Nussbaum RL, Ellis CE. Alzheimer’s disease and Parkinson’s disease. N Engl J Med. 2003;348(14):1356–64.
PubMed
CAS
Google Scholar
Tinazzi M, Gandolfi M, Ceravolo R, Capecci M, Andrenelli E, Ceravolo MG, Bonanni L, Onofrj M, Vitale M, Catalan M, et al. Postural Abnormalities in Parkinson’s Disease: An Epidemiological and Clinical Multicenter Study. Mov Disord Clin Pract. 2019;6(7):576–85.
PubMed
PubMed Central
Google Scholar
Pandey S, Kumar H. Assessment of striatal & postural deformities in patients with Parkinson’s disease. Indian J Med Res. 2016;144(5):682–8.
PubMed
PubMed Central
Google Scholar
Doherty KM, van de Warrenburg BP, Peralta MC, Silveira-Moriyama L, Azulay J-P, Gershanik OS, Bloem BR. Postural deformities in Parkinson’s disease. The Lancet Neurology. 2011;10(6):538–49.
PubMed
Google Scholar
McFarland C, Wang-Price S, Richard S. Clinical measurements of cervical lordosis using flexirule and inclinometer methods in individuals with and without cervical spine dysfunction: A reliability and validity study. J Back Musculoskelet Rehabil. 2015;28(2):295–302.
PubMed
Google Scholar
Tinazzi M, Geroin C, Gandolfi M, Smania N, Tamburin S, Morgante F, Fasano A. Pisa syndrome in Parkinson’s disease: An integrated approach from pathophysiology to management. Movement Disorders. 2016;31(12):1785–95.
PubMed
Google Scholar
Margraf NG, Wolke R, Granert O, Berardelli A, Bloem BR, Djaldetti R, Espay AJ, Fasano A, Furusawa Y, Giladi N, Hallett M. Consensus for the measurement of the camptocormia angle in the standing patient. Parkinsonism Related Disorders. 2018;52:1–5.
PubMed
Google Scholar
Tinazzi M, Gandolfi M, Artusi CA, Lanzafame R, Zanolin E, Ceravolo R, Capecci M, Andrenelli E, Ceravolo MG, Bonanni L, et al. Validity of the wall goniometer as a screening tool to detect postural abnormalities in Parkinson’s disease. Parkinsonism Relat Disord. 2019;69:159–65.
PubMed
Google Scholar
Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stern MB, Dodel R, et al. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Movement Disorders. 2008;23(15):2129–70.
PubMed
Google Scholar
Orcioli-Silva D, Beretta VS. Applicability of the Wall Goniometer in Parkinson’s disease. Parkinsonism Relat Disord. 2019;69:157–8.
PubMed
Google Scholar
Palmerini L, Rocchi L, Mellone S, Valzania F, Chiari L. Feature selection for accelerometer-based posture analysis in Parkinson’s disease. IEEE Trans Inform Technol Biomed. 2011;15(3):481–90.
Google Scholar
Caudron S, Guerraz M, Eusebio A, Gros JP, Azulay JP, Vaugoyeau M. Evaluation of a visual biofeedback on the postural control in Parkinson’s disease. Neurophysiologie Clinique. 2014;44(1):77–86.
PubMed
CAS
Google Scholar
Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI. Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel). 2014;14(9):17235–55.
Google Scholar
Asakawa T, Sugiyama K, Nozaki T, Sameshima T, Kobayashi S, Wang L, Hong Z, Chen S, Li C, Namba H. Can the latest computerized technologies revolutionize conventional assessment tools and therapies for a neurological disease? The example of Parkinson’s disease. Neurol Med Chir (Tokyo). 2019;59(3):69–78.
Google Scholar
Ledger D, Mccaffrey D: Inside wearables: How the science of human behavior change offers the secret to long-Term engagement. 2014.
Espay AJ, Bonato P, Nahab FB, Maetzler W, Dean JM, Klucken J, Eskofier BM, Merola A, Horak F, Lang AE, et al. Technology in Parkinson’s disease: challenges and opportunities. Mov Disord. 2016;31(9):1272–82.
PubMed
PubMed Central
Google Scholar
Han J, Shao L, Xu D, Shotton J. Enhanced computer vision with Microsoft Kinect sensor: a review. IEEE Trans Cybern. 2013;43(5):1318–34.
PubMed
Google Scholar
Okada Y, Shibata T, Tamei T, Orito Y, Funaya H. In-home posture evaluation and visual feedback training to improve posture with a kinect-based system in Parkinson’s disease. J Novel Physiother. 2014;4(5):232.
Google Scholar
Giger ML. Machine learning in medical imaging. J Am Coll Radiol. 2018;15(3 Pt B):512–20.
PubMed
Google Scholar
Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603–19.
PubMed
CAS
Google Scholar
Lee S, Mohr NM, Street WN, Nadkarni P. Machine learning in relation to emergency medicine clinical and operational scenarios: an overview. West J Emerg Med. 2019;20(2):219–27.
PubMed
PubMed Central
Google Scholar
Saber H, Somai M, Rajah GB, Scalzo F, Liebeskind DS. Predictive analytics and machine learning in stroke and neurovascular medicine. Neurol Res. 2019;41(8):681–90.
