{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:49:09Z","timestamp":1791928149573,"version":"4.3.4"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"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 the second-most common neurodegenerative disease. With more than 20,000 new diagnosed cases each year, PD affects millions of individuals worldwide and is most prevalent in the elderly population. The current clinical methods for the diagnosis and severity assessment of PD rely on the visual and physical examination of subjects and identifying key disease motor signs and symptoms such as bradykinesia, rigidity, tremor, and postural instability. In the present study, we developed a method for the diagnosis and severity assessment of PD using Gramian Angular Fields (GAFs) in combination with deep Convolutional Neural Networks (CNNs). Our model was applied to PD gait signals captured using pressure sensors embedded into insoles. Our results indicated an accuracy of 98.6%, a true positive rate (TPR) of 99.2%, and a true negative rate (TNR) of 98.5%, showcasing superior classification performance for PD diagnosis compared to the methods used in recent studies in the literature. The estimation of disease severity scores using gait signals showed a high accuracy for the Hoehn and Yahr score as well as the Timed Up and Go (TUG) test score (R2 &gt; 0.8), while we achieved a lower prediction performance for the Unified Parkinson\u2019s Disease Rating Scale (UPDRS) and its motor component (UPDRSM) scores (R2 &lt; 0.2). These results were achieved using gait signals recorded in time windows as small as 10 s, which may pave the way for shorter, more accessible assessment tools for diagnosis and severity assessment of PD.<\/jats:p>","DOI":"10.3390\/s25113421","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T06:25:04Z","timestamp":1748499904000},"page":"3421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Detection and Severity Assessment of Parkinson\u2019s Disease Through Analyzing Wearable Sensor Data Using Gramian Angular Fields and Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"25","author":[{"given":"Sayyed Mostafa","family":"Mostafavi","sequence":"first","affiliation":[{"name":"College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1949-9795","authenticated-orcid":false,"given":"Shovito Barua","family":"Soumma","sequence":"additional","affiliation":[{"name":"College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4639-6544","authenticated-orcid":false,"given":"Daniel","family":"Peterson","sequence":"additional","affiliation":[{"name":"College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3608-4456","authenticated-orcid":false,"given":"Shyamal H.","family":"Mehta","sequence":"additional","affiliation":[{"name":"Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ 85259, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1844-1416","authenticated-orcid":false,"given":"Hassan","family":"Ghasemzadeh","sequence":"additional","affiliation":[{"name":"College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s00702-017-1686-y","article-title":"Epidemiology of Parkinson\u2019s disease","volume":"124","author":"Tysnes","year":"2017","journal-title":"J. Neural Transm."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S1474-4422(06)70471-9","article-title":"Epidemiology of Parkinson\u2019s disease","volume":"5","author":"Breteler","year":"2006","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1016\/S1474-4422(17)30299-5","article-title":"Global, regional, and national burden of neurological disorders during 1990\u20132015: A systematic analysis for the Global Burden of Disease Study 2015","volume":"16","author":"Feigin","year":"2017","journal-title":"Lancet Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1001\/jamaneurol.2017.3299","article-title":"The Parkinson pandemic\u2014A call to action","volume":"75","author":"Dorsey","year":"2018","journal-title":"JAMA Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/S0896-6273(03)00568-3","article-title":"Parkinson\u2019s disease: Mechanisms and models","volume":"39","author":"Dauer","year":"2003","journal-title":"Neuron"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17013","DOI":"10.1038\/nrdp.2017.13","article-title":"Parkinson disease","volume":"3","author":"Poewe","year":"2017","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_7","first-page":"684","article-title":"Falls and freezing of gait in Parkinson\u2019s disease","volume":"3","author":"Bloem","year":"2004","journal-title":"Lancet Neurol."},{"key":"ref_8","first-page":"S8","article-title":"Understanding freezing of gait in Parkinson\u2019s disease: Clinical implications and treatment","volume":"23","author":"Nieuwboer","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1002\/mds.21720","article-title":"The role of executive function and attention in gait","volume":"23","author":"Hausdorff","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_10","first-page":"518","article-title":"Gait dynamics in Parkinson\u2019s disease: Comparison with a healthy control group and relevance to the occurrence of freezing of gait","volume":"18","author":"Schaafsma","year":"2003","journal-title":"Mov. Disord."