},{"key":"58912_CR33","doi-asserted-by":"crossref","unstructured":"Rastegari, E., Azizian, S. & Ali, H. Machine learning and similarity network approaches to support automatic classification of parkinson\u2019s diseases using accelerometer-based gait analysis. In Proceedings of the 52nd Hawaii International Conference on System Sciences (2019).","DOI":"10.24251\/HICSS.2019.511"},{"key":"58912_CR34","doi-asserted-by":"publisher","first-page":"137","DOI":"10.3390\/e21020137","volume":"21","author":"MD Hssayeni","year":"2019","unstructured":"Hssayeni, M. D., Jimenez-Shahed, J. & Ghoraani, B. Hybrid feature extraction for detection of degree of motor fluctuation severity in parkinson\u2019s disease patients. Entropy 21, 137 (2019).","journal-title":"Entropy"},{"key":"58912_CR35","doi-asserted-by":"crossref","unstructured":"Rahimi, F., Samotus, O., Lee, J. & Jog, M. Effective management of upper limb parkinsonian tremor by incobotulinumtoxina injections using sensor-based biomechanical patterns. Tremor Other Hyperkinetic Movements 5 (2015).","DOI":"10.5334\/tohm.240"},{"key":"58912_CR36","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.jns.2018.02.001","volume":"387","author":"S Memar","year":"2018","unstructured":"Memar, S., Delrobaei, M., Pieterman, M., McIsaac, K. & Jog, M. Quantification of whole-body bradykinesia in parkinson\u2019s disease participants using multiple inertial sensors. J. neurological sciences 387, 157\u2013165 (2018).","journal-title":"J. neurological sciences"},{"key":"58912_CR37","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.jns.2016.07.056","volume":"368","author":"M Delrobaei","year":"2016","unstructured":"Delrobaei, M., Tran, S., Gilmore, G., McIsaac, K. & Jog, M. Characterization of multi-joint upper limb movements in a single task to assess bradykinesia. J. neurological sciences 368, 337\u2013342 (2016).","journal-title":"J. neurological sciences"},{"key":"58912_CR38","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.jns.2017.11.004","volume":"384","author":"M Delrobaei","year":"2018","unstructured":"Delrobaei, M. et al. Towards remote monitoring of parkinson\u2019s disease tremor using wearable motion capture systems. J. neurological sciences 384, 38\u201345 (2018).","journal-title":"J. neurological sciences"},{"key":"58912_CR39","doi-asserted-by":"publisher","first-page":"e0153739","DOI":"10.1371\/journal.pone.0153739","volume":"11","author":"O Samotus","year":"2016","unstructured":"Samotus, O., Rahimi, F., Lee, J. & Jog, M. Functional ability improved in essential tremor by incobotulinumtoxina injections using kinematically determined biomechanical patterns\u2013a new future. PloS one 11, e0153739 (2016).","journal-title":"PloS one"},{"key":"58912_CR40","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1177\/0278364909104065","volume":"28","author":"SG Yuen","year":"2009","unstructured":"Yuen, S. G., Kettler, D. T., Novotny, P. M., Plowes, R. D. & Howe, R. D. Robotic motion compensation for beating heart intracardiac surgery. The Int. J. Robotics Res. 28, 1355\u20131372 (2009).","journal-title":"The Int. J. Robotics Res."},{"key":"58912_CR41","doi-asserted-by":"publisher","first-page":"1705","DOI":"10.1109\/JPROC.2006.880722","volume":"94","author":"CN Riviere","year":"2006","unstructured":"Riviere, C. N., Gangloff, J. & De Mathelin, M. Robotic compensation of biological motion to enhance surgical accuracy. Proc. IEEE 94, 1705\u20131716 (2006).","journal-title":"Proc. IEEE"},{"key":"58912_CR42","doi-asserted-by":"publisher","DOI":"10.1186\/1748-717X-2-1","volume":"2","author":"JRS de Koste","year":"2007","unstructured":"de Koste, J. R. S. et al. Verifying 4d gated radiotherapy using time-integrated electronic portal imaging: a phantom and clinical study. Radiat. Oncol. 2, 1 (2007).","journal-title":"Radiat. Oncol."},{"key":"58912_CR43","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1002\/rcs.408","volume":"7","author":"GP Moustris","year":"2011","unstructured":"Moustris, G. P., Hiridis, S. C., Deliparaschos, K. M. & Konstantinidis, K. M. Evolution of autonomous and semi autonomous robotic surgical systems: a review of the literature. The Int. J. Med. Robotics Comput. Assist. Surg. 7, 375\u2013392 (2011).","journal-title":"The Int. J. Med. Robotics Comput. Assist. Surg."},{"key":"58912_CR44","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1002\/rcs.16","volume":"1","author":"M Tavakoli","year":"2005","unstructured":"Tavakoli, M., Patel, R. & Moallem, M. Haptic interaction in robot-assisted endoscopic surgery: a sensorized end-effector. The Int. J. Med. Robotics Comput. Assist. Surg. 1, 53\u201363 (2005).","journal-title":"The Int. J. Med. Robotics Comput. Assist. Surg."