PubMed
Google Scholar
Badillo S, Banfai B, Birzele F, Davydov II, Hutchinson L, Kam-Thong T, Siebourg-Polster J, Steiert B, Zhang JD. An Introduction to Machine Learning. Clin Pharmacol Ther. 2020;107(4):871–85.
PubMed
PubMed Central
Google Scholar
Dranca L. Using Kinect to classify Parkinson’s disease stages related to severity of gait impairment. BMC Bioinform. 2018;19(1):471.
Google Scholar
Ferraris C, Nerino R, Chimienti A, Pettiti G, Cau N, Cimolin V, Azzaro C, Albani G, Priano L, Mauro A. A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson’s Disease. Sensors (Basel). 2018;18:10.
Google Scholar
Arippa F, Pau M, Cimolin V, Stocchi F, Goffredo M, Franceschini M, Condoluci C, De Pandis MF, Galli M. A novel summary kinematic index for postural characterization in subjects with Parkinson’s disease. Eur J Phys Rehab Med. 2020;56:2.
Google Scholar
Ferraris C, Nerino R, Chimienti A, Pettiti G, Cau N, Cimolin V, Azzaro C, Priano L, Mauro A. Feasibility of home-based automated assessment of postural instability and lower limb impairments in Parkinson’s disease. Sensors (Basel). 2019;19(5):1129.
Google Scholar
Buongiorno D, Bortone I, Cascarano GD, Trotta GF, Brunetti A, Bevilacqua V. A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease. BMC Med Inform Decis Mak. 2019;19(Suppl 9):243.
PubMed
PubMed Central
Google Scholar
Tan D, Pua YH, Balakrishnan S, Scully A, Bower KJ, Prakash KM, Tan EK, Chew JS, Poh E, Tan SB, et al. Automated analysis of gait and modified timed up and go using the Microsoft Kinect in people with Parkinson’s disease: associations with physical outcome measures. Med Biol Eng Comput. 2019;57(2):369–77.
PubMed
Google Scholar
Bonanni L, Thomas A, Varanese S, Scorrano V, Onofrj M. Botulinum toxin treatment of lateral axial dystonia in Parkinsonism. Mov Disord. 2007;22(14):2097–103.
PubMed
Google Scholar
Barone P, Santangelo G, Amboni M, Pellecchia MT, Vitale C. Pisa syndrome in Parkinson’s disease and parkinsonism: clinical features, pathophysiology, and treatment. Lancet Neurol. 2016;15(10):1063–74.
PubMed
Google Scholar
Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155–63.
PubMed
PubMed Central
Google Scholar
Siderowf A, McDermott M, Kieburtz K, Blindauer K, Plumb S, Shoulson I, Parkinson Study G. Test-retest reliability of the unified Parkinson’s disease rating scale in patients with early Parkinson’s disease: results from a multicenter clinical trial. Movement Disorders. 2002;17(4):758–63.
Google Scholar
Mao Y, He Y, Liu L, Chen X. Disease classification based on eye movement features with decision tree and random forest. Front Neurosci. 2020;14:798.
PubMed
PubMed Central
Google Scholar
Panhalkar AR, Doye DD: A novel approach to build accurate and diverse decision tree forest. Evolutionary intelligence 2021:1–15.
Clark RA, Pua YH, Bryant AL, Hunt MA. Validity of the Microsoft Kinect for providing lateral trunk lean feedback during gait retraining. Gait Posture. 2013;38(4):1064–6.
PubMed
Google Scholar
Clark RA, Pua YH, Fortin K, Ritchie C, Webster KE, Denehy L, Bryant AL. Validity of the Microsoft Kinect for assessment of postural control. Gait Posture. 2012;36(3):372–7.
PubMed
Google Scholar
Clark RA, Pua YH, Oliveira CC, Bower KJ, Thilarajah S, McGaw R, Hasanki K, Mentiplay BF. Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control. Gait Posture. 2015;42(2):210–3.
PubMed
Google Scholar
Xu H, Yu Y, Zhou Y, Li Y, Du S. Measuring accurate body parameters of dressed humans with large-scale motion using a Kinect sensor. Sensors (Basel). 2013;13(9):11362–84.
Google Scholar
Guerrero C, Uribe-Quevedo A. Kinect-based posture tracking for correcting positions during exercise. Stud Health Technol Inform. 2013;184:158–60.
PubMed
Google Scholar
Romano G, Viggiano D. Interception of moving objects in karate: an experimental, marker-free benchmark. Muscles Ligaments Tendons J. 2014;4(2):101–5.
PubMed
PubMed Central
Google Scholar
Asakawa T, Fang H, Sugiyama K, Nozaki T, Kobayashi S, Hong Z, Suzuki K, Mori N, Yang Y, Hua F, et al. Human behavioral assessments in current research of Parkinson’s disease. Neurosci Biobehav Rev. 2016;68:741–72.
PubMed
Google Scholar
Asakawa T, Fang H, Sugiyama K, Nozaki T, Hong Z, Yang Y, Hua F, Ding G, Chao D, Fenoy AJ, et al. Animal behavioral assessments in current research of Parkinson’s disease. Neurosci Biobehav Rev. 2016;65:63–94.
PubMed
Google Scholar
Greene PE, Bressman S. Exteroceptive and interoceptive stimuli in dystonia. Mov Disord. 1998;13(3):549–51.
PubMed
CAS
Google Scholar