{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T16:02:19Z","timestamp":1781798539608,"version":"3.54.5"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s11517-025-03372-4","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T01:40:42Z","timestamp":1746495642000},"page":"2871-2887","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A deep learning model with interpretable squeeze-and-excitation for automated rehabilitation exercise assessment"],"prefix":"10.1007","volume":"63","author":[{"given":"Md. Johir","family":"Raihan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Atiqur Rahman","family":"Ahad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2391-5767","authenticated-orcid":false,"given":"Abdullah-Al","family":"Nahid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"key":"3372_CR1","unstructured":"UI - PRMD. https:\/\/webpages.uidaho.edu\/ui-prmd\/"},{"key":"3372_CR2","doi-asserted-by":"crossref","unstructured":"Bach Sebastian, Binder Alexander, Montavon Gr\u00e9goire, Klauschen Frederick, M\u00fcller Klaus-Robert, Samek Wojciech (2015) On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10(7):e0130140","DOI":"10.1371\/journal.pone.0130140"},{"issue":"1","key":"3372_CR3","first-page":"27","volume":"27","author":"Roberta Balestrino and AHV Schapira","year":"2020","unstructured":"Roberta Balestrino and AHV Schapira (2020) Parkinson disease. European J Neuro 27(1):27\u201342","journal-title":"Parkinson disease. European J Neuro"},{"key":"3372_CR4","doi-asserted-by":"crossref","unstructured":"Bernhardt J, Urimubenshi G, Gandhi D\u00a0BC, Eng J\u00a0J (2020) Stroke rehabilitation in low-income and middle-income countries: a call to action. Lancet 396(10260):1452\u20131462","DOI":"10.1016\/S0140-6736(20)31313-1"},{"key":"3372_CR5","doi-asserted-by":"crossref","unstructured":"Bibaeva V (2018) Using metaheuristics for hyper-parameter optimization of convolutional neural networks. In: 2018 IEEE 28Th international workshop on machine learning for signal processing (MLSP), IEEE, pp 1\u20136","DOI":"10.1109\/MLSP.2018.8516989"},{"key":"3372_CR6","doi-asserted-by":"crossref","unstructured":"Bloem B\u00a0R, Okun M\u00a0S, Klein C (2021) Parkinson\u2019s disease. The Lancet, 397(10291):2284\u20132303","DOI":"10.1016\/S0140-6736(21)00218-X"},{"key":"3372_CR7","doi-asserted-by":"crossref","unstructured":"Buhagiar M\u00a0A, Naylor J\u00a0M, Harris I\u00a0A, Xuan W, Adie S, Lewin A (2019) Assessment of outcomes of inpatient or clinic-based vs home-based rehabilitation after total knee arthroplasty: a systematic review and meta-analysis. JAMA Netw Open 2(4):e192810\u2013e192810","DOI":"10.1001\/jamanetworkopen.2019.2810"},{"key":"3372_CR8","doi-asserted-by":"crossref","unstructured":"Capecci M, Ceravolo M\u00a0G, Ferracuti F, Iarlori S, Monteriu A, Romeo L, Verdini F (2019) The KIMORE dataset: Kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Trans Neural Syst Rehab Eng 27(7):1436\u20131448","DOI":"10.1109\/TNSRE.2019.2923060"},{"key":"3372_CR9","doi-asserted-by":"crossref","unstructured":"Chen J, Koju W, Xu S, Liu Z (2021) Sales forecasting using deep neural network and SHAP techniques. In: 2021 IEEE 2nd international conference on big data, artificial intelligence and internet of things engineering (ICBAIE), IEEE, pp 135\u2013138","DOI":"10.1109\/ICBAIE52039.2021.9389930"},{"key":"3372_CR10","doi-asserted-by":"crossref","unstructured":"Deb S, Islam Md\u00a0F, Rahman S, Rahman S (2022) Graph convolutional networks for assessment of physical rehabilitation exercises. IEEE Trans Neural Syst Rehab Eng 30:410\u2013419","DOI":"10.1109\/TNSRE.2022.3150392"},{"key":"3372_CR11","doi-asserted-by":"crossref","unstructured":"Dolatabadi