{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T03:20:33Z","timestamp":1789788033267,"version":"4.0.1"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is characterized by alterations in movement dynamics that are difficult to quantify with conventional clinical assessment. This study proposes an integrated approach combining graph-based kinematic analysis with explainable machine learning to identify digital biomarkers of Parkinsonian motor impairment. Kinematic signals were acquired using Xsens inertial sensors from 51 patients with PD and 53 healthy controls. For each participant, subject-specific kinematic networks were constructed by modeling inter-segment similarities through Jensen\u2013Shannon divergence, from which global and local graph-theoretical metrics were extracted. A machine learning pipeline incorporating voting feature selection, and XGBoost classification was evaluated using a nested cross-validation design. The model achieved robust performance (AUC = 0.87), and explainability analyses using SHAP identified a subset of 13 features capturing alterations in velocity, inter-segment connectivity, and network centrality. PD was characterized by increased positional variability, reduced distal limb velocity, and a redistribution of network centrality towards proximal body segments. These features were associated with clinical severity, confirming their physiological relevance. By integrating graph-theoretical modeling, explainable artificial intelligence, and machine learning methodology, this work provides a method of discovering quantitative biomarkers capturing alterations in motor coordination. These findings highlight the potential of ML and kinematic networks to support objective motor assessment in PD.<\/jats:p>","DOI":"10.3390\/bioengineering13020151","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T08:48:04Z","timestamp":1769590084000},"page":"151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Similarity Gait Networks with XAI for Parkinson\u2019s Disease Classification: A Pilot Study"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2705-8718","authenticated-orcid":false,"given":"Maria Giovanna","family":"Bianco","sequence":"first","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7936-8408","authenticated-orcid":false,"given":"Camilla","family":"Calomino","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marianna","family":"Cras\u00e0","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5153-5595","authenticated-orcid":false,"given":"Alessia","family":"Cristofaro","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Sgr\u00f2","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabiana","family":"Novellino","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3391-1698","authenticated-orcid":false,"given":"Salvatore Andrea","family":"Pullano","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Applications Technologies and Sensors (BATS), Department of Health Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0501-0027","authenticated-orcid":false,"given":"Syed Kamrul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3813-283X","authenticated-orcid":false,"given":"Jolanda","family":"Buonocore","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"},{"name":"Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aldo","family":"Quattrone","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2071-2083","authenticated-orcid":false,"given":"Andrea","family":"Quattrone","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"},{"name":"Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4664-1500","authenticated-orcid":false,"given":"Rita","family":"Nistic\u00f2","sequence":"additional","affiliation":[{"name":"Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1002\/mds.22512","article-title":"Motor patterns in Parkinson\u2019s disease: A data-driven approach","volume":"24","author":"Visser","year":"2009","journal-title":"Mov. Disord."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1002\/mds.29362","article-title":"Redefining Bradykinesia","volume":"38","author":"Bologna","year":"2023","journal-title":"Mov. Disord."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kehnemouyi, Y.M., Coleman, T.P., and Tass, P.A. (2024). Emerging wearable technologies for multisystem monitoring and treatment of Parkinson\u2019s disease: A narrative review. Front. Netw. Physiol., 4.","DOI":"10.3389\/fnetp.2024.1354211"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Brognara, L., Mazzotti, A., Zielli, S.O., Arceri, A., Artioli, E., Traina, F., and Faldini, C. (2024). Wearable Technology Applications and Methods to Assess Clinical Outcomes in Foot and Ankle Disorders: Achievements and Perspectives. Sensors, 24.","DOI":"10.3390\/s24217059"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Caballol, N., Bay\u00e9s, \u00c0., Prats, A., Mart\u00edn-Baranera, M., and Quispe, P. (2023). Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson\u2019s disease. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0279910"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Calomino, C., Gramigna, V., Bianco, M.G., Cristofaro, A., Quattrone, A., and Quattrone, A. (2023, January 28\u201329). A Quantitative Kinematic Evaluation of Postural Response in Parkinson\u2019s Disease Subtypes. Proceedings of the 2023 International Workshop on Biomedical Applications, Technologies and Sensors (BATS), Catanzaro, Italy.","DOI":"10.1109\/BATS59463.2023.10303069"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/0924-980X(95)00109-X","article-title":"Leg muscle activation during gait in Parkinson\u2019s disease: Adaptation and interlimb coordination","volume":"97","author":"Dietz","year":"1995","journal-title":"Electroencephalogr. Clin. Neurophysiol.\/Electromyogr. Mot. Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1002\/mds.22609","article-title":"Bradykinesia, muscle weakness and reduced muscle power in Parkinson\u2019s disease","volume":"24","author":"Allen","year":"2009","journal-title":"Mov. Disord."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rovini, E., Maremmani, C., and Cavallo, F. (2017). How Wearable Sensors Can Support Parkinson\u2019s Disease Diagnosis and Treatment: A Systematic Review. Front. Neurosci., 11.","DOI":"10.3389\/fnins.2017.00555"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/nn.4502","article-title":"Network neuroscience","volume":"20","author":"Bassett","year":"2017","journal-title":"Nat. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lodin, J., Jel\u00ednek, M., Same\u0161, M., and Vachata, P. (2022). Quantitative Gait Analysis of Patients with Severe Symptomatic Spinal Stenosis Utilizing the Gait Profile Score: An Observational Clinical Study. Sensors, 22.","DOI":"10.3390\/s22041633"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Calomino, C., Quattrone, A., Bianco, M.G., Nistic\u00f2, R., Buonocore, J., Cras\u00e0, M., Vaccaro, M.G., Sarica, A., and Quattrone, A. (2024). Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor. Front. Neurol., 15.","DOI":"10.3389\/fneur.2024.1372262"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e70346","DOI":"10.1002\/brb3.70346","article-title":"Volumetric Assessment and Graph Theoretical Analysis of Thalamic Nuclei in Essential Tremor","volume":"15","author":"Bianco","year":"2025","journal-title":"Brain Behav."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"30908","DOI":"10.1038\/s41598-024-81762-8","article-title":"Human motion similarity evaluation based on deep metric learning","volume":"14","author":"Zhang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"129450","DOI":"10.1016\/j.neucom.2025.129450","article-title":"ChiGa-Net: A genetically optimized neural network with refined deeply extracted features using statistical score for trustworthy Parkinson\u2019s disease detection","volume":"624","author":"Ali","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, J., Zhuang, L., Chaurasia, A., and Najafi, A. (2024). Investigating the Application of Stress Wave Factors in Machine Learning for Delamination Location Prediction in a Composite Laminate. AIAA SCITECH 2024 Forum, American Institute of Aeronautics and Astronautics.","DOI":"10.2514\/6.2024-2620"},{"key":"ref_17","first-page":"100216","article-title":"A machine learning framework for guided wave-based damage detection of rail head using surface-bonded piezo-electric wafer transducers","volume":"7","author":"Mahajan","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_18","first-page":"723","article-title":"Motion Graphs","volume":"Volume 2","author":"Kovar","year":"2023","journal-title":"Seminal Graphics Papers: Pushing the Boundaries"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jalata, I.K., Truong, T.-D., Allen, J.L., Seo, H.-S., and Luu, K. (2021). Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network. Future Internet, 13.","DOI":"10.3390\/fi13080194"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Naro, A., Calabr\u00f2, R.S., La Rosa, G., Andronaco, V.A., Billeri, L., Lauria, P., Bramanti, A., and Bramanti, P. (2019). Toward understanding the neurophysiological basis of peripersonal space: An EEG study on healthy individuals. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0218675"},{"key":"ref_21","unstructured":"Rashnu, A., Salimi-Badr, A., Rashnu, A., and Salimi-Badr, A. (2024). Integrative Deep Learning Framework for Parkinson\u2019s Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach. arXiv."},{"key":"ref_22","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_23","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): Scale presentation and clinimetric testing results","volume":"23","author":"Goetz","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_24","unstructured":"Movella (Xsens Technologies) (2025, December 01). Xsens MVN Animate. Available online: https:\/\/www.movella.com\/motion-capture\/xsens-mvn-animate."