{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T12:01:01Z","timestamp":1790164861408,"version":"4.1.0"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1A2C100933711"],"award-info":[{"award-number":["2022R1A2C100933711"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"award":["2022R1A2C100933711"],"award-info":[{"award-number":["2022R1A2C100933711"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Basic Science Research Program through the NRF","doi-asserted-by":"publisher","award":["2022R1A6A3A0108756411"],"award-info":[{"award-number":["2022R1A6A3A0108756411"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Education of the Republic of Korea and NRF","doi-asserted-by":"publisher","award":["2024S1A5B5A16021673"],"award-info":[{"award-number":["2024S1A5B5A16021673"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Ministry of Education of the Republic of Korea and NRF","doi-asserted-by":"publisher","award":["2025S1A5B5A16007988"],"award-info":[{"award-number":["2025S1A5B5A16007988"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Neuromuscular dysfunction during the sit-to-walk (STW) task in Parkinson\u2019s disease (PD) remains poorly understood. We aimed to analyze surface electromyography (sEMG) signals across STW phases using explainable machine learning (ML) and deep learning approaches in PD. Individuals with PD (n = 101) and healthy controls (n = 50) performed a standardized STW task while sEMG signals were recorded bilaterally from eight lower-limb muscles. These signals were preprocessed and segmented into three phases in the STW task. Feature-based ML models were compared with convolutional neural networks (CNN) trained on wavelet-transformed sEMG spectrograms. Explainable artificial intelligence methods identified physiologically interpretable neuromuscular patterns. STW phase 1 was the most sensitive interval for detecting PD-related neuromuscular abnormalities. Among the feature-based classifiers, the random forest model demonstrated the highest performance under five-fold cross-validation, with an accuracy of 77.5% and an area under the receiver operating characteristic curveof 0.795. SHapley Additive exPlanations analysis identified mean and median frequency during STW phase 1, left biceps femoris integrated sEMG amplitude, and left rectus femoris\u2013biceps femoris short head co-contraction as major contributors to classification. CNN-based analyses revealed earlier and temporally concentrated activation patterns in individuals with PD, particularly in proximal muscles critical for momentum generation and postural stabilization. Overall, individuals with PD exhibited distinct temporal, spectral, and coordinative patterns of lower-limb neuromuscular activity during the STW transition. The findings support the potential of sEMG-based explainable AI for characterizing PD-associated neuromuscular patterns during functional transitions.<\/jats:p>","DOI":"10.3390\/s26185909","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T09:04:17Z","timestamp":1790067857000},"page":"5909","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Altered Neuromuscular Time\u2013Frequency Organization During Sit-to-Walk in Parkinson\u2019s Disease: An Explainable Artificial Intelligence Approach Using Surface Electromyography"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4625-9021","authenticated-orcid":false,"given":"Hwayoung","family":"Park","sequence":"first","affiliation":[{"name":"Biomechanics Laboratory, Dong-A University, Saha-gu, Busan 49315, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7574-5259","authenticated-orcid":false,"given":"Changhong","family":"Youm","sequence":"additional","affiliation":[{"name":"Biomechanics Laboratory, Dong-A University, Saha-gu, Busan 49315, Republic of Korea"},{"name":"Department of Health Sciences, Graduate School, Dong-A University, Saha-gu, Busan 49315, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5905-2188","authenticated-orcid":false,"given":"Sang-Myung","family":"Cheon","sequence":"additional","affiliation":[{"name":"Department of Neurology, School of Medicine, Dong-A University, Seo-gu, Busan 49315, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0012-5615","authenticated-orcid":false,"given":"Bohyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Biomechanics Laboratory, Dong-A University, Saha-gu, Busan 49315, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Juseon","family":"Hwang","sequence":"additional","affiliation":[{"name":"Biomechanics Laboratory, Dong-A University, Saha-gu, Busan 49315, Republic of Korea"},{"name":"Department of Health Sciences, Graduate School, Dong-A University, Saha-gu, Busan 49315, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9033-9843","authenticated-orcid":false,"given":"Minsoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Biomechanics Laboratory, Dong-A University, Saha-gu, Busan 49315, Republic of Korea"},{"name":"Department of Health Sciences, Graduate School, Dong-A University, Saha-gu, Busan 49315, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11660","DOI":"10.1038\/s41598-025-95315-0","article-title":"A