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Our analysis reveals that while material innovations have achieved milligram-level sensitivity, a significant \u2018translational gap\u2019 persists due to limited validation in real-world environments and small cohort sizes. We conclude that multimodal fusion architectures are essential for accurately mapping digital biomarkers to clinical gold standards such as MDS-UPDRS. By leveraging edge AI for privacy and closed-loop feedback for intervention, this integration facilitates the transition from reactive clinical visits to proactive, personalized digital home-care ecosystems.<\/jats:p>","DOI":"10.3390\/s26072071","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T13:48:41Z","timestamp":1774532921000},"page":"2071","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AI-Enabled Flexible Sensing Ecosystems for Parkinson\u2019s Disease: Advancing Digital Biomarkers and Closed-Loop Interventions"],"prefix":"10.3390","volume":"26","author":[{"given":"Jiadong","family":"Jin","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongchang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yukai","family":"Zhou","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenkai","family":"Zhu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangbo","family":"Hua","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9063-1177","authenticated-orcid":false,"given":"Wen","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Nanjing University, Suzhou 215163, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Shi","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8917-7843","authenticated-orcid":false,"given":"Lijia","family":"Pan","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1038\/s41531-023-00585-y","article-title":"Overview on wearable sensors for the management of Parkinson\u2019s disease","volume":"9","author":"Moreau","year":"2023","journal-title":"npj Park. Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s11010-024-04985-3","article-title":"A crazy trio in Parkinson\u2019s disease: Metabolism alteration, \u03b1-synuclein aggregation, and oxidative stress","volume":"480","author":"Li","year":"2024","journal-title":"Mol. Cell. Biochem."},{"key":"ref_3","first-page":"14","article-title":"Prenatal Exposure to Maternal Stress and Neurodevelopmental Outcomes in Children: A Longitudinal Study with Epigenetic Analysis in Jakarta, Indonesia","volume":"2","author":"Mursyid","year":"2023","journal-title":"Sriwij. J. Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1007\/s13205-023-03553-8","article-title":"An update on pathogenesis and clinical scenario for Parkinson\u2019s disease: Diagnosis and treatment","volume":"13","author":"Adam","year":"2023","journal-title":"3 Biotech"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.parkreldis.2006.12.014","article-title":"Non-motor dysfunction in Parkinson\u2019s disease","volume":"13","author":"Ziemssen","year":"2007","journal-title":"Park. Relat. Disord."},{"key":"ref_6","first-page":"4693019","article-title":"Evaluation of Wearable Sensor Devices in Parkinson\u2019s Disease: A Review of Current Status and Future Prospects","volume":"2020","author":"Lu","year":"2020","journal-title":"Park. Dis."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Luo, L. (2022). An Overview of Freezing of Gait in Neurodegenerative Diseases. Encyclopedia of Behavioral Neuroscience, Elsevier. [2nd ed.].","DOI":"10.1016\/B978-0-12-819641-0.00148-1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Johnson, S., Kantartjis, M., Severson, J., Dorsey, R., Adams, J.L., Kangarloo, T., Kostrzebski, M.A., Best, A., Merickel, M., and Amato, D. (2024). Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson\u2019s Disease. Sensors, 24.","DOI":"10.20944\/preprints202408.1197.v1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"S468","DOI":"10.1002\/mds.22144","article-title":"Clinimetrics of freezing of gait","volume":"23","author":"Snijders","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, X., Ma, L., Wei, P., Shan, Y., Chan, P., Wang, K., and Zhao, G. (2024). Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson\u2019s disease through an interpretable machine learning model. Front. Neurol., 15.","DOI":"10.3389\/fneur.2024.1387477"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Al-Adhaileh, M.H., Wadood, A., Aldhyani, T.H.H., Khan, S., Uddin, M.I., and Al-Nefaie, A.H. (2025). Deep learning techniques for detecting freezing of gait episodes in Parkinson\u2019s disease using wearable sensors. Front. Physiol., 16.","DOI":"10.3389\/fphys.2025.1581699"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Qadri, Y.A., Ahmad, K., and Kim, S.W. (2024). Artificial General Intelligence for the Detection of Neurodegenerative Disorders. Sensors, 24.","DOI":"10.3390\/s24206658"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"242002","DOI":"10.1088\/1361-6528\/adddc5","article-title":"Advances in flexible sensor monitoring technology for neurodegenerative diseases in the elderly","volume":"36","author":"Zhu","year":"2025","journal-title":"Nanotechnology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.gaitpost.2021.10.029","article-title":"Surrogates for rigidity and PIGD MDS-UPDRS subscores using wearable sensors","volume":"91","author":"Safarpour","year":"2022","journal-title":"Gait Posture"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Habets, J.G.V., Heijmans, M., Leentjens, A.F.G., Simons, C.J.P., Temel, Y., Kuijf, M.L., Kubben, P.L., and Herff, C. (2021). A Long-Term, Real-Life Parkinson Monitoring Database Combining Unscripted Objective and Subjective Recordings. Data, 6.","DOI":"10.3390\/data6020022"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1002\/mds.29317","article-title":"Clinical Diagnostic Accuracy of Parkinson\u2019s Disease: Where Do We Stand?","volume":"38","author":"Virameteekul","year":"2023","journal-title":"Mov. Disords"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Mart\u00edn, D., and P\u00e9rez-L\u00f3pez, C. (2024). Commercial symptom monitoring devices in Parkinson\u2019s disease: Benefits, limitations, and trends. Front. Neurol., 15.","DOI":"10.3389\/fneur.2024.1470928"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"858","DOI":"10.56028\/aetr.14.1.858.2025","article-title":"Study on Flexible Wearable Sensors for Motion Detection and Health Monitoring","volume":"14","author":"Jiang","year":"2025","journal-title":"Adv. Eng. Technol. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1080\/23294515.2023.2274582","article-title":"Multi-Level Ethical Considerations of Artificial Intelligence Health Monitoring for People Living with Parkinson\u2019s Disease","volume":"15","author":"Ho","year":"2024","journal-title":"AJOB Empir. Bioeth."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5974","DOI":"10.1038\/s41467-024-49939-x","article-title":"Fabric-based lamina emergent MXene-based electrode for electrophysiological monitoring","volume":"15","author":"Lee","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Paul, S.S., and Mancini, M. (2024). Wearables for diagnosis and predicting clinical milestones. Handbook of Digital Technologies in Movement Disorders, Academic Press.","DOI":"10.1016\/B978-0-323-99494-1.00017-4"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s42114-024-00949-9","article-title":"Review: Developments and challenges of advanced flexible electronic materials for medical monitoring applications","volume":"7","author":"Zeng","year":"2024","journal-title":"Adv. Compos. Hybrid Mater."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2402010","DOI":"10.1002\/adhm.202402010","article-title":"Kirigami-Inspired Stretchable Piezoelectret Sensor for Analysis and Assessment of Parkinson\u2019s Tremor","volume":"14","author":"Xie","year":"2025","journal-title":"Adv. Healthc. Mater."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e08361","DOI":"10.1002\/smll.202508361","article-title":"Bifunctional Flexible Eutectogel: Zinc Cell Dendrite Inhibition and Machine Learning-Assisted Parkinson\u2019s Disease Monitoring","volume":"21","author":"Hu","year":"2025","journal-title":"Small"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/TBCAS.2023.3281596","article-title":"Flexible Gel-Free Multi-Modal Wireless Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait Symptom","volume":"17","author":"Hou","year":"2023","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Khan, M., and Ayyob, F. (2025). Wearable Parkinson\u2019s Tremor Monitoring Using a PNC Based Flexible Capacitive Sensor with an AC Bridge and Two-Step Calibration. Front. Eng., 1.","DOI":"10.63313\/FE.9002"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1803018","DOI":"10.1002\/smll.201803018","article-title":"A Flexible Wearable Pressure Sensor with Bioinspired Microcrack and Interlocking for Full-Range Human\u2013Machine Interfacing","volume":"14","author":"Guo","year":"2018","journal-title":"Small"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Samarentsis, A.G., Makris, G., Spinthaki, S., Christodoulakis, G., Tsiknakis, M., and