E, Zhi Y\u00a0X, Ye B, Coahran M, Lupinacci G, Mihailidis A, Wang R, Taati B (2017) The Toronto rehab stroke pose dataset to detect compensation during stroke rehabilitation therapy. In ACM Other conferences, pp 375\u2013381. Association for Computing Machinery, New York, NY, USA, May 2017","DOI":"10.1145\/3154862.3154925"},{"key":"3372_CR12","doi-asserted-by":"crossref","unstructured":"Du Y, Wang W, Wang L (2015) Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1110\u20131118","DOI":"10.1109\/CVPR.2015.7298714"},{"issue":"7","key":"3372_CR13","doi-asserted-by":"publisher","first-page":"3332","DOI":"10.3390\/app12073332","volume":"12","author":"K Duraj","year":"2022","unstructured":"Duraj K, Piaseczna N, Kostka P, Tkacz E (2022) Semantic segmentation of 12-lead ECG using 1D residual U-net with squeeze-excitation blocks. Appl Sci 12(7):3332","journal-title":"Appl Sci"},{"key":"3372_CR14","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3372_CR15","doi-asserted-by":"crossref","unstructured":"Jabeur S\u00a0B, Mefteh-Wali S, Viviani J-L (2024) Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annal Operation Res 334(1):679\u2013699","DOI":"10.1007\/s10479-021-04187-w"},{"issue":"1","key":"3372_CR16","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.cnc.2019.11.004","volume":"32","author":"Maureen Le Danseur","year":"2020","unstructured":"Le Danseur Maureen (2020) Stroke rehabilitation. Critical care nursing clinics of North America 32(1):97\u2013108","journal-title":"Critical care nursing clinics of North America"},{"key":"3372_CR17","doi-asserted-by":"crossref","unstructured":"LeCun Y, Boser B, Denker J\u00a0S, Henderson D, Howard R\u00a0E, Hubbard W, Jackel L\u00a0D (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551","DOI":"10.1162\/neco.1989.1.4.541"},{"issue":"11","key":"3372_CR18","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceed IEEE 86(11):2278\u20132324","journal-title":"Proceed IEEE"},{"key":"3372_CR19","doi-asserted-by":"crossref","unstructured":"Lee M\u00a0H, Siewiorek D\u00a0P, Smailagic A, Bernardino A, Berm\u00fadez\u00a0i Badia S (2020) Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment. Proceed ACM Human-Comput Interact, 4(CSCW2):1\u201327","DOI":"10.1145\/3415227"},{"key":"3372_CR20","doi-asserted-by":"crossref","unstructured":"Li C, Zhong Q, Xie D, Pu S (2018) Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. arXiv:1804.06055","DOI":"10.24963\/ijcai.2018\/109"},{"key":"3372_CR21","doi-asserted-by":"crossref","unstructured":"Li D, Sun T, Xue Y, Xie Y, Chen X, Nan J (2022) Detection of premature ventricular contractions using 12-lead dynamic ECG based on squeeze-excitation residual network. Int J Adv Comput Sci Appl 13(7)","DOI":"10.14569\/IJACSA.2022.0130702"},{"issue":"2","key":"3372_CR22","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1109\/TNSRE.2020.2966249","volume":"28","author":"Y Liao","year":"2020","unstructured":"Liao Y, Vakanski A, Xian M (2020) A deep learning framework for assessing physical rehabilitation exercises. IEEE Trans Neural Syst Rehab Eng 28(2):468\u2013477","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"key":"3372_CR23","unstructured":"Lundberg S\u00a0M, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30"},{"key":"3372_CR24","doi-asserted-by":"crossref","unstructured":"Malik A, Tikhamarine Y, Souag-Gamane D, Kisi O, Pham Q\u00a0B (2020) Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction. Stoch Environ Res Risk Assess 34:1755\u20131773","DOI":"10.1007\/s00477-020-01874-1"},{"key":"3372_CR25","doi-asserted-by":"crossref","unstructured":"Mirjalili S, Mirjalili