{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T06:58:25Z","timestamp":1787122705366,"version":"build-2736575974"},"reference-count":59,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Early diagnosis of Parkinson\u2019s disease (PD) is crucial for slowing its progression. Gait analysis is increasingly used to detect early symptoms, with smart insoles emerging as a cost-effective and convenient tool for daily monitoring. However, smart insoles have practical limitations, including durability concerns, limited battery life, and difficulties in minimizing the number of sensors. In this study, we designed a novel deep convolutional neural network model for accurately detecting abnormal gaits in patients with PD using a reduced number of sensors embedded in smart insoles. The proposed convolutional neural network (CNN) model was trained on a gait dataset collected from a total of 29 participants, including 13 healthy individuals, 9 elderly individuals, and 7 patients with Parkinson\u2019s disease (PD). Instead of combining plantar pressure data from both feet, the model processes each foot independently through sequential layers to better capture gait asymmetries. The proposed CNN model achieved a classification accuracy of 90.35% using only 8 of the 32 plantar pressure sensors in the smart insole, outperforming a conventional CNN model by approximately 10%. The experimental results demonstrate the potential of our CNN model for effectively detecting abnormal gait patterns in patients with PD while minimizing sensor requirements, enhancing the practicality and efficiency of smart insoles for real-world use.<\/jats:p>","DOI":"10.3390\/bios16010040","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T12:38:56Z","timestamp":1767616736000},"page":"40","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Convolutional Neural Network-Based Detection of Gait Abnormalities in Parkinson\u2019s Disease Using Fewer Plantar Sensors in a Smart Insole"],"prefix":"10.3390","volume":"16","author":[{"given":"Eun-Seo","family":"Park","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong 30019, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9519-9840","authenticated-orcid":false,"given":"Xianghong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong 30019, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1183-1219","authenticated-orcid":false,"given":"Han-Jeong","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea"},{"name":"Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong 30019, Republic of Korea"},{"name":"Digital Healthcare Center, Sejong Institute for Business and Technology, Korea University, Sejong 30019, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8668-3989","authenticated-orcid":false,"given":"Chang-Hee","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong 30019, Republic of Korea"},{"name":"Digital Healthcare Center, Sejong Institute for Business and Technology, Korea University, Sejong 30019, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s40035-023-00378-6","article-title":"Role of Dopamine in the Pathophysiology of Parkinson\u2019s Disease","volume":"12","author":"Zhou","year":"2023","journal-title":"Transl. Neurodegener."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Galv\u00e1n, A., Devergnas, A., and Wichmann, T. (2015). Alterations in Neuronal Activity in Basal Ganglia-Thalamocortical Circuits in the Parkinsonian State. Front. Neuroanat., 9.","DOI":"10.3389\/fnana.2015.00005"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1136\/jnnp.2007.131045","article-title":"Parkinson\u2019s Disease: Clinical Features and Diagnosis","volume":"79","author":"Jankovic","year":"2008","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_4","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_5","first-page":"485063","article-title":"Dopamine-Induced Nonmotor Symptoms of Parkinson\u2019s Disease","volume":"2011","author":"Park","year":"2011","journal-title":"Park. Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/nursrep12010001","article-title":"Relationship Between Motor and Nonmotor Symptoms and Quality of Life in Patients with Parkinson\u2019s Disease","volume":"12","author":"Parra","year":"2021","journal-title":"Nurs. Rep."