quantum inspired machine learning approach for multimodal Parkinson\u2019s disease screening","volume":"15","author":"Vatsavai","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.neuroscience.2010.12.060","article-title":"Differential involvement of striato- and cerebello-thalamo-cortical pathways in tremor- and akinetic\/rigid-predominant Parkinson\u2019s disease","volume":"177","author":"Lewis","year":"2011","journal-title":"Neuroscience"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","article-title":"Parkinson\u2019s disease","volume":"397","author":"Bloem","year":"2021","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2399","DOI":"10.1109\/TNSRE.2023.3277749","article-title":"Multi-modal deep learning diagnosis of Parkinson\u2019s disease\u2014A systematic review","volume":"31","author":"Skaramagkas","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1038\/s41746-024-01236-z","article-title":"Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson\u2019s disease","volume":"7","author":"Brzenczek","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1109\/TNNLS.2021.3052652","article-title":"Parkinson\u2019s disease classification and clinical score regression via united embedding and sparse learning from longitudinal data","volume":"33","author":"Huang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1002\/mds.25383","article-title":"How to identify tremor dominant and postural instability\/gait difficulty groups with the movement disorder society unified Parkinson\u2019s disease rating scale: Comparison with the unified Parkinson\u2019s disease rating scale","volume":"28","author":"Stebbins","year":"2013","journal-title":"Mov. Disord."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lanzani, V., Brambilla, C., and Scano, A. (2024). A methodological scoping review on EMG processing and synergy-based results in muscle synergy studies in Parkinson\u2019s disease. Front. Bioeng. Biotechnol., 12.","DOI":"10.3389\/fbioe.2024.1445447"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Romanato, M., Strazza, A., Piatkowska, W.J., Spolaor, F., Fioretti, S., Volpe, D., Sawacha, Z., and Di Nardo, F. (2021, January 23\u201325). Characterization of EMG time-frequency content during Parkinson walking: A pilot study. Proceedings of the 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Lausanne, Switzerland.","DOI":"10.1109\/MeMeA52024.2021.9478707"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"11687","DOI":"10.1038\/s41598-025-96575-6","article-title":"Explainable artificial intelligence to diagnose early Parkinson\u2019s disease via voice analysis","volume":"15","author":"Shen","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"16555","DOI":"10.1038\/s41598-023-43728-0","article-title":"Age-related differences in lower limb muscle activation patterns and balance control strategies while walking over a compliant surface","volume":"13","author":"Jeon","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.gaitpost.2024.12.028","article-title":"Two-dimensional identification of lower limb gait features based on the variational modal decomposition of sEMG signal and convolutional neural network","volume":"117","author":"Liu","year":"2025","journal-title":"Gait Posture"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fricke, C., Alizadeh, J., Zakhary, N., Woost, T.B., Bogdan, M., and Classen, J. (2021). Evaluation of three machine learning algorithms for the automatic classification of EMG patterns in gait disorders. Front. Neurol., 12.","DOI":"10.3389\/fneur.2021.666458"},{"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":"633","DOI":"10.3988\/jcn.2020.16.4.633","article-title":"Validation study of the official Korean version of the Movement Disorder Society-unified Parkinson\u2019s disease rating scale","volume":"16","author":"Park","year":"2020","journal-title":"J. Clin. Neurol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1212\/WNL.17.5.427","article-title":"Parkinsonism: Onset, progression, and mortality","volume":"17","author":"Hoehn","year":"1967","journal-title":"Neurology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1002\/mds.27242","article-title":"members of the QUALPD Study Group. Validation study of the Hoehn and yahr scale included in the MDS-UPDRS","volume":"33","author":"Skorvanek","year":"2018","journal-title":"Mov. Disord."},{"key":"ref_21","unstructured":"Biagini, C. (2021). Muscle Co-contractions Estimated During Walking in Parkinson Disease by Analyzing the Surface EMG Signal in Time-Frequency Domain. [Master\u2019s Thesis, University of Politecnica delle Marche]."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lang, K.C., Hackney, M.E., Ting, L.H., and McKay, J.L. (2019). Antagonist muscle activity during reactive balance responses is elevated in Parkinson\u2019s disease and in balance impairment. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211137"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1186\/s12984-021-00932-1","article-title":"Muscle activation strategies of people with early-stage Parkinson\u2019s during walking","volume":"18","author":"Keloth","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1002\/mds.22079","article-title":"Dynamic postural stability during sit-to-walk transitions in Parkinson disease patients","volume":"23","author":"Buckley","year":"2008","journal-title":"Mov. Disord."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.clinbiomech.2019.03.002","article-title":"Sit-to-walk performance in Parkinson\u2019s disease: A comparison between faller and non-faller patients","volume":"63","author":"Palmisano","year":"2019","journal-title":"Clin. Biomech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.gaitpost.2024.07.010","article-title":"Brain (EEG) and muscle (EMG) activity related to 3D sit-to-stand kinematics in healthy adults and in central neurological pathology\u2013A systematic review","volume":"113","author":"McDonald","year":"2024","journal-title":"Gait Posture"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1109\/JTEHM.2023.3272796","article-title":"Parkinson\u2019s disease diagnosis with gait characteristics extracted using wavelet transforms","volume":"11","author":"Vimalajeewa","year":"2023","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.gaitpost.2025.01.012","article-title":"Sensory reweighting for balance in people living with Parkinson\u2019s disease: Postural adaptation, muscle co-contraction, and perceptual delays","volume":"117","author":"McDonnell","year":"2025","journal-title":"Gait Posture"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bailey, C.A., Corona, F., Murgia, M., Pili, R., Pau, M., and C\u00f4t\u00e9, J.N. (2018). Electromyographical gait characteristics in Parkinson\u2019s disease: Effects of combined physical therapy and rhythmic auditory stimulation. Front. Neurol., 9.","DOI":"10.3389\/fneur.2018.00211"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1152\/jn.00030.2015","article-title":"Adaptation of multijoint coordination during standing balance in healthy young and healthy old individuals","volume":"115","author":"Engelhart","year":"2016","journal-title":"J. Neurophysiol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Fern\u00e1ndez, L.P. (2024). Biomechanics of Parkinson\u2019s disease with systems based on expert knowledge and machine learning: A scoping review. Computation, 12.","DOI":"10.3390\/computation12110230"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pradon, D., Tong, L., Chalitsios, C., and Roche, N. (2024). Development of surface EMG for gait analysis and rehabilitation of hemiparetic patients. Sensors, 24.","DOI":"10.20944\/preprints202407.0021.v1"},{"key":"ref_33","first-page":"1675","article-title":"Pre-movement cortico-muscular dynamics underlying improved Parkinson gait initiation after instructed arm swing","volume":"10","author":"Weersink","year":"2020","journal-title":"J. Park. Dis."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12984-025-01570-7","article-title":"Sway frequencies may predict postural instability in Parkinson\u2019s disease: A novel convolutional neural network approach","volume":"22","author":"Engel","year":"2025","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kim, J.K., Bae, M.N., Lee, K., Kim, J.C., and Hong, S.G. (2022). Explainable artificial intelligence and wearable sensor-based gait analysis to identify patients with osteopenia and sarcopenia in daily life. Biosensors, 12.","DOI":"10.3390\/bios12030167"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Miyashita, T., Kawanishi, K., and Kudo, S. (2025). Relationship between lower-extremity co-contraction and jerk during gait. Sensors, 25.","DOI":"10.3390\/s25072327"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Aquino, G., Costa, M.G.F., and Costa Filho, C.F.F. (2022). Explaining one-dimensional convolutional models in human activity recognition and biometric identification tasks. Sensors, 22.","DOI":"10.3390\/s22155644"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shin, H. (2022). Deep convolutional neural network-based hemiplegic gait detection using an inertial sensor located freely in a pocket. Sensors, 22.","DOI":"10.3390\/s22051920"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7337","DOI":"10.1038\/s41598-025-92105-6","article-title":"Pre-trained convolutional neural networks identify Parkinson\u2019s disease from spectrogram images of voice samples","volume":"15","author":"Rahmatallah","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1109\/TNSRE.2024.3383857","article-title":"Continuous motion intention prediction using sEMG for upper-limb rehabilitation: A systematic review of model-based and model-free approaches","volume":"32","author":"Wei","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5909\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:12:13Z","timestamp":1790161933000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5909"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,18]]},"references-count":40,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185909"],"URL":"https:\/\/doi.org\/10.3390\/s26185909","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,18]]}}}