S\u00a0M, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","DOI":"10.1016\/j.advengsoft.2013.12.007"},{"issue":"5","key":"3372_CR26","doi-asserted-by":"publisher","first-page":"46","DOI":"10.3390\/data6050046","volume":"6","author":"A Miron","year":"2021","unstructured":"Miron A, Sadawi N, Ismail W, Hussain H, Grosan C (2021) IntelliRehabDS (IRDS)\u2013a dataset of physical rehabilitation movements. Data 6(5):46","journal-title":"Data"},{"key":"3372_CR27","doi-asserted-by":"crossref","unstructured":"Nguyen S\u00a0M, Devanne M, Remy-Neris O, Lempereur M, Thepaut A (2024) A medical low-back pain physical rehabilitation dataset for human body movement analysis. arXiv, June 2024","DOI":"10.1109\/IJCNN60899.2024.10650036"},{"key":"3372_CR28","doi-asserted-by":"crossref","unstructured":"Paolucci T, Attanasi C, Cecchini W, Marazzi A, Capobianco S\u00a0V, Santilli V (2018) Chronic low back pain and postural rehabilitation exercise: a literature review. J Pain Res pp 95\u2013107","DOI":"10.2147\/JPR.S171729"},{"key":"3372_CR29","doi-asserted-by":"crossref","unstructured":"Leandro AP, Joao PP (2020) A metaheuristic-driven approach to fine-tune deep Boltzmann machines. Appl Soft Comput 97","DOI":"10.1016\/j.asoc.2019.105717"},{"key":"3372_CR30","doi-asserted-by":"crossref","unstructured":"Ribeiro M\u00a0T, Singh S, Guestrin C (2016) \" Why should I trust you?\" Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"key":"3372_CR31","doi-asserted-by":"crossref","unstructured":"Rojas-Dom\u00ednguez A, Padierna L\u00a0C, Valadez J M\u00a0C, Puga-Soberanes H\u00a0J, Fraire H\u00a0J (2017) Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. IEEE Access 6:7164\u20137176","DOI":"10.1109\/ACCESS.2017.2779794"},{"key":"3372_CR32","doi-asserted-by":"crossref","unstructured":"Sagga D, Echtioui A, Khemakhem R, Ghorbel M (2020) Epileptic seizure detection using EEG signals based on 1D-CNN approach. In: 2020 20th International conference on sciences and techniques of automatic control and computer engineering (STA), IEEE, pp 51\u201356","DOI":"10.1109\/STA50679.2020.9329321"},{"issue":"12","key":"3372_CR33","doi-asserted-by":"publisher","first-page":"17057","DOI":"10.1007\/s11042-022-12702-9","volume":"81","author":"M Sameer","year":"2022","unstructured":"Sameer M, Gupta B (2022) CNN based framework for detection of epileptic seizures. Multimed Tools Appl 81(12):17057\u201317070","journal-title":"Multimed Tools Appl"},{"key":"3372_CR34","doi-asserted-by":"crossref","unstructured":"Schlegel U, Arnout H, El-Assady M, Oelke D, Keim D\u00a0A (2019) Towards a rigorous evaluation of XAI methods on time series. In: 2019 IEEE\/CVF international conference on computer vision workshop (ICCVW), IEEE, pp 4197\u20134201","DOI":"10.1109\/ICCVW.2019.00516"},{"key":"3372_CR35","unstructured":"Shrikumar A, Greenside P, Kundaje A (2017) Learning important features through propagating activation differences. In: International conference on machine learning, PMLR, pp 3145\u20133153"},{"key":"3372_CR36","doi-asserted-by":"crossref","unstructured":"Shukla S\u00a0K, Koley E, Ghosh S (2020) Grey wolf optimization-tuned convolutional neural network for transmission line protection with immunity against symmetrical and asymmetrical power swing. Neural Comput Appl 32(22):17059\u201317076","DOI":"10.1007\/s00521-020-04938-z"},{"key":"3372_CR37","unstructured":"Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv:1312.6034"},{"key":"3372_CR38","doi-asserted-by":"crossref","unstructured":"Singh D, Merdivan E, Kropf J, Holzinger A (2024) Class imbalance in multi-resident activity recognition: an evaluative study on explainability of deep learning approaches. Univ Access Inf Soc, pp 1\u201319, June 2024","DOI":"10.1007\/s10209-024-01123-0"},{"key":"3372_CR39","doi-asserted-by":"crossref","unstructured":"Song