},{"key":"ref_7","first-page":"323","article-title":"The Early Treatment Phase in Parkinson\u2019s Disease: Not a Honeymoon for All, Not a Honeymoon at All?","volume":"13","author":"Voeten","year":"2023","journal-title":"J. Park. Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1177\/1756285609339383","article-title":"Review: Improving Symptom Control in Early Parkinson\u2019s Disease","volume":"2","author":"Isaacson","year":"2009","journal-title":"Ther. Adv. Neurol. Disord."},{"key":"ref_9","first-page":"s00431777775","article-title":"The Clinical Diagnosis of Parkinson\u2019s Disease","volume":"82","author":"Munhoz","year":"2024","journal-title":"Arq. Neuro-Psiquiatr."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tartaglia, M.C., Zhan, W., and Ofori, E. (2023). Editorial: Neuroimaging in Parkinson\u2019s Disease and Parkinsonism. Front. Neurol., 14.","DOI":"10.3389\/fneur.2023.1153682"},{"key":"ref_11","first-page":"65","article-title":"Psychiatric Aspects of Parkinson\u2019s Disease","volume":"6","author":"Grover","year":"2014","journal-title":"J. Neurosci. Rural Pract."},{"key":"ref_12","first-page":"2046","article-title":"Parkinson\u2019s Disease: Diagnosis and Treatment","volume":"74","author":"Rao","year":"2006","journal-title":"Am. Fam. Physician"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Holmes, S., and Tinaz, S. (2024). Neuroimaging Biomarkers in Parkinson\u2019s Disease. Neurophysiologic Biomarkers in Neuropsychiatric Disorders, Springer.","DOI":"10.1007\/978-3-031-69491-2_21"},{"key":"ref_14","first-page":"1537","article-title":"Barriers to Accessing Healthcare Services for People with Parkinson\u2019s Disease: A Scoping Review","volume":"11","author":"Zaman","year":"2021","journal-title":"J. Park. Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1016\/j.neurol.2012.07.004","article-title":"When Does Parkinson\u2019s Disease Begin? From Prodromal Disease to Motor Signs","volume":"168","author":"Meissner","year":"2012","journal-title":"Rev. Neurol."},{"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","unstructured":"Bayen, S., Lagon, X., Cauet, C., Bayen, M., Richebe, T., Messaadi, N., and Calafiore, M. (2025). Time is Health: Management of Parkinson\u2019s Disease in Primary Care: A Retrospective Quantitative Study of Diagnostic and Therapeutic Timelines. BMC Prim. Care, 26.","DOI":"10.1186\/s12875-025-02911-0"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, Y., Yang, J., Liu, Y., Chen, X., and Yang, G. (2022). Detection and Assessment of Parkinson\u2019s Disease Based on Gait Analysis: A Survey. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.916971"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, X., Fan, W., Hu, Y., Li, L., Chen, Z., and Guan, Q. (2022). Single- and Dual-Task Gait Performance and Their Diagnostic Value in Early-Stage Parkinson\u2019s Disease. Front. Neurol., 13.","DOI":"10.3389\/fneur.2022.974985"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yin, W., Zhu, W., Gao, H., Niu, X., Shen, C., Fan, X., and Wang, C. (2024). Gait Analysis in the Early Stage of Parkinson\u2019s Disease with a Machine Learning Approach. Front. Neurol., 15.","DOI":"10.3389\/fneur.2024.1472956"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Fling, B.W., Curtze, C., and Horak, F.B. (2018). Gait Asymmetry in People with Parkinson\u2019s Disease Is Linked to Reduced Integrity of Callosal Sensorimotor Regions. Front. Neurol., 9.","DOI":"10.3389\/fneur.2018.00215"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Toly Chen, T.-C., and Lee, Y.-J. (2024). Smart and Healthy Walking: Toward Better Health and Life in Smart Cities, Springer.","DOI":"10.1007\/978-3-031-59443-4"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nassajpour, M., Seifallahi, M., Rosenfeld, A., Tolea, M.I., Galvin, J.E., and Ghoraani, B. (2025). Comparison of Wearable and Depth-Sensing Technologies with Electronic Walkway for Comprehensive Gait Analysis. Sensors, 25.","DOI":"10.3390\/s25175501"},{"key":"ref_25","first-page":"S90","article-title":"Gait Analysis\u2014Available Platforms for Outcome Assessment","volume":"51","author":"Brand","year":"2019","journal-title":"Injury"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hulleck, A.A., Mohan, D.M., Abdallah, N., El-Rich, M., and Khalaf, K. (2022). Present and Future of Gait Assessment in Clinical Practice: Towards the Application of Novel Trends and Technologies. Front. Med. Technol., 4.","DOI":"10.3389\/fmedt.2022.901331"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Schlachetzki, J.C.M., Barth, J., Marxreiter, F., Go\u00dfler, J., Kohl, Z., Reinfelder, S., Ga\u00dfner, H., Aminian, K., Eskofier, B.M., and Winkler, J. (2017). Wearable Sensors Objectively Measure Gait Parameters in Parkinson\u2019s Disease. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183989"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Prasanth, H., Caban, M., Keller, U., Courtine, G., Ijspeert, A.J., Vallery, H., and von Zitzewitz, J. (2021). Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review. Sensors, 21.","DOI":"10.3390\/s21082727"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chatzaki, C., Skaramagkas, V., Tachos, N.S., 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_30","unstructured":"Boucharas, D., Androutsos, C., Gkois, G., Tsakanikas, V., Pezoulas, V., Manousos, D., Skaramagkas, V., Chatzaki, C., Kontogiannis, S., and Spandonidis, C. (2022). Smart Insole: A Gait Analysis Monitoring Platform Targeting Parkinson Disease Patients Based on Insoles. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106250","DOI":"10.1016\/j.mtcomm.2023.106250","article-title":"Smart Wearables Addressing Gait Disorders: A Review","volume":"35","author":"Biswas","year":"2023","journal-title":"Mater. Today Commun."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Santos, V., Gomes, B.B., Neto, M.A., and Amaro, A.M. (2024). A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions. Appl. Sci., 14.","DOI":"10.3390\/app14146085"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Subramaniam, S., Majumder, S., Faisal, A.I., and Deen, M.J. (2022). Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges. Sensors, 22.","DOI":"10.3390\/s22020438"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ramamoorthy, E.N., and Kleiber, B. (2015). The Foot, Springer eBooks.","DOI":"10.1007\/978-3-319-16157-0_11"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Perry, J., and Burnfield, J.M. (2024). Gait Analysis, CRC Press eBooks.","DOI":"10.1201\/9781003525592"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1249\/00005768-198902000-00020","article-title":"Asymmetries in Ground Reaction Force Patterns in Normal Human Gait","volume":"21","author":"Herzog","year":"1989","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.gaitpost.2014.11.010","article-title":"Foot Posture, Range of Motion and Plantar Pressure Characteristics in Obese and Non-Obese Individuals","volume":"41","author":"Butterworth","year":"2014","journal-title":"Gait Posture"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1249\/01.MSS.0000069337.49567.F0","article-title":"Symmetry and Reproducibility of Kinematic Parameters during Various Running Techniques","volume":"35","author":"Karamanidis","year":"2003","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1186\/s13643-019-1063-z","article-title":"Effects of Walking Speed on Gait Biomechanics in Healthy Participants: A Systematic Review and Meta-Analysis","volume":"8","author":"Fukuchi","year":"2019","journal-title":"Syst. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3937","DOI":"10.1109\/TNSRE.2023.3314100","article-title":"An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson\u2019s Disease","volume":"31","author":"Chen","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"20240565","DOI":"10.1098\/rsif.2024.0565","article-title":"Modelling Individual Variation in Human Walking Gait across Populations and Walking Conditions via Gait Recognition","volume":"21","author":"Duncanson","year":"2024","journal-title":"J. R. Soc. Interface"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gupta, S., Saviour, C.M., Pal, B., Chanda, S., and Mukherjee, K. (2025). Biomechanics of Gait. Biomechanics of Gait, Springer.","DOI":"10.1007\/978-981-96-0586-6_3"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lee, S.-S., Choi, S.T., and Choi, S. (2019). Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole. Sensors, 19.","DOI":"10.3390\/s19081757"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/S0268-0033(01)00069-9","article-title":"Heel to Toe Motion Characteristics in Parkinson Patients during Free Walking","volume":"16","author":"Kimmeskamp","year":"2001","journal-title":"Clin. Biomech."