Pantazis, A.K. (2022). A 3D-Printed Capacitive Smart Insole for Plantar Pressure Monitoring. Sensors, 22.","DOI":"10.3390\/s22249725"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, X., Pan, W., Wu, Z., Liu, X., Sun, Y., Fan, B., Cai, M., Li, T., and Liu, T. (2025). Evaluation of Parkinson\u2019s Disease Motor Symptoms via Wearable Inertial Measurements Units and Surface Electromyography Sensors. Bioengineering, 12.","DOI":"10.3390\/bioengineering12101116"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2206982","DOI":"10.1002\/advs.202206982","article-title":"Learning Hand Kinematics for Parkinson\u2019s Disease Assessment Using a Multimodal Sensor Glove","volume":"10","author":"Li","year":"2023","journal-title":"Adv. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Moore, A., Li, J., Contag, C.H., Currano, L.J., Pyles, C.O., Hinkle, D.A., and Patil, V.S. (2024). Wearable Surface Electromyography System to Predict Freeze of Gait in Parkinson\u2019s Disease Patients. Sensors, 24.","DOI":"10.3390\/s24237853"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108473","DOI":"10.1016\/j.bspc.2025.108473","article-title":"Shallow multi-view network for sensor-based recognition of Parkinson\u2019s freezing of gait","volume":"112","author":"Chen","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1021\/acsaenm.5c00834","article-title":"On-Foot Gait Lab: A Hybrid Wearable Sensor Platform for Spatiotemporal Gait Analysis Using Electrospun Piezocapacitive Insoles and IMUs","volume":"4","author":"Onorati","year":"2026","journal-title":"ACS Appl. Eng. Mater."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhang, X., Li, J., Pan, W., Sun, Y., Lin, S., and Liu, T. (2025). Automated UPDRS Gait Scoring Using Wearable Sensor Fusion and Deep Learning. Bioengineering, 12.","DOI":"10.3390\/bioengineering12070686"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111030","DOI":"10.1016\/j.nanoen.2025.111030","article-title":"Biocompatible, biodegradable, and high-performance flexible pressure sensors for severity grading and rehabilitation assessment in Parkinson\u2019s disease management","volume":"140","author":"Zheng","year":"2025","journal-title":"Nano Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"16744","DOI":"10.1021\/acsnano.5c01457","article-title":"Electromagnetized MXenes Enhance the Efficient Direct Reprogramming of Dopamine Neurons for Parkinson\u2019s Disease Therapy","volume":"19","author":"Kim","year":"2025","journal-title":"ACS Nano"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e09704","DOI":"10.1002\/adfm.202509704","article-title":"Additively Manufactured 3D Auxetic Metamaterials for Structurally Guided Capacitive and Resistive Tactile Sensing","volume":"35","author":"Kang","year":"2025","journal-title":"Adv. Funct. Mater."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"22035","DOI":"10.1021\/acsnano.3c08624","article-title":"Omnidirectional Configuration of Stretchable Strain Sensor Enabled by the Strain Engineering with Chiral Auxetic Metamaterial","volume":"17","author":"Hu","year":"2023","journal-title":"ACS Nano"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"112661","DOI":"10.1016\/j.diamond.2025.112661","article-title":"Daily carbon ink functionalized conductive fabric for simple, low-cost, and wide range flexible pressure sensor with multifunctional applications","volume":"158","author":"Zheng","year":"2025","journal-title":"Diam. Relat. Mater."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.sna.2019.05.006","article-title":"Flexible Paper Touchpad for Parkinson\u2019s Hand Tremor Detection","volume":"294","author":"Bhattacharjee","year":"2019","journal-title":"Sens. Actuators A Phys."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s10443-020-09834-2","article-title":"Fabrication, Design and Application of Stretchable Strain Sensors for Tremor Detection in Parkinson Patient","volume":"27","author":"Kantarak","year":"2020","journal-title":"Appl. Compos. Mater."},{"key":"ref_42","unstructured":"Vera A, D.F., He, T., Redoute, J.M., Lee, C., and Yuce, M.R. (2022, January 11\u201315). Flexible Forearm Triboelectric Sensors for Parkinson\u2019s Disease Diagnosing and Monitoring. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, UK."