{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T11:05:48Z","timestamp":1783940748079,"version":"3.55.0"},"reference-count":54,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T00:00:00Z","timestamp":1783728000000},"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>Background: Artificial intelligence (AI)-driven gait analysis using plantar pressure and ground reaction force (GRF) signals may provide objective digital biomarkers for rehabilitation, but clinical translation remains uncertain. This scoping review and evidence map aimed to summarize clinical applications, compare evidence maturity, and identify methodological and translational gaps. Methods: PubMed, Web of Science, Embase, and Scopus were searched from the earliest available indexed records in each database to May 2026. Original clinical studies using plantar pressure- or GRF-derived signals with AI methods for disease recognition, severity assessment, risk prediction, rehabilitation monitoring, or decision support were included. Results: Fifteen studies met the eligibility criteria. Evidence was concentrated in Parkinson\u2019s disease (PD), particularly PD recognition and freezing of gait prediction, where relatively more mature evidence was supported by multiple studies and participant-level or cross-dataset validation. Evidence for PD severity assessment, knee osteoarthritis monitoring, chronic ankle instability rehabilitation, fall-risk stratification, sarcopenia screening, peripheral artery disease recognition, and functional gait disorder classification remained less mature. Translation was limited by small or single-center samples, unclear participant-level data splitting, limited external validation, absent calibration, sparse explainable AI reporting, and insufficient real-world workflow testing. Conclusions: Future studies should prioritize prospective, externally validated, interpretable, calibrated, and clinically embedded models before routine rehabilitation implementation.<\/jats:p>","DOI":"10.3390\/bioengineering13070796","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T09:51:09Z","timestamp":1783936269000},"page":"796","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical Translation of Artificial Intelligence-Driven Gait Analysis Using Plantar Pressure and Ground Reaction Force"],"prefix":"10.3390","volume":"13","author":[{"given":"Junxiao","family":"Yang","sequence":"first","affiliation":[{"name":"School of Nursing, Jining Medical University, Jining 272000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2508-1644","authenticated-orcid":false,"given":"Chunli","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Nursing, Jining Medical University, Jining 272000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Nursing, Jining Medical University, Jining 272000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Nursing, Jining Medical University, Jining 272000, China"},{"name":"Xiangya School of Nursing, Central South University, Changsha 410013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiya","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Nursing, Jining Medical University, Jining 272000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s00508-016-1096-4","article-title":"Gait disorders in adults and the elderly: A clinical guide","volume":"129","author":"Pirker","year":"2017","journal-title":"Wien. Klin. Wochenschr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/1743-0003-3-4","article-title":"Gait analysis methods in rehabilitation","volume":"3","author":"Baker","year":"2006","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1016\/j.jbiomech.2004.02.047","article-title":"Quantification of human motion: Gait analysis-benefits and limitations to its application to clinical problems","volume":"37","author":"Simon","year":"2004","journal-title":"J. Biomech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.gaitpost.2014.02.001","article-title":"Summary measures for clinical gait analysis: A literature review","volume":"39","author":"Cimolin","year":"2014","journal-title":"Gait Posture"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.gaitpost.2024.04.007","article-title":"Machine learning applied to gait analysis data in cerebral palsy and stroke: A systematic review","volume":"111","author":"Merlo","year":"2024","journal-title":"Gait Posture"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e58261","DOI":"10.2196\/58261","article-title":"Functional Monitoring of Patients With Knee Osteoarthritis Based on Multidimensional Wearable Plantar Pressure Features: Cross-Sectional Study","volume":"7","author":"Xie","year":"2024","journal-title":"JMIR Aging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1093\/ptj\/80.4.399","article-title":"Plantar pressure assessment","volume":"80","author":"Orlin","year":"2000","journal-title":"Phys. Ther."