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1136\/jnnp.2008.157362","article-title":"Bilateral coordination of gait and Parkinson\u2019s disease: The effects of dual tasking","volume":"80","author":"Plotnik","year":"2009","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.gaitpost.2012.09.025","article-title":"Is gait variability reliable in older adults and Parkinson\u2019s disease? Towards an optimal testing protocol","volume":"37","author":"Galna","year":"2013","journal-title":"Gait Posture"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/S0268-0033(01)00035-3","article-title":"The biomechanics and motor control of gait in Parkinson\u2019s disease","volume":"16","author":"Morris","year":"2001","journal-title":"Clin. Biomech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1002\/mds.22340","article-title":"Movement Disorder Society-sponsored revision of the Unified Parkinson\u2019s Disease Rating Scale (MDS-UPDRS)","volume":"23","author":"Goetz","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_15","first-page":"1127","article-title":"Characterization of gait abnormalities in Parkinson\u2019s disease using a smartphone-based system","volume":"41","author":"Tien","year":"2010","journal-title":"Gait Posture"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/S0197-4580(02)00065-9","article-title":"Staging of brain pathology related to sporadic Parkinson\u2019s disease","volume":"24","author":"Braak","year":"2003","journal-title":"Neurobiol. Aging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1002\/mds.26424","article-title":"MDS clinical diagnostic criteria for Parkinson\u2019s disease","volume":"30","author":"Postuma","year":"2015","journal-title":"Mov. Disord."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1002\/1531-8257(200011)15:6<1112::AID-MDS1008>3.0.CO;2-A","article-title":"Cross-sectional prevalence survey of idiopathic Parkinson\u2019s disease and parkinsonism in London","volume":"15","author":"Schrag","year":"2000","journal-title":"Mov. Disord."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1097\/WCO.0000000000000112","article-title":"Premotor and nonmotor features of Parkinson\u2019s disease","volume":"27","author":"Goldman","year":"2014","journal-title":"Curr. Opin. Neurol."},{"key":"ref_20","first-page":"109","article-title":"Distinguishing different stages of Parkinson\u2019s disease using composite image of handwritings","volume":"81","author":"Zham","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1950011","DOI":"10.1142\/S0129065719500114","article-title":"Assisted diagnosis of parkinsonism based on the striatal morphology","volume":"29","author":"Segovia","year":"2019","journal-title":"Int. J. Neural Syst."},{"key":"ref_22","first-page":"2372","article-title":"Wearable pendant device monitoring using new wavelet-based methods shows changes in gait for people with early Parkinson\u2019s disease","volume":"63","author":"Brodie","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"V\u00e1radi, C., Neh\u00e9z, K., Horny\u00e1k, O., Viskolcz, B., and Bones, J. (2019). Serum N-glycosylation in Parkinson\u2019s disease: A novel approach for potential alterations. Molecules, 24.","DOI":"10.3390\/molecules24122220"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"985789","DOI":"10.1155\/2014\/985789","article-title":"An efficient diagnosis system for Parkinson\u2019s disease using kernel-based extreme learning machine with subtractive clustering features weighting approach","volume":"2014","author":"Ma","year":"2014","journal-title":"Comput. Math. Methods Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1177\/0891988702250580","article-title":"Effects of cognitive challenge on gait variability in patients with Parkinson\u2019s disease","volume":"16","author":"Hausdorff","year":"2003","journal-title":"J. Geriatr. Psychiatry Neurol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1002\/mds.20507","article-title":"Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson\u2019s disease","volume":"20","author":"Giladi","year":"2005","journal-title":"Mov. Disord."},{"key":"ref_27","first-page":"892","article-title":"Wearable inertial measurement units for automatic detection of freezing of gait in Parkinson\u2019s disease patients using machine learning","volume":"17","author":"Cabestany","year":"2017","journal-title":"Sensors"},{"key":"ref_28","first-page":"1805","article-title":"Gait pattern recognition in Parkinson\u2019s disease using wearable inertial sensors and a recurrent neural network","volume":"9","author":"Zhao","year":"2018","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_29","first-page":"2306","article-title":"Smartphone-based gait analysis using machine learning algorithms for detecting Parkinson\u2019s disease","volume":"63","author":"Eskofier","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","first-page":"3836","article-title":"A