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e10154","DOI":"10.1002\/mds3.10154","article-title":"Stretchable triboelectric sensor for measurement of the forearm muscles movements and fingers motion for Parkinson\u2019s disease assessment and assisting technologies","volume":"4","author":"Yuce","year":"2021","journal-title":"Med. Devices Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107978","DOI":"10.1016\/j.nanoen.2022.107978","article-title":"A flexible, stretchable and triboelectric smart sensor based on graphene oxide and polyacrylamide hydrogel for high precision gait recognition in Parkinsonian and hemiplegic patients","volume":"104","author":"Wang","year":"2022","journal-title":"Nano Energy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"26472","DOI":"10.1109\/JSEN.2024.3416847","article-title":"3D-Printed Piezoelectric PLA-Based Insole for Event Detection in Gait Analysis","volume":"24","author":"Latsch","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5507","DOI":"10.1021\/acsaem.9b00677","article-title":"Introduction of Triboelectric Positive Bioplastic for Powering Portable Electronics and Self-Powered Gait Sensor","volume":"2","author":"Sarkar","year":"2019","journal-title":"ACS Appl. Energy Mater."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ren, K., Chen, Z., Ling, Y., and Zhao, J. (2022). Recognition of freezing of gait in Parkinson\u2019s disease based on combined wearable sensors. BMC Neurol., 22.","DOI":"10.1186\/s12883-022-02732-z"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"103155","DOI":"10.1016\/j.inffus.2025.103155","article-title":"AiCareGaitRehabilitation: Multi-modalities sensor data fusion for AI-IoT enabled realtime electrical stimulation device for pre-Fog and post-Fog to person with Parkinson\u2019s Disease","volume":"122","author":"Ghayvat","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"72","DOI":"10.17816\/ACEN.1001","article-title":"Spectrum of Non-Motor Symptoms in Parkinson\u2019s Disease\u2014A Review","volume":"18","author":"Rizvi","year":"2024","journal-title":"Ann. Clin. Exp. Neurol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2402508","DOI":"10.1002\/adfm.202402508","article-title":"A Fully Self-Healing Patch of Integrated Bio-Signal Monitoring Sensors with Self-Healing Microporous Foam and Au Nanosheet Electrodes","volume":"34","author":"Park","year":"2024","journal-title":"Adv. Funct. Mater."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2401151","DOI":"10.1002\/adma.202401151","article-title":"Skin-Inspired All-Natural Biogel for Bioadhesive Interface","volume":"36","author":"Lan","year":"2024","journal-title":"Adv. Mater."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sharma, A., Agarwal, A., Kalenga, M.K.W., Gupta, V., and Srivastava, V. (2026). AI in Parkinson\u2019s Disease: A Short Review of Machine Learning Approaches for Diagnosis. Processes, 14.","DOI":"10.3390\/pr14020199"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102076","DOI":"10.1016\/j.bspc.2020.102076","article-title":"Discrete wavelet transform based data representation in deep neural network for gait abnormality detection","volume":"62","author":"Chakraborty","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"117310","DOI":"10.1016\/j.measurement.2025.117310","article-title":"A new method for long-term temperature compensation of structural health monitoring by ultrasonic guided wave","volume":"252","author":"Liu","year":"2025","journal-title":"Measurement"},{"key":"ref_55","first-page":"2920255","article-title":"Abbreviated MDS-UPDRS for Remote Monitoring in PD Identified Using Exhaustive Computational Search","volume":"2022","author":"Morinan","year":"2022","journal-title":"Park. Dis."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4799","DOI":"10.1140\/epjs\/s11734-025-01722-9","article-title":"Leveraging wavelet scattering transform on accelerometry data for classification of Parkinson\u2019s tremor","volume":"234","year":"2025","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"28050","DOI":"10.1038\/s41598-024-79111-w","article-title":"A multimodal fusion network based on a cross-attention mechanism for the classification of Parkinsonian tremor and essential tremor","volume":"14","author":"Tang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5561","DOI":"10.1038\/s41598-024-82027-0","article-title":"Early detection of Parkinson\u2019s disease using a multi area graph convolutional network","volume":"15","author":"Huo","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1021\/acssensors.3c02555","article-title":"Current On-Skin Flexible Sensors, Materials, Manufacturing Approaches, and Study Trends for Health Monitoring: A