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9884","DOI":"10.3390\/s120709884","article-title":"Foot plantar pressure measurement system: A review","volume":"12","author":"Zayegh","year":"2012","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/S1474-4422(19)30397-7","article-title":"Long-term unsupervised mobility assessment in movement disorders","volume":"19","author":"Warmerdam","year":"2020","journal-title":"Lancet Neurol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/RBME.2017.2747402","article-title":"A Review in Detection and Monitoring Gait Disorders Using In-Shoe Plantar Measurement Systems","volume":"10","year":"2017","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41597-020-0481-z","article-title":"GaiTRec, a large-scale ground reaction force dataset of healthy and impaired gait","volume":"7","author":"Horsak","year":"2020","journal-title":"Sci. Data"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e29672","DOI":"10.1016\/j.heliyon.2024.e29672","article-title":"In-shoe plantar pressure measurement technologies for the diabetic foot: A systematic review","volume":"10","author":"Marques","year":"2024","journal-title":"Heliyon"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.bbe.2024.01.005","article-title":"A systematic review of artificial neural network techniques for analysis of foot plantar pressure","volume":"44","author":"Wang","year":"2024","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1038\/s41598-024-83357-9","article-title":"Gait-based Parkinson\u2019s disease diagnosis and severity classification using force sensors and machine learning","volume":"15","author":"Navita","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s12938-022-00992-x","article-title":"Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway","volume":"21","author":"Hu","year":"2022","journal-title":"Biomed. Eng. Online"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s12984-024-01337-6","article-title":"Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices","volume":"21","author":"Li","year":"2024","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Haque, F., Reaz, M.B.I., Chowdhury, M.E.H., Ezeddin, M., Kiranyaz, S., Alhatou, M., Ali, S.H.M., Bakar, A.A.A., and Srivastava, G. (2022). Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait. Sensors, 22.","DOI":"10.3390\/s22093507"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1016\/j.apmr.2024.08.006","article-title":"Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome in Acute Ischemic Stroke","volume":"105","author":"Jeon","year":"2024","journal-title":"Arch. Phys. Med. Rehabil."},{"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","first-page":"734","DOI":"10.1016\/S1474-4422(11)70143-0","article-title":"Freezing of gait: Moving forward on a mysterious clinical phenomenon","volume":"10","author":"Nutt","year":"2011","journal-title":"Lancet Neurol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/S1474-4422(15)00041-1","article-title":"Freezing of gait: A practical approach to management","volume":"14","author":"Nonnekes","year":"2015","journal-title":"Lancet Neurol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1016\/S0140-6736(19)30417-9","article-title":"Osteoarthritis","volume":"393","author":"Hunter","year":"2019","journal-title":"Lancet"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e131","DOI":"10.1161\/CIR.0000000000001251","article-title":"2024 ACC\/AHA\/AACVPR\/APMA\/ABC\/SCAI\/SVM\/SVN\/SVS\/SIR\/VESS Guideline for the Management of Lower Extremity Peripheral Artery Disease: A Report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","volume":"149","author":"Gornik","year":"2024","journal-title":"Circulation"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1093\/ageing\/afy169","article-title":"Sarcopenia: Revised European consensus on definition and diagnosis","volume":"48","author":"Bahat","year":"2019","journal-title":"Age Ageing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"afac205","DOI":"10.1093\/ageing\/afac205","article-title":"World guidelines for falls prevention and management for older adults: A global initiative","volume":"51","author":"Martin","year":"2022","journal-title":"Age Ageing"},{"key":"ref_28","first-page":"1","article-title":"Machine Vision System Measuring the Trajectory of Upper Limb Motion Applying the Matlab Software","volume":"19","author":"Cyganiuk","year":"2019","journal-title":"Meas. Sci. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.proeng.2016.06.685","article-title":"Mobile robot controlling possibilities of inertial navigation system","volume":"149","author":"Qazizada","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kuric, I., Klar\u00e1k, J., Bulej, V., S\u00e1ga, M., Kandera, M., Hajdu\u010d\u00edk, A., and Tucki, K. (2022). Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence. Appl. Sci., 12.","DOI":"10.3390\/app12020864"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"B\u0159ezina, T., and Jablo\u0144ski, R. (2014). Diagnostics of Mechatronic Systems on the Basis of Neural Networks with High-Performance Data Collection. Mechatronics 2013, Springer International Publishing.","DOI":"10.1007\/978-3-319-02294-9"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Munn, Z., Peters, M.D.J., Stern, C., Tufanaru, C., McArthur, A., and Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med. Res. Methodol., 18.","DOI":"10.1186\/s12874-018-0611-x"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.11124\/JBIES-20-00167","article-title":"Updated methodological guidance for the conduct of scoping reviews","volume":"18","author":"Peters","year":"2020","journal-title":"JBI Evid. Synth."