},{"key":"58912_CR45","first-page":"1729","volume":"52","author":"T Ortmaier","year":"2005","unstructured":"Ortmaier, T., Groger, M., Boehm, D. H., Falk, V. & Hirzinger, G. Motion estimation in beating heart surgery. IEEE Transactions on. Biomed. Eng. 52, 1729\u20131740 (2005).","journal-title":"Biomed. Eng."},{"key":"58912_CR46","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1109\/TNSRE.2010.2091283","volume":"19","author":"APL Bo","year":"2011","unstructured":"Bo, A. P. L., Poignet, P. & Geny, C. Pathological tremor and voluntary motion modeling and online estimation for active compensation. IEEE Transactions on Neural Syst. Rehabil. Eng. 19, 177\u2013185 (2011).","journal-title":"IEEE Transactions on Neural Syst. Rehabil. Eng."},{"key":"58912_CR47","doi-asserted-by":"publisher","first-page":"3020","DOI":"10.3390\/s110303020","volume":"11","author":"KC Veluvolu","year":"2011","unstructured":"Veluvolu, K. C. & Ang, W. T. Estimation of physiological tremor from accelerometers for real-time applications. Sensors 11, 3020\u20133036 (2011).","journal-title":"Sensors"},{"key":"58912_CR48","doi-asserted-by":"crossref","unstructured":"Ang, W. T., Riviere, C. N. & Khosla, P. K. An active hand-held instrument for enhanced microsurgical accuracy. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 878\u2013886 (Springer, 2000).","DOI":"10.1007\/978-3-540-40899-4_91"},{"key":"58912_CR49","doi-asserted-by":"publisher","first-page":"1864","DOI":"10.1109\/JSEN.2009.2030980","volume":"9","author":"WT Latt","year":"2009","unstructured":"Latt, W. T., Tan, U.-X., Shee, C. Y., Riviere, C. N. & Ang, W. T. Compact sensing design of a handheld active tremor compensation instrument. IEEE Sensors J. 9, 1864\u20131871 (2009).","journal-title":"IEEE Sensors J."},{"key":"58912_CR50","doi-asserted-by":"publisher","first-page":"3074","DOI":"10.1109\/TBME.2013.2264546","volume":"60","author":"KC Veluvolu","year":"2013","unstructured":"Veluvolu, K. C., Tatinati, S., Hong, S.-M. & Ang, W. T. Multistep prediction of physiological tremor for surgical robotics applications. IEEE Transactions on Biomed. Eng. 60, 3074\u20133082 (2013).","journal-title":"IEEE Transactions on Biomed. Eng."},{"key":"58912_CR51","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1002\/rcs.340","volume":"6","author":"K Veluvolu","year":"2010","unstructured":"Veluvolu, K. & Ang, W. Estimation and filtering of physiological tremor for real-time compensation in surgical robotics applications. The Int. J. Med. Robotics Comput. Assist. Surg. 6, 334\u2013342 (2010).","journal-title":"The Int. J. Med. Robotics Comput. Assist. Surg."},{"key":"58912_CR52","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.bspc.2010.02.005","volume":"5","author":"A Hossen","year":"2010","unstructured":"Hossen, A., Muthuraman, M., Raethjen, J., Deuschl, G. & Heute, U. Discrimination of parkinsonian tremor from essential tremor by implementation of a wavelet-based soft-decision technique on emg and accelerometer signals. Biomed. Signal Process. Control. 5, 181\u2013188 (2010).","journal-title":"Biomed. Signal Process. Control."},{"key":"58912_CR53","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/j.dsp.2011.01.010","volume":"21","author":"L Ai","year":"2011","unstructured":"Ai, L., Wang, J. & Yao, R. Classification of parkinsonian and essential tremor using empirical mode decomposition and support vector machine. Digit. Signal Process. 21, 543\u2013550 (2011).","journal-title":"Digit. Signal Process."},{"key":"58912_CR54","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1134\/S1064226915100113","volume":"60","author":"O Sushkova","year":"2015","unstructured":"Sushkova, O. et al. Time\u2013frequency analysis of simultaneous measurements of electroencephalograms, electromyograms, 41 and mechanical tremor under parkinson disease. J. Commun. Technol. Electron. 60, 1109\u20131116 (2015).","journal-title":"J. Commun. Technol. Electron."},{"key":"58912_CR55","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1002\/mdc3.12379","volume":"4","author":"RJ Elble","year":"2017","unstructured":"Elble, R. J., Hellriegel, H., Raethjen, J. & Deuschl, G. Assessment of head tremor with accelerometers versus gyroscopic transducers. Mov. Disord. Clin. Pract. 4, 205\u2013211 (2017).","journal-title":"Mov. Disord. Clin. Pract."