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1080\/13658816.2022.2143505","article-title":"A Siamese neural network for learning the similarity metrics of linear features","volume":"37","author":"Li","year":"2023","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v021.i07","article-title":"ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R","volume":"21","author":"Duong","year":"2007","journal-title":"J. Stat. Softw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1038\/nrn3901","article-title":"The connectomics of brain disorders","volume":"16","author":"Fornito","year":"2015","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_28","unstructured":"Fornito, A., Zalesky, A., and Bullmore, E.T. (2016). Fundamentals of Brain Network Analysis, Academic Press."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1002\/mds.26718","article-title":"Free-living monitoring of Parkinson\u2019s disease: Lessons from the field","volume":"31","author":"Godfrey","year":"2016","journal-title":"Mov. Disord."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1007\/s00415-014-7513-6","article-title":"Gait and balance in Parkinson\u2019s disease subtypes: Objective measures and classification considerations","volume":"261","author":"Herman","year":"2014","journal-title":"J. Neurol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/TITB.2009.2033471","article-title":"Monitoring Motor Fluctuations in Patients with Parkinson\u2019s Disease Using Wearable Sensors","volume":"13","author":"Patel","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gong, L., and Wang, J. (2013, January 11\u201314). Complexity analysis of electroencephalogram signal based on Jensen-Shannon divergence. Proceedings of the 2013 6th International Conference on Biomedical Engineering and Informatics, Barcelona, Spain.","DOI":"10.1109\/BMEI.2013.6746937"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"giad046","DOI":"10.1093\/gigascience\/giad046","article-title":"Efficient real-time selective genome sequencing on resource-constrained devices","volume":"12","author":"Shih","year":"2022","journal-title":"Gigascience"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Faes, L., Porta, A., Nollo, G., and Javorka, M. (2016). Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks. Entropy, 19.","DOI":"10.3390\/e19010005"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1111\/j.1460-9568.2008.06167.x","article-title":"Bilateral coordination of walking and freezing of gait in Parkinson\u2019s disease","volume":"27","author":"Plotnik","year":"2008","journal-title":"Eur. J. Neurosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/S1474-4422(19)30044-4","article-title":"Gait impairments in Parkinson\u2019s disease","volume":"18","author":"Mirelman","year":"2019","journal-title":"Lancet Neurol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1111\/jon.13107","article-title":"Topological reorganization of functional hubs in patients with Parkinson\u2019s disease with freezing of gait","volume":"33","author":"Sreenivasan","year":"2023","journal-title":"J. Neuroimaging"},{"key":"ref_38","first-page":"6139716","article-title":"Quantitative Analysis of Motor Status in Parkinson\u2019s Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications","volume":"2017","author":"Suzuki","year":"2017","journal-title":"Park. Dis."},{"key":"ref_39","first-page":"497","article-title":"Interlimb coordination in patients with Parkinson\u2019s disease: Motor learning deficits and the importance of augmented information feedback","volume":"113","author":"Verschueren","year":"1997","journal-title":"ExBrain Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Trabassi, D., Serrao, M., Varrecchia, T., Ranavolo, A., Coppola, G., De Icco, R., Tassorelli, C., and Castiglia, S.F. (2022). Machine Learning Approach to Support the Detection of Parkinson\u2019s Disease in IMU-Based Gait Analysis. Sensors, 22.","DOI":"10.3390\/s22103700"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1002\/mds.22896","article-title":"Motor laterality asymmetry and nonmotor symptoms in Parkinson\u2019s disease","volume":"25","author":"Cubo","year":"2010","journal-title":"Mov. Disord."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1093\/brain\/aws360","article-title":"The cerebellum in Parkinson\u2019s disease","volume":"136","author":"Wu","year":"2013","journal-title":"Brain"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"23732","DOI":"10.1038\/s41598-024-75445-7","article-title":"Deep learning-based detection of affected body parts in Parkinson\u2019s disease and freezing of gait using time-series imaging","volume":"14","author":"Park","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TBME.2006.886670","article-title":"Quantification of Tremor and Bradykinesia in Parkinson\u2019s Disease Using a Novel Ambulatory Monitoring System","volume":"54","author":"Salarian","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/2\/151\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T09:14:44Z","timestamp":1769591684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/2\/151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,28]]},"references-count":45,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["bioengineering13020151"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13020151","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,28]]}}}