{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T01:29:44Z","timestamp":1769909384315,"version":"3.49.0"},"reference-count":64,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is a neurodegenerative disorder characterized by motor and nonmotor impairment with various implications on patients\u2019 quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical, or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HCs) was utilized. The data were preprocessed to extract relevant time-, frequency-, and energy-related features, and a bidirectional long short-term memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using fivefold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HCs. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms.<\/jats:p>","DOI":"10.3390\/s23187850","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T05:31:28Z","timestamp":1694583088000},"page":"7850","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Detecting Minor Symptoms of Parkinson\u2019s Disease in the Wild Using Bi-LSTM with Attention Mechanism"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3279-8016","authenticated-orcid":false,"given":"Vasileios","family":"Skaramagkas","sequence":"first","affiliation":[{"name":"Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), GR-700 13 Heraklion, Greece"},{"name":"Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR-710 04 Heraklion, Greece"}]},{"given":"Iro","family":"Boura","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Crete, GR-710 03 Heraklion, Greece"},{"name":"Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King\u2019s College London, London WC2R 2LS, UK"}]},{"given":"Cleanthi","family":"Spanaki","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Crete, GR-710 03 Heraklion, Greece"},{"name":"Department of Neurology, University Hospital of Heraklion, GR-715 00 Heraklion, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7245-0882","authenticated-orcid":false,"given":"Emilia","family":"Michou","sequence":"additional","affiliation":[{"name":"School of Health Rehabilitation Sciences, Department of Speech and Language Therapy, University of Patras, GR-265 04 Patras, Greece"}]},{"given":"Georgios","family":"Karamanis","sequence":"additional","affiliation":[{"name":"Department of Neurology, Patras University Hospital, GR-264 04 Patras, Greece"}]},{"given":"Zinovia","family":"Kefalopoulou","sequence":"additional","affiliation":[{"name":"Department of Neurology, Patras University Hospital, GR-264 04 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8454-1450","authenticated-orcid":false,"given":"Manolis","family":"Tsiknakis","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), GR-700 13 Heraklion, Greece"},{"name":"Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR-710 04 Heraklion, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"ref_1","unstructured":"(2023). Parkinson Disease, World Health Organization. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/parkinson-disease."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1002\/mds.27063","article-title":"Projection of the prevalence of Parkinson\u2019s disease in the coming decades: Revisited","volume":"33","author":"Rossi","year":"2018","journal-title":"Mov. Disord. Off. J. Mov. Disord. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"42","DOI":"10.14802\/jmd.20079","article-title":"Health-related quality of life for parkinson\u2019s disease patients and their caregivers","volume":"14","author":"Lubomski","year":"2021","journal-title":"J. Mov. Disord."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1002\/mds.25664","article-title":"Health-related quality of life in early Parkinson\u2019s disease: The impact of nonmotor symptoms","volume":"29","author":"Duncan","year":"2014","journal-title":"Mov. Disord. Off. J. Mov. Disord. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/S1474-4422(18)30295-3","article-title":"Global, regional, and national burden of Parkinson\u2019s disease, 1990\u20132016: A systematic analysis for the Global Burden of Disease Study 2016","volume":"17","author":"Dorsey","year":"2018","journal-title":"Lancet Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1016\/bs.irn.2017.05.015","article-title":"Personalized medicine and nonmotor symptoms in parkinson\u2019s disease","volume":"134","author":"Titova","year":"2017","journal-title":"Int. Rev. Neurobiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1001\/jama.2019.22360","article-title":"Diagnosis and treatment of parkinson disease: A review","volume":"323","author":"Armstrong","year":"2020","journal-title":"JAMA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s10020-021-00279-2","article-title":"Novel targeted therapies for Parkinson\u2019s disease","volume":"27","author":"Ntetsika","year":"2021","journal-title":"Mol. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1146\/annurev-pathmechdis-031521-034145","article-title":"Genetics and pathogenesis of parkinson\u2019s syndrome","volume":"18","author":"Ye","year":"2023","journal-title":"Annu. Rev. Pathol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"612","DOI":"10.3389\/fnins.2018.00612","article-title":"Parkinson\u2019s disease: Biomarkers, treatment, and risk factors","volume":"12","author":"Emamzadeh","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Straccia, G., Colucci, F., Eleopra, R., and Cilia, R. (2022). Precision medicine in parkinson\u2019s disease: From genetic risk signals to personalized therapy. Brain Sci., 12.","DOI":"10.3390\/brainsci12101308"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1136\/jnnp-2015-311890","article-title":"The prediagnostic phase of Parkinson\u2019s disease","volume":"87","author":"Noyce","year":"2016","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","article-title":"Parkinson\u2019s disease","volume":"397","author":"Bloem","year":"2021","journal-title":"Lancet"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1002\/mds.26424","article-title":"MDS clinical diagnostic criteria for Parkinson\u2019s disease: MDS-PD Clinical Diagnostic Criteria","volume":"30","author":"Postuma","year":"2015","journal-title":"Mov. Disord."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"S41","DOI":"10.1016\/j.parkreldis.2015.09.027","article-title":"Non motor subtypes and Parkinson\u2019s disease","volume":"22","author":"Sauerbier","year":"2016","journal-title":"Park. Relat. Disord."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","article-title":"Challenges in the diagnosis of Parkinson\u2019s disease","volume":"20","author":"Tolosa","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1093\/brain\/awx376","article-title":"Progression of tremor in early stages of Parkinson\u2019s disease: A clinical and neuroimaging study","volume":"141","author":"Pasquini","year":"2018","journal-title":"Brain J. Neurol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e1095","DOI":"10.1212\/WNL.0000000000005215","article-title":"The nature of postural tremor in Parkinson disease","volume":"90","author":"Dirkx","year":"2018","journal-title":"Neurology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1001\/archneur.63.3.noc50312","article-title":"Subjective complaints precede Parkinson disease: The rotterdam study","volume":"63","author":"Koudstaal","year":"2006","journal-title":"Arch. Neurol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1212\/WNL.0b013e318227042d","article-title":"Phenotype in parkinsonian and nonparkinsonian LRRK2 G2019S mutation carriers","volume":"77","author":"Marras","year":"2011","journal-title":"Neurology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1109\/TNSRE.2023.3277749","article-title":"Multi-modal deep learning diagnosis of parkinson\u2019s disease\u2014A systematic review","volume":"31","author":"Skaramagkas","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1038\/s41591-022-01932-x","article-title":"Artificial intelligence-enabled detection and assessment of Parkinson\u2019s disease using nocturnal breathing signals","volume":"28","author":"Yang","year":"2022","journal-title":"Nat. Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tsakanikas, V., Ntanis, A., Rigas, G., Androutsos, C., Boucharas, D., Tachos, N., Skaramagkas, V., Chatzaki, C., Kefalopoulou, Z., and Tsiknakis, M. (2023). Evaluating gait impairment in parkinson\u2019s disease from instrumented insole and imu sensor data. Sensors, 23.","DOI":"10.3390\/s23083902"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chatzaki, C., Skaramagkas, V., Tachos, N., Christodoulakis, G., Maniadi, E., Kefalopoulou, Z., Fotiadis, D.I., and Tsiknakis, M. (2021). The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson\u2019s Patients. Sensors, 21.","DOI":"10.3390\/s21082821"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Skaramagkas, V., Andrikopoulos, G., Kefalopoulou, Z., and Polychronopoulos, P. (2020, January 16\u201319). Towards differential diagnosis of essential and parkinson\u2019s tremor via machine learning. Proceedings of the 2020 28th Mediterranean Conference on Control and Automation, MED 2020, Saint-Rapha\u00ebl, France.","DOI":"10.1109\/MED48518.2020.9182922"},{"key":"ref_26","unstructured":"Goschenhofer,, J., Pfister, F.M., Yuksel, K.A., Bischl, B., Fietzek, U., and Thomas, J. (2019). Lecture Notes in Computer Science, Springer. Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics."},{"key":"ref_27","first-page":"S193","article-title":"Pressure Sensor Insole Gait Assessment for Parkinson\u2019s Disease patients: A pilot study","volume":"Volume 37","author":"Kefalopoulou","year":"2022","journal-title":"Movement Disorders"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chatzaki, C., Skaramagkas, V., Kefalopoulou, Z., Tachos, N., Kostikis, N., Kanellos, F., Triantafyllou, E., Chroni, E., Fotiadis, D.I., and Tsiknakis, M. (2022). Can Gait Features Help in Differentiating Parkinson\u2019s Disease Medication States and Severity Levels? A Machine Learning Approach. Sensors, 22.","DOI":"10.3390\/s22249937"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"201","DOI":"10.3390\/signals2020016","article-title":"A Study on the Essential and Parkinson\u2019s Arm Tremor Classification","volume":"2","author":"Skaramagkas","year":"2021","journal-title":"Signals"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102179","DOI":"10.1016\/j.media.2021.102179","article-title":"Quantifying Parkinson\u2019s disease motor severity under uncertainty using MDS-UPDRS videos","volume":"73","author":"Lu","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tsakanikas, V.D., Dimopoulos, D.G., Tachos, N.S., Chatzaki, C., Skaramagkas, V., Christodoulakis, G., Tsiknakis, M., and Fotiadis, D.I. (2021, January 1\u20135). Gait and balance patterns related to Free-Walking and TUG tests in Parkinson\u2019s Disease based on plantar pressure data. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual.","DOI":"10.1109\/EMBC46164.2021.9629637"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3109\/03091902.2016.1148792","article-title":"Parkinson\u2019s disease hand tremor detection system for mobile application","volume":"40","author":"Fraiwan","year":"2016","journal-title":"J. Med. Eng. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Castrill\u00f3n, R., Acien, A., Orozco-Arroyave, J.R., Morales, A., Vargas-Bonilla, J.F., Vera-Rodr\u00edguez, R., Fierrez, J., Ortega-Garcia, J., and Villegas, A. (2019, January 14\u201318). Characterization of the Handwriting Skills as a Biomarker for Parkinson\u2019s Disease. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France.","DOI":"10.1109\/FG.2019.8756508"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bazgir, O., Frounchi, J., Habibi, S.A.H., Palma, L., and Pierleoni, P. (2015, January 25\u201327). A neural network system for diagnosis and assessment of tremor in Parkinson disease patients. Proceedings of the 2015 22nd Iranian Conference on Biomedical Engineering, ICBME, Tehran, Iran.","DOI":"10.1109\/ICBME.2015.7404105"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wu, H., Zhang, Y., Wu, X., and Yang, F. (2020, January 19\u201320). Assessment of Upper Limb Tremors in Patients with Parkinson\u2019s Disease Based on Displacement and Acceleration Information. Proceedings of the 5th International Conference on Automation, Control and Robotics Engineering, CACRE 2020, Dalian, China.","DOI":"10.1109\/CACRE50138.2020.9230024"},{"key":"ref_37","unstructured":"Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., Tsakou, G., Auss\u00f3, S., Alberich, L.C., Marias, K., and Tsiknakis, M. (2021). Future-ai: Guiding principles and consensus recommendations for trustworthy artificial intelligence in medical imaging. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"701632","DOI":"10.3389\/fnins.2022.701632","article-title":"Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms","volume":"16","author":"Xing","year":"2022","journal-title":"Front. Neurosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7002504","DOI":"10.1109\/LSENS.2021.3074958","article-title":"CNN-Based PD Hand Tremor Detection Using Inertial Sensors","volume":"5","author":"Tong","year":"2021","journal-title":"IEEE Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sun, M., Watson, A., Blackwell, G., Jung, W., Wang, S., Koltermann, K., Helm, N., Zhou, G., Cloud, L., and Pretzer-Aboff, I. (2021, January 16\u201317). TremorSense: Tremor Detection for Parkinson\u2019s Disease Using Convolutional Neural Network. Proceedings of the 2021 IEEE\/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2021, Washington, DC, USA.","DOI":"10.1109\/CHASE52844.2021.00009"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11517-020-02303-9","article-title":"On the use of histograms of oriented gradients for tremor detection from sinusoidal and spiral handwritten drawings of people with Parkinson\u2019s disease","volume":"59","author":"Folador","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.compbiomed.2018.02.007","article-title":"Wrist sensor-based tremor severity quantification in Parkinson\u2019s disease using convolutional neural network","volume":"95","author":"Kim","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3389\/fdata.2020.00004","article-title":"AI in healthcare: Time-series forecasting using statistical, neural, and ensemble architectures","volume":"3","author":"Kaushik","year":"2020","journal-title":"Front. Big Data"},{"key":"ref_44","first-page":"170","article-title":"The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases","volume":"7","author":"Absar","year":"2021","journal-title":"Infect. Dis. Model."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Thummikarat, H., and Chongstitvatana, P. (2021, January 19\u201322). An implementation of machine learning for parkinson\u2019s disease diagnosis. Proceedings of the ECTI-CON 2021\u20142021 18th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology: Smart Electrical System and Technology, Virtual.","DOI":"10.1109\/ECTI-CON51831.2021.9454784"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"A V, A.S., Lones, M.A., Smith, S.L., and Vallejo, M. (2021, January 1\u20135). Evaluation of Recurrent Neural Network Models for Parkinson\u2019s Disease Classification Using Drawing Data. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual.","DOI":"10.1109\/EMBC46164.2021.9630106"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hssayeni, M.D., Jimenez-Shahed, J., Burack, M.A., and Ghoraani, B. (2019). Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements. Sensors, 19.","DOI":"10.3390\/s19194215"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hathaliya, J.J., Modi, H., Gupta, R., and Tanwar, S. (2022, January 2\u20135). Deep learning and Blockchain-based Essential and Parkinson Tremor Classification Scheme. Proceedings of the IEEE INFOCOM 2022\u2014IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Virtual.","DOI":"10.1109\/INFOCOMWKSHPS54753.2022.9798053"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"9630","DOI":"10.1038\/s41598-021-88919-9","article-title":"A deep explainable artificial intelligent framework for neurological disorders discrimination","volume":"11","author":"Shahtalebi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Taleb, C., Likforman-Sulem, L., Mokbel, C., and Khachab, M. (2020). Detection of Parkinson\u2019s disease from handwriting using deep learning: A comparative study. Evol. Intell., 1\u201312.","DOI":"10.1007\/s12065-020-00470-0"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Taleb, C., Khachab, M., Mokbel, C., and Likforman-Sulem, L. (2017, January 3\u20135). Feature selection for an improved Parkinson\u2019s disease identification based on handwriting. Proceedings of the 1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017, Nancy, France.","DOI":"10.1109\/ASAR.2017.8067759"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2559","DOI":"10.1109\/JBHI.2019.2961748","article-title":"Detecting parkinsonian tremor from imu data collected in-the-wild using deep multiple-instance learning","volume":"24","author":"Papadopoulos","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4021","DOI":"10.1038\/s41598-022-07957-z","article-title":"Motion characteristics of subclinical tremors in Parkinson\u2019s disease and normal subjects","volume":"12","author":"Chan","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_54","unstructured":"Oppenheim, A., and Schafer, R. (2013). Discrete-Time Signal Processing: Pearson New International Edition, Pearson Education Limited. [3rd ed.]."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yamansava\u015f\u00e7\u0131lar, B., and G\u00fcvensan, M.A. (2016, January 14\u201318). Activity recognition on smartphones: Efficient sampling rates and window sizes. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Sydney, Australia.","DOI":"10.1109\/PERCOMW.2016.7457154"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Djordjevic, I. (2012). Quantum Information Processing and Quantum Error Correction, Academic Press.","DOI":"10.1016\/B978-0-12-385491-9.00007-1"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Akandeh, A., and Salem, F.M. (2019, January 4\u20137). Slim lstm networks: Lstm_6 and lstm_c6. Proceedings of the 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), Dallas, TX, USA.","DOI":"10.1109\/MWSCAS.2019.8884912"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Namin, A.S. (2019, January 9\u201312). The performance of lstm and bilstm in forecasting time series. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Duch, W., Kacprzyk, J., Oja, E., and Zadro\u017cny, S. (2005, January 11\u201315). Bidirectional lstm networks for improved phoneme classification and recognition. Proceedings of the Artificial Neural Networks: Formal Models and Their Applications\u2014ICANN, Warsaw, Poland. Lecture Notes in Computer Science.","DOI":"10.1007\/11550907"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.jspi.2018.07.005","article-title":"Optimality of training\/test size and resampling effectiveness in cross-validation","volume":"199","author":"Afendras","year":"2019","journal-title":"J. Stat. Plan. Inference"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"21370","DOI":"10.1038\/s41598-020-78418-8","article-title":"Unobtrusive detection of Parkinson\u2019s disease from multi-modal and in-the-wild sensor data using deep learning techniques","volume":"10","author":"Papadopoulos","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Habets, J.G.V., Herff, C., Kubben, P.L., Kuijf, M.L., Temel, Y., Evers, L.J.W., Bloem, B.R., Starr, P.A., Gilron, R., and Little, S. (2021). Rapid dynamic naturalistic monitoring of bradykinesia in parkinson\u2019s disease using a wrist-worn accelerometer. Sensors, 21.","DOI":"10.1101\/2021.09.03.458142"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41746-019-0217-7","article-title":"Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device","volume":"3","author":"Mahadevan","year":"2020","journal-title":"npj Digit. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1136\/pgmj.2005.032979","article-title":"Differential diagnosis of common tremor syndromes","volume":"81","author":"Bhidayasiri","year":"2005","journal-title":"Postgrad. Med. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7850\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:50:04Z","timestamp":1760129404000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7850"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,13]]},"references-count":64,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187850"],"URL":"https:\/\/doi.org\/10.3390\/s23187850","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,13]]}}}