Review","volume":"9","author":"Ferreira","year":"2024","journal-title":"ACS Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e71560","DOI":"10.2196\/71560","article-title":"Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait","volume":"27","author":"Qi","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"120541","DOI":"10.1016\/j.eswa.2023.120541","article-title":"Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson\u2019s disease: A systematic review","volume":"229","author":"Sigcha","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/JTEHM.2025.3611498","article-title":"Diagnosis and Severity Rating of Parkinson\u2019s Disease Based on Multimodal Gait Signal Analysis With GLRT and ST-CNN-Transformer Networks","volume":"13","author":"Ji","year":"2025","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"110138","DOI":"10.1016\/j.compbiomed.2025.110138","article-title":"A new lightweight deep learning model optimized with pruning and dynamic quantization to detect freezing gait on wearable devices","volume":"191","author":"Yi","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"He, Y., Yang, T., Yang, C., and Zhou, H. (2022). Integrated Equipment for Parkinson\u2019s Disease Early Detection Using Graph Convolution Network. Electronics, 11.","DOI":"10.3390\/electronics11071154"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ozkoc, E., Zech, T.S., Pfeiffer, N., G\u00f6b, S., and Frickel, J. (September, January 31). Compressed and Lightweight CNN for Real-Time Parkinson\u2019s Tremor Detection from Wearable IMU Data. Proceedings of the 2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP), Istanbul, Turkey.","DOI":"10.1109\/MLSP62443.2025.11204316"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"110530","DOI":"10.1016\/j.compeleceng.2025.110530","article-title":"Edge-based freezing of gait recognition in Parkinson\u2019s disease","volume":"127","author":"Sigcha","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Twala, B. (2025). AI-driven precision diagnosis and treatment in Parkinson\u2019s disease: A comprehensive review and experimental analysis. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1638340"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"200","DOI":"10.3934\/Neuroscience.2023017","article-title":"Depletion of dopamine in Parkinson\u2019s disease and relevant therapeutic options: A review of the literature","volume":"10","author":"Ramesh","year":"2023","journal-title":"AIMS Neurosci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"16231","DOI":"10.1021\/acsanm.5c02646","article-title":"Review of MXene-Based Electrospun Nanocomposites for Flexible and Wearable Sensors","volume":"8","author":"Khadem","year":"2025","journal-title":"ACS Appl. Nano Mater."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Slemen\u0161ek, J., Ger\u0161ak, J., Bratina, B., Van Midden, V.M., Pirto\u0161ek, Z., and \u0160afari\u010d, R. (2024). Wearable Online Freezing of Gait Detection and Cueing System. Bioengineering, 11.","DOI":"10.3390\/bioengineering11101048"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Elbatanouny, H., Kleanthous, N., Dahrouj, H., Alusi, S., Almajali, E., Mahmoud, S., and Hussain, A. (2024). Insights into Parkinson\u2019s Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis. Sensors, 24.","DOI":"10.3390\/s24123959"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"e53008","DOI":"10.2196\/53008","article-title":"Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges","volume":"26","author":"Chen","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Rabbani, S.A., El-Tanani, M., Sharma, S., Rabbani, S.S., El-Tanani, Y., Kumar, R., and Saini, M. (2025). Generative Artificial Intelligence in Healthcare: Applications, Implementation Challenges, and Future Directions. BioMedInformatics, 5.","DOI":"10.3390\/biomedinformatics5030037"},{"key":"ref_75","first-page":"1","article-title":"Generative artificial intelligence in public health research and scientific communication: A narrative review of real applications and future directions","volume":"11","author":"Ratnam","year":"2025","journal-title":"Digit. Health"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/7\/2071\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T13:55:42Z","timestamp":1774533342000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/7\/2071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,26]]},"references-count":75,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26072071"],"URL":"https:\/\/doi.org\/10.3390\/s26072071","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,26]]}}}