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Pardoel, S., Shalin, G., Nantel, J., Lemaire, E.D., and Kofman, J. (2021). Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data. Sensors, 21.","DOI":"10.3390\/s21062246"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pardoel, S., Nantel, J., Kofman, J., and Lemaire, E.D. (2022). Prediction of Freezing of Gait in Parkinson\u2019s Disease Using Unilateral and Bilateral Plantar-Pressure Data. Front. Neurol., 13.","DOI":"10.3389\/fneur.2022.831063"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pardoel, S., AlAkhras, A., Jafari, E., Kofman, J., Lemaire, E.D., and Nantel, J. (2024). Real-Time Freezing of Gait Prediction and Detection in Parkinson\u2019s Disease. Sensors, 24.","DOI":"10.3390\/s24248211"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1186\/s12984-021-00958-5","article-title":"Prediction and detection of freezing of gait in Parkinson\u2019s disease from plantar pressure data using long short-term memory neural-networks","volume":"18","author":"Shalin","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/JBHI.2025.3589772","article-title":"VGRF Signal-Based Gait Analysis for Parkinson\u2019s Disease Detection: A Multi-Scale Directed Graph Neural Network Approach","volume":"30","author":"Wang","year":"2026","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1978","DOI":"10.1109\/TNSRE.2025.3563924","article-title":"Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor System","volume":"33","author":"Guo","year":"2025","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e86132","DOI":"10.7554\/eLife.86132","article-title":"Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning","volume":"13","author":"Wipperman","year":"2024","journal-title":"eLife"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Al-Ramini, A., Hassan, M., Fallahtafti, F., Takallou, M.A., Rahman, H., Qolomany, B., Pipinos, I.I., Alsaleem, F., and Myers, S.A. (2022). Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data. Sensors, 22.","DOI":"10.3390\/s22197432"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Naseem, M.T., Kim, N.H., Seo, H., Lee, J., Chung, C.M., Shin, S., and Lee, C.S. (2024). Sarcopenia diagnosis using skeleton-based gait sequence and foot-pressure image datasets. Front. Public Health, 12.","DOI":"10.3389\/fpubh.2024.1443188"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/s12984-025-01599-8","article-title":"Wearable fall risk assessment by discriminating recessive weak foot individual","volume":"22","author":"Song","year":"2025","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"110818","DOI":"10.1016\/j.jbiomech.2021.110818","article-title":"Analysis of the stance phase of the gait cycle in Parkinson\u2019s disease and its potency for Parkinson\u2019s disease discrimination","volume":"129","author":"Farashi","year":"2021","journal-title":"J. Biomech."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1109\/JBHI.2017.2785682","article-title":"Automatic Classification of Functional Gait Disorders","volume":"22","author":"Slijepcevic","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1109\/TNSRE.2026.3664483","article-title":"A Wearable \u201cLab-in-Shoe\u201d Gait Analysis System for Routine Clinical Assessment of People With Parkinson\u2019s Disease","volume":"34","author":"Huang","year":"2026","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"q902","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Alharthi, A.S. (2024). Interpretable machine learning comprehensive human gait deterioration analysis. Front. Neuroinform., 18.","DOI":"10.3389\/fninf.2024.1451529"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1123\/jsr.2024-0269","article-title":"Minimizing running load via gentle heel strike techniques: A gait modification study","volume":"34","author":"Shen","year":"2025","journal-title":"J. Sport Rehabil."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1080\/10255842.2025.2465354","article-title":"Assessment of plantar pressure dynamics in firefighters carrying self-contained breathing apparatus: A computational approach to gait stability analysis","volume":"29","author":"Xie","year":"2026","journal-title":"Comput. Methods Biomech. Biomed. Engin."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e070904","DOI":"10.1136\/bmj-2022-070904","article-title":"Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"377","author":"Vasey","year":"2022","journal-title":"BMJ"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/7\/796\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:15:49Z","timestamp":1783937749000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/7\/796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,11]]},"references-count":54,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["bioengineering13070796"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13070796","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,11]]}}}