{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:17:42Z","timestamp":1773008262712,"version":"3.50.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"S9","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T00:00:00Z","timestamp":1576108800000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Assessment and rating of Parkinson\u2019s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society \u2013 sponsored revision of Unified Parkinson\u2019s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-019-0987-5","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T14:44:23Z","timestamp":1576507463000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson\u2019s Disease"],"prefix":"10.1186","volume":"19","author":[{"given":"Domenico","family":"Buongiorno","sequence":"first","affiliation":[]},{"given":"Ilaria","family":"Bortone","sequence":"additional","affiliation":[]},{"given":"Giacomo Donato","family":"Cascarano","sequence":"additional","affiliation":[]},{"given":"Gianpaolo Francesco","family":"Trotta","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Brunetti","sequence":"additional","affiliation":[]},{"given":"Vitoantonio","family":"Bevilacqua","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,12]]},"reference":[{"issue":"11","key":"987_CR1","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1016\/S1474-4422(17)30299-5","volume":"16","author":"Valery L Feigin","year":"2017","unstructured":"Feigin VL, Abajobir AA, Abate KH, Abd-Allah F, Abdulle AM, Abera SF, Abyu GY, Ahmed MB, Aichour AN, Aichour I, Aichour MTE, Akinyemi RO, Alabed S, Al-Raddadi R, Alvis-Guzman N, Amare AT, Ansari H, Anwari P, \u00c4rnl\u00f6v J, Asayesh H, Asgedom SW, Atey TM, Avila-Burgos L, Frinel E, Avokpaho GA, Azarpazhooh MR, Barac A, Barboza M, Barker-Collo SL, B\u00e4rnighausen T, Bedi N, Beghi E, Bennett DA, Bensenor IM, Berhane A, Betsu BD, Bhaumik S, Birlik SM, Biryukov S, Boneya DJ, Bulto LNB, Carabin H, Casey D, Casta\u00f1eda-Orjuela CA, Catal\u00e1-L\u00f3pez F, Chen H, Chitheer AA, Chowdhury R, Christensen H, Dandona L, Dandona R, de Veber GA, Dharmaratne SD, Do HP, Dokova K, Dorsey ER, Ellenbogen RG, Eskandarieh S, Farvid MS, Fereshtehnejad S-M, Fischer F, Foreman KJ, Geleijnse JM, Gillum RF, Giussani G, Goldberg EM, Gona PN, Goulart AC, Gugnani HC, Gupta R, Hachinski V, Gupta R, Hamadeh RR, Hambisa M, Hankey GJ, Hareri HA, Havmoeller R, Hay SI, Heydarpour P, Hotez PJ, Jakovljevic MMB, Javanbakht M, Jeemon P, Jonas JB, Kalkonde Y, Kandel A, Karch A, Kasaeian A, Kastor A, Keiyoro PN, Khader YS, Khalil IA, Khan EA, Khang Y. -H., Tawfih A, Khoja A, Khubchandani J, Kulkarni C, Kim D, Kim YJ, Kivimaki M, Kokubo Y, Kosen S, Kravchenko M, Krishnamurthi RV, Defo BK, Kumar GA, Kumar R, Kyu HH, Larsson A, Lavados PM, Li Y, Liang X, Liben ML, Lo WD, Logroscino G, Lotufo PA, Loy CT, Mackay MT, Razek HMAE, Razek MMAE, Majeed A, Malekzadeh R, Manhertz T, Mantovani LG, Massano J, Mazidi M, McAlinden C, Mehata S, Mehndiratta MM, Memish ZA, Mendoza W, Mengistie MA, Mensah GA, Meretoja A, Mezgebe HB, Miller TR, Mishra SR, Ibrahim NM, Mohammadi A, Mohammed KE, Mohammed S, Mokdad AH, Moradi-Lakeh M, Velasquez IM, Musa KI, Naghavi M, Ngunjiri JW, Nguyen CT, Nguyen G, Nguyen QL, Nguyen TH, Nichols E, Ningrum DNA, Nong VM, Norrving B, Noubiap JJN, Ogbo FA, Owolabi MO, Pandian JD, Parmar PG, Pereira DM, Petzold M, Phillips MR, Piradov MA, Poulton RG, Pourmalek F, Qorbani M, Rafay A, Rahman M, Rahman MH, Rai RK, Rajsic S, Ranta A, Rawaf S, Renzaho AMN, Rezai MS, Roth GA, Roshandel G, Rubagotti E, Sachdev P, Safiri S, Sahathevan R, Sahraian MA, Samy AM, Santalucia P, Santos IS, Sartorius B, Satpathy M, Sawhney M, Saylan MI, Sepanlou SG, Shaikh MA, Shakir R, Shamsizadeh M, Sheth KN, Shigematsu M, Shoman H, Silva DAS, Smith M, Sobngwi E, Sposato LA, Stanaway JD, Stein DJ, Steiner TJ, Stovner LJ, Abdulkader RS, Szoeke CE, Tabar\u00e9s-Seisdedos R, Tanne D, Theadom AM, Thrift AG, Tirschwell DL, Topor-Madry R, Tran BX, Truelsen T, Tuem KB, Ukwaja KN, Uthman OA. 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