},{"key":"58912_CR56","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/TRO.2016.2623336","volume":"33","author":"SF Atashzar","year":"2016","unstructured":"Atashzar, S. F., Polushin, I. G. & Patel, R. V. A small-gain approach for nonpassive bilateral telerobotic rehabilitation: Stability analysis and controller synthesis. IEEE Transactions on Robotics 33, 49\u201366 (2016).","journal-title":"IEEE Transactions on Robotics"},{"key":"58912_CR57","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1109\/TCST.2016.2594584","volume":"25","author":"SF Atashzar","year":"2016","unstructured":"Atashzar, S. F., Shahbazi, M., Tavakoli, M. & Patel, R. V. A passivity-based approach for stable patient\u2013robot interaction in haptics-enabled rehabilitation systems: modulated time-domain passivity control. IEEE Transactions on Control. Syst. Technol. 25, 991\u20131006 (2016).","journal-title":"IEEE Transactions on Control. Syst. Technol."},{"key":"58912_CR58","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1177\/0278364916689139","volume":"36","author":"SF Atashzar","year":"2017","unstructured":"Atashzar, S. F., Shahbazi, M., Tavakoli, M. & Patel, R. V. A grasp-based passivity signature for haptics-enabled human robot interaction: Application to design of a new safety mechanism for robotic rehabilitation. The Int. J. Robotics Res. 36, 778\u2013799 (2017).","journal-title":"The Int. J. Robotics Res."},{"key":"58912_CR59","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1109\/TOH.2018.2818134","volume":"11","author":"M Shahbazi","year":"2018","unstructured":"Shahbazi, M., Atashzar, S. F. & Patel, R. V. A systematic review of multilateral teleoperation systems. IEEE transactions on haptics 11, 338\u2013356 (2018).","journal-title":"IEEE transactions on haptics"},{"key":"58912_CR60","doi-asserted-by":"crossref","unstructured":"Atashzar, S. F., Shahbazi, M., Tavakoli, M. & Patel, R. V. A new passivity-based control technique for safe patient-robot interaction in haptics-enabled rehabilitation systems. In 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), 4556\u20134561 (IEEE, 2015).","DOI":"10.1109\/IROS.2015.7354025"},{"key":"58912_CR61","unstructured":"Oppenheim, A. V. & Schafer, R. W. Discrete-time signal processing (Pearson Education, 2014)."},{"key":"58912_CR62","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nat. 521, 436 (2015).","journal-title":"Nat."},{"key":"58912_CR63","unstructured":"Goodfellow, I., Courville, A. & Bengio, Y. Deep learning, vol. 1 (MIT press Cambridge, 2016)."},{"key":"58912_CR64","unstructured":"Nair, V. & Hinton, G. E. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807\u2013814 (2010)."},{"key":"58912_CR65","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M. & Paliwal, K. K. Bidirectional recurrent neural networks. IEEE Transactions on Signal Process. 45, 2673\u20132681 (1997).","journal-title":"IEEE Transactions on Signal Process."},{"key":"58912_CR66","doi-asserted-by":"crossref","unstructured":"Cho, K. et al. Learning phrase representations using rnn encoder-decoder for statistical machine translation. Conf. on Empir. Methods Nat. Lang. Process. (EMNLP) 1724\u20131734 (2014).","DOI":"10.3115\/v1\/D14-1179"},{"key":"58912_CR67","unstructured":"Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. NIPS 2014 Work. on Deep. Learn. (2014)."},{"key":"58912_CR68","unstructured":"Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. Int. Conf. on Learn. Represent. (ICLR) (2015)."},{"key":"58912_CR69","first-page":"231","volume":"64","author":"ES Pearson","year":"1977","unstructured":"Pearson, E. S., D\u2019Agostino, R. B. & Bowman, K. O. Tests for departure from normality: Comparison of powers. Biom. 64, 231\u2013246 (1977).","journal-title":"Biom."},{"key":"58912_CR70","doi-asserted-by":"publisher","DOI":"10.1186\/s40035-017-0086-4","volume":"6","author":"W Chen","year":"2017","unstructured":"Chen, W., Hopfner, F., Becktepe, J. S. & Deuschl, G. Rest tremor revisited: Parkinson\u2019s disease and other disorders. Transl. neurodegeneration 6, 16 (2017).","journal-title":"Transl. neurodegeneration"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-58912-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-58912-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-58912-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T06:54:42Z","timestamp":1670309682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-58912-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,10]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["58912"],"URL":"https:\/\/doi.org\/10.1038\/s41598-020-58912-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,10]]},"assertion":[{"value":"9 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2195"}}