{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:31:37Z","timestamp":1780104697857,"version":"3.54.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"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":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-04934-7","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:52:31Z","timestamp":1762735951000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["TFF-Net: A hybrid MSC-TCN-Attention network for enhanced long\u2013short term features extraction in gesture classification"],"prefix":"10.1007","volume":"19","author":[{"given":"Gautam","family":"Shah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhinav","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak","family":"Joshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajit Singh","family":"Rathor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunil","family":"Semwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"4934_CR1","unstructured":"University, T.O.S.: Limb Loss Stats. [Online]. (2019). Available: u.osu.edu\/fitness4all\/loss-limb-stats\/"},{"key":"4934_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3147338","volume":"71","author":"YK Li","year":"2022","unstructured":"Li, Y.K., Meng, Q.H., Yang, T.H., Wang, Y.X., Hou, H.R.: Touch gesture and emotion recognition using decomposed Spatiotemporal convolutions. IEEE Trans. Instrum. Meas. 71, 1\u20139 (2022). https:\/\/doi.org\/10.1109\/TIM.2022.3147338","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4934_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3217868","volume":"71","author":"L Meng","year":"2022","unstructured":"Meng, L., Jiang, X., Liu, X., Fan, J., Ren, H., Guo, Y., Chen, W.: User-tailored hand gesture recognition system for wearable prosthesis and armband based on surface electromyogram. IEEE Trans. Instrum. Meas. 71, 1\u201316 (2022). https:\/\/doi.org\/10.1109\/TIM.2022.3217868","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4934_CR4","doi-asserted-by":"publisher","first-page":"110094","DOI":"10.1016\/j.compeleceng.2025.110094","volume":"123","author":"G Shah","year":"2025","unstructured":"Shah, G., Sharma, A., Joshi, D., Rathor, A.S.: Revolutionizing prosthetic hand control using non-invasive sensors and intelligent algorithms: A comprehensive review. Comput. Electr. Eng. 123, 110094 (2025). https:\/\/doi.org\/10.1016\/j.compeleceng.2025.110094","journal-title":"Comput. Electr. Eng."},{"issue":"4","key":"4934_CR5","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1109\/TMC.2019.2962760","volume":"20","author":"T Zhao","year":"2019","unstructured":"Zhao, T., Liu, J., Wang, Y., Liu, H., Chen, Y.: Towards low-cost sign Language gesture recognition leveraging wearables. IEEE Trans. Mob. Comput. 20(4), 1685\u20131701 (2019). https:\/\/doi.org\/10.1109\/TMC.2019.2962760","journal-title":"IEEE Trans. Mob. Comput."},{"key":"4934_CR6","doi-asserted-by":"publisher","first-page":"102074","DOI":"10.1016\/j.bspc.2020.102074","volume":"62","author":"K Li","year":"2020","unstructured":"Li, K., Zhang, J., Wang, L., Zhang, M., Li, J., Bao, S.: A review of the key technologies for sEMG-based human-robot interaction systems. Biomed. Signal Process. Control. 62, 102074 (2020). https:\/\/doi.org\/10.1016\/j.bspc.2020.102074","journal-title":"Biomed. Signal Process. Control"},{"key":"4934_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e34998","author":"AS Rathor","year":"2024","unstructured":"Rathor, A.S., Choudhury, S., Sharma, A., Nautiyal, P., Shah, G.: Empowering vertical farming through IoT and AI-Driven technologies: A comprehensive review. Heliyon. (2024). https:\/\/doi.org\/10.1016\/j.heliyon.2024.e34998","journal-title":"Heliyon"},{"key":"4934_CR8","doi-asserted-by":"publisher","unstructured":"Shah, G., Sharma, A., Joshi, D., Rathor, A.S.: A novel AVOA optimized DNN-BiLSTM-Attention model for improved gesture classification using electromyography signal. Results Eng. 105648 (2025). https:\/\/doi.org\/10.1016\/j.rineng.2025.105648","DOI":"10.1016\/j.rineng.2025.105648"},{"issue":"1","key":"4934_CR9","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/10.204774","volume":"40","author":"B Hudgins","year":"1993","unstructured":"Hudgins, B., Parker, P., Scott, R.: N. A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40(1), 82\u201394 (1993). https:\/\/doi.org\/10.1109\/10.204774","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"4934_CR10","doi-asserted-by":"publisher","DOI":"10.36227\/techrxiv.21287487.v1","author":"S Bala","year":"2023","unstructured":"Bala, S., Joshi, D.: An attention-based deep CNN-BiLSTM model for forecasting of fatigue-induced surface electromyography signals during isotonic contractions. Authorea Preprints. (2023). https:\/\/doi.org\/10.36227\/techrxiv.21287487.v1","journal-title":"Authorea Preprints"},{"key":"4934_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2025.3564045","author":"P Tokas","year":"2025","unstructured":"Tokas, P., Semwal, V.B., Jain, S.A.: Lightweight and explainable hybrid deep learning model for wearable Sensor-Based human activity recognition. IEEE Sens. J. (2025). https:\/\/doi.org\/10.1109\/JSEN.2025.3564045","journal-title":"IEEE Sens. J."},{"key":"4934_CR12","doi-asserted-by":"publisher","first-page":"102948","DOI":"10.1016\/j.bspc.2021.102948","volume":"70","author":"NK Karnam","year":"2021","unstructured":"Karnam, N.K., Turlapaty, A.C., Dubey, S.R., Gokaraju, B.: Classification of sEMG signals of hand gestures based on energy features. Biomed. Signal Process. Control. 70, 102948 (2021). https:\/\/doi.org\/10.1016\/j.bspc.2021.102948","journal-title":"Biomed. Signal Process. Control"},{"key":"4934_CR13","doi-asserted-by":"publisher","unstructured":"Shen, S., Gu, K., Chen, X.-R., Lv, C.-X., Wang, R.-C.: Gesture recognition through sEMG with wearable device based on deep learning. Mob. Netw. Appl. 25(6), 2447\u20132458 (Dec. 2020). https:\/\/doi.org\/10.1007\/s11036-020-01590-8","DOI":"10.1007\/s11036-020-01590-8"},{"issue":"3","key":"4934_CR14","doi-asserted-by":"publisher","first-page":"672","DOI":"10.3390\/s20030672","volume":"20","author":"L Chen","year":"2020","unstructured":"Chen, L., Fu, J., Wu, Y., Li, H., Zheng, B.: Hand gesture recognition using compact CNN via surface electromyography signals. Sensors. 20(3), 672 (2020). https:\/\/doi.org\/10.3390\/s20030672","journal-title":"Sensors"},{"key":"4934_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2024.3373045","volume":"73","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Li, X., Yang, L., Yu, H.: A transformer-based gesture prediction model via sEMG sensor for human-robot interaction. IEEE Trans. Instrum. Meas. 73, 1\u201315 (2024). https:\/\/doi.org\/10.1109\/TIM.2024.3373045","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4934_CR16","doi-asserted-by":"publisher","first-page":"121055","DOI":"10.1016\/j.eswa.2023.121055","volume":"234","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Zhao, P., Zhang, Z.: A deep learning approach using attention mechanism and transfer learning for electromyographic hand gesture Estimation. Expert Syst. Appl. 234, 121055 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.121055","journal-title":"Expert Syst. Appl."},{"key":"4934_CR17","doi-asserted-by":"publisher","first-page":"105935","DOI":"10.1016\/j.bspc.2023.105935","volume":"91","author":"Z Zhang","year":"2024","unstructured":"Zhang, Z., Shen, Q., Wang, Y.: EMG hand gesture recognition using CNN with multi-attention. Biomed. Signal Process. Control. 91, 105935 (2024). https:\/\/doi.org\/10.1016\/j.bspc.2023.105935","journal-title":"Biomed. Signal Process. Control"},{"key":"4934_CR18","doi-asserted-by":"publisher","unstructured":"Wang, W., Chen, B., Xia, P., Hu, J., Peng, Y.: Sensor fusion for myoelectric control based on DL with recurrent convolutional neural networks. Artif. Organs. 