Y-F, Zhang Z, Shan C, Wang L (2020) Richly activated graph convolutional network for robust skeleton-based action recognition. IEEE Trans Circ Syst Video Technol 31(5):1915\u20131925","DOI":"10.1109\/TCSVT.2020.3015051"},{"key":"3372_CR40","doi-asserted-by":"crossref","unstructured":"Stinear C\u00a0M, Lang C\u00a0E, Zeiler S, Byblow W\u00a0D (2020) Advances and challenges in stroke rehabilitation. Lancet Neuro 19(4):348\u2013360","DOI":"10.1016\/S1474-4422(19)30415-6"},{"key":"#cr-split#-3372_CR41.1","doi-asserted-by":"crossref","unstructured":"Teasell R, Salbach N\u00a0M, Foley N, Mountain A, Cameron J\u00a0I, de Jong A, Acerra N\u00a0E, Bastasi D, Carter S\u00a0L, Fung J, et\u00a0al (2020) Canadian stroke best practice recommendations: rehabilitation, recovery, and community participation following stroke. Part one: rehabilitation and recovery following stroke","DOI":"10.1177\/1747493019897843"},{"key":"#cr-split#-3372_CR41.2","doi-asserted-by":"crossref","unstructured":"update 2019. Int J Stroke 15(7):763-788","DOI":"10.1142\/S1793042119500398"},{"issue":"1","key":"3372_CR42","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/S1474-4422(05)70285-4","volume":"5","author":"E Tolosa","year":"2006","unstructured":"Tolosa E, Wenning G, Poewe W (2006) The diagnosis of Parkinson\u2019s disease. The Lancet Neuro 5(1):75\u201386","journal-title":"The Lancet Neuro"},{"key":"3372_CR43","doi-asserted-by":"crossref","unstructured":"Xie H, Zhang L, Lim C\u00a0P (2020) Evolving CNN-LSTM models for time series prediction using enhanced grey wolf optimizer. IEEE Access 8:161519\u2013161541","DOI":"10.1109\/ACCESS.2020.3021527"},{"key":"3372_CR44","doi-asserted-by":"crossref","unstructured":"Gaowei X, Tianhe R Y, Chen, Wenliang C (2020) A one-dimensional CNN-LSTM model for epileptic seizure recognition using EEG signal analysis. Front Neurosci 14:578126","DOI":"10.3389\/fnins.2020.578126"},{"key":"3372_CR45","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, volume\u00a032","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"3372_CR46","doi-asserted-by":"crossref","unstructured":"Zhang H, Zhao W, Liu S (2020) SE-ECGNet: a multi-scale deep residual network with squeeze-and-excitation module for ECG signal classification. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM), IEEE, pp 2685\u20132691","DOI":"10.1109\/BIBM49941.2020.9313548"},{"key":"3372_CR47","doi-asserted-by":"crossref","unstructured":"Zhang P, Lan C, Zeng W, Xing J, Xue J, Zheng N (2020) Semantics-guided neural networks for efficient skeleton-based human action recognition. In: proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1112\u20131121","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"3372_CR48","doi-asserted-by":"crossref","unstructured":"Zhang S, Fan L, Ye J, Chen G, Fu C, Leng Y (2023) An intelligent rehabilitation assessment method for stroke patients based on lower limb exoskeleton robot. IEEE Trans Neural Syst Rehab Eng","DOI":"10.1109\/TNSRE.2023.3298670"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03372-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03372-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03372-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T17:03:03Z","timestamp":1757955783000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03372-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,6]]},"references-count":49,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["3372"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03372-4","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,6]]},"assertion":[{"value":"12 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}