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.gaitpost.2024.06.018","article-title":"A Cross-Sectional Pilot Study Utilising STRain Analysis and Mapping of the Plantar Surface (STAMPS) to Measure Plantar Load Characteristics within a Healthy Population","volume":"113","author":"Jones","year":"2024","journal-title":"Gait Posture"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s13047-017-0209-2","article-title":"Normal Foot Loading Parameters and Repeatability of the Footscan\u00ae Platform System","volume":"10","author":"Xu","year":"2017","journal-title":"J. Foot Ankle Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wolff, C., Steinheimer, P., Warmerdam, E., Dahmen, T., Slusallek, P., Schlinkmann, C., Chen, F., Orth, M., Pohlemann, T., and Ganse, B. (2023). Effects of Age, Body Height, Body Weight, Body Mass Index and Handgrip Strength on the Trajectory of the Plantar Pressure Stance-Phase Curve of the Gait Cycle. Front. Bioeng. Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1110099"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.gaitpost.2024.01.005","article-title":"Spatiotemporal Gait Characteristics Across the Adult Lifespan: Reference Values from a Healthy Population\u2014Analysis of the COMPLETE Cohort Study","volume":"109","author":"Wagner","year":"2024","journal-title":"Gait Posture"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Anderson, A.J., Eguren, D., Gonzalez, M., Khan, N., Watkinson, S.A., Caiola, M., Hirczy, S., Zabetian, C.P., Mills, K.A., and Moukheiber, E. (2024). WearGait-PD: An Open-Access Wearables Dataset for Gait in Parkinson\u2019s Disease and Age-Matched Controls. medRxiv.","DOI":"10.1101\/2024.09.11.24313476"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Santos, V., Gomes, B.B., Neto, M.A., Rodrigues, P.F., and Amaro, A.M. (2025). A Systematic Review on Smart Insole Prototypes: Development and Optimization Pathways. Actuators, 14.","DOI":"10.37766\/inplasy2025.2.0031"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fazio, R.D., Perrone, E., Vel\u00e1zquez, R., Di Vittorio, M., and Visconti, P. (2021). Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring. Sensors, 21.","DOI":"10.3390\/s21134539"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Neseem, M., Nelson, J., and Reda, S. (2020, January 19\u201323). AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices. Proceedings of the 2020 57th ACM\/IEEE Design Automation Conference (DAC), San Francisco, CA, USA.","DOI":"10.1109\/DAC18072.2020.9218568"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Park, J.M., Moon, C.-W., Lee, B.C., Oh, E., Lee, J., Jang, W., Cho, K.H., and Lee, S.-H. (2024). Detection of Freezing of Gait in Parkinson\u2019s Disease from Foot-Pressure Sensing Insoles Using a Temporal Convolutional Neural Network. Front. Aging Neurosci., 16.","DOI":"10.3389\/fnagi.2024.1437707"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s12984-022-00987-8","article-title":"Deep Learning Approach to Estimate Foot Pressure Distribution in Walking with Application for a Cost-Effective Insole System","volume":"19","author":"Mun","year":"2022","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Leyh, C., and Feipel, V. (2022). Impact of Sex and Velocity on Plantar Pressure Distribution during Gait: A Cross-Sectional Study Using an Instrumented Pressure-Sensitive Walkway. J. Funct. Morphol. Kinesiol., 7.","DOI":"10.3390\/jfmk7040106"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1016\/j.apmr.2023.05.008","article-title":"Cerebral Small Vessel Disease Burden: An Independent Biomarker for Anomia Treatment Responsiveness in Chronic Stroke Patients with Aphasia","volume":"104","author":"Varkanitsa","year":"2023","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lee, M., Youm, C., Noh, B., Park, H., and Cheon, S. (2020). Gait Characteristics under Imposed Challenge Speed Conditions in Patients with Parkinson\u2019s Disease during Overground Walking. Sensors, 20.","DOI":"10.3390\/s20072132"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"100355","DOI":"10.1016\/j.medntd.2025.100355","article-title":"Differences in Gait Dynamic Stability of Healthy Subjects Due to Age, Body Mass, Height, BMI, and Walking Speed","volume":"26","author":"Raza","year":"2025","journal-title":"Med. Nov. Technol. 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