machine learning-based approach to assess gait patterns of Parkinson\u2019s disease patients","volume":"50","author":"Pereira","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_31","first-page":"026037","article-title":"A novel feature extraction method using GAF and CNN for motor imagery EEG classification","volume":"18","author":"Liu","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/1743-0003-2-23","article-title":"Effect of gait speed on gait rhythmicity in Parkinson\u2019s disease: Variability of stride time and swing time respond differently","volume":"2","author":"Giladi","year":"2005","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.neuroscience.2018.03.021","article-title":"Dual-task interference disrupts Parkinson\u2019s gait across multiple cognitive domains","volume":"379","author":"Penko","year":"2018","journal-title":"Neuroscience"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1111\/j.1460-9568.2005.04298.x","article-title":"Dual tasking, gait rhythmicity, and Parkinson\u2019s disease: Which aspects of gait are attention demanding?","volume":"22","author":"Yogev","year":"2005","journal-title":"Eur. J. Neurosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1111\/j.1460-9568.2007.05810.x","article-title":"Rhythmic auditory stimulation modulates gait variability in Parkinson\u2019s disease","volume":"26","author":"Hausdorff","year":"2007","journal-title":"Eur. J. Neurosci."},{"key":"ref_37","first-page":"18156","article-title":"Human activity recognition based on Gramian angular field and deep convolutional neural network","volume":"8","author":"Xu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","first-page":"1","article-title":"pyts: A Python package for time series classification","volume":"21","author":"Faouzi","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_39","unstructured":"Wang, Z., and Oates, T. (2015). Imaging time-series to improve classification and imputation. arXiv."},{"key":"ref_40","first-page":"273","article-title":"Electroencephalogram analysis based on Gramian angular field transformation","volume":"2485","author":"Bragin","year":"2019","journal-title":"CEUR Workshop Proc."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, C., De la Torre-Lorenzo, J., Hornero, R., Alonso-Betanzos, A., and Ibarrola, D. (2020). Automatic seizure detection based on imaged-EEG signals through fully convolutional networks. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-78784-3"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7338","DOI":"10.1016\/j.eswa.2012.01.084","article-title":"Parkinson\u2019s disease classification using gait characteristics and wavelet-based feature extraction","volume":"39","author":"Lee","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.bspc.2012.04.007","article-title":"Chi-square distance kernel of the gaits for the diagnosis of Parkinson\u2019s disease","volume":"8","author":"Daliri","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.jbi.2016.01.014","article-title":"A q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson\u2019s disease","volume":"60","author":"Jane","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.eswa.2016.03.018","article-title":"Detection of Parkinson\u2019s disease by shifted one-dimensional local binary patterns from gait","volume":"56","author":"Kaya","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.bspc.2016.08.022","article-title":"Measuring signal fluctuations in gait rhythm time series of patients with Parkinson\u2019s disease using entropy parameters","volume":"31","author":"Wu","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Alam, M.N., Garg, A., Munia, T.T., Fazel-Rezai, R., and Tavakolian, K. (2017). Vertical ground reaction force marker for Parkinson\u2019s disease. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0175951"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yurdakul, O.C., Subathra, M.S.P., and George, S.T. (2020). Detection of Parkinson\u2019s disease from gait using neighborhood representation local binary patterns. Biomed. Signal Process. Control, 62.","DOI":"10.1016\/j.bspc.2020.102070"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.dsp.2015.05.011","article-title":"Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect","volume":"47","year":"2015","journal-title":"Digit. Signal Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.gaitpost.2014.01.008","article-title":"Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson\u2019s disease","volume":"39","author":"Galna","year":"2014","journal-title":"Gait Posture"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/11\/3421\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:42:58Z","timestamp":1760031778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/11\/3421"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,29]]},"references-count":52,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["s25113421"],"URL":"https:\/\/doi.org\/10.3390\/s25113421","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,29]]}}}