42(9), E272\u2013E282 (Sep. 2018). https:\/\/doi.org\/10.1111\/aor.13153","DOI":"10.1111\/aor.13153"},{"key":"4934_CR19","doi-asserted-by":"publisher","first-page":"107497","DOI":"10.1016\/j.compbiomed.2023.107497","volume":"166","author":"B Xiong","year":"2023","unstructured":"Xiong, B., Chen, W., Niu, Y., Gan, Z., Mao, G., Xu, Y.A.: Global and local feature fused CNN architecture for the sEMG-based HGR. Comput. Biol. Med. 166, 107497 (2023). https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107497","journal-title":"Comput. Biol. Med."},{"key":"4934_CR20","unstructured":"The dataset is taken from Ninapro-DB1 Database: Accessed: 16.05.2025. Available: https:\/\/ninapro.hevs.ch\/instructions\/DB1.html"},{"issue":"1","key":"4934_CR21","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/TNSRE.2014.2328495","volume":"23","author":"M Atzori","year":"2014","unstructured":"Atzori, M., Gijsberts, A., Kuzborskij, I., Elsig, S., Hager, A.G., Caputo, B.: Characterization of a benchmark database for myoelectric movement classification. IEEE Trans. Neural Syst. Rehabil. Eng. 23(1), 73\u201383 (2014). https:\/\/doi.org\/10.1109\/TNSRE.2014.2328495","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"4934_CR22","unstructured":"Harvard Dataverse: The dataset is taken from Harvard Dataverse repository. Accessed: 26.05.2025. Available: https:\/\/dataverse.harvard.edu\/dataset.xhtml?persistentId=doi:10.7910\/DVN\/ R6JJ4Q"},{"key":"4934_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3279873","volume":"72","author":"NK Karnam","year":"2023","unstructured":"Karnam, N.K., Dubey, S.R., Gokaraju, B.: EMAHA-DB1: A new upper limb sEMG dataset for classification of activities of daily living. IEEE Trans. Instrum. Meas. 72, 1\u201311 (2023). https:\/\/doi.org\/10.1109\/TIM.2023.3279873","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"4","key":"4934_CR24","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1109\/TMRB.2022.3216957","volume":"4","author":"S Pancholi","year":"2022","unstructured":"Pancholi, S., Joshi, A.M., Joshi, D.D.L.P.R.: Deep Learning-Based enhanced pattern recognition Frame-Work for improved myoelectric prosthesis control. IEEE Trans. Med. Rob. Bionics. 4(4), 991\u2013999 (2022). https:\/\/doi.org\/10.1109\/TMRB.2022.3216957","journal-title":"IEEE Trans. Med. Rob. Bionics"},{"issue":"1","key":"4934_CR25","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.bbe.2022.02.005","volume":"42","author":"NK Karnam","year":"2022","unstructured":"Karnam, N.K., Dubey, S.R., Turlapaty, A.C., Gokaraju, B., EMGHandNet:: A hybrid CNN and Bi-LSTM architecture for hand activity classification using surface EMG signals. Biocybernetics Biomedical Eng. 42(1), 325\u2013340 (2022). https:\/\/doi.org\/10.1016\/j.bbe.2022.02.005","journal-title":"Biocybernetics Biomedical Eng."},{"issue":"10","key":"4934_CR26","doi-asserted-by":"publisher","first-page":"11082","DOI":"10.1109\/JSEN.2023.3264646","volume":"23","author":"L Tong","year":"2023","unstructured":"Tong, L., Zhang, M., Ma, H., Wang, C., Peng, L.: SEMG-based gesture recognition method for coal mine inspection manipulator using multistream CNN. IEEE Sens. J. 23(10), 11082\u201311090 (2023). https:\/\/doi.org\/10.1109\/JSEN.2023.3264646","journal-title":"IEEE Sens. J."},{"key":"4934_CR27","doi-asserted-by":"publisher","first-page":"105846","DOI":"10.1016\/j.bspc.2023.105846","volume":"90","author":"X Mian","year":"2024","unstructured":"Mian, X., Bingtao, Z., Shiqiang, C., Song, L.: MCMP-Net: MLP combining max pooling network for sEMG gesture recognition. Biomed. Signal Process. Control. 90, 105846 (2024). https:\/\/doi.org\/10.1016\/j.bspc.2023.105846","journal-title":"Biomed. Signal Process. Control"},{"key":"4934_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3560665","author":"W Sijin","year":"2025","unstructured":"Sijin, W., Jianjun, T., Bingtao, Z., Tao, H., Li, Z., Mian, X.: STMS-Net: Spatial Temporal Multi-Spectral network for sEMG gesture recognition. IEEE Access. (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3560665","journal-title":"IEEE Access."},{"key":"4934_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3467065","author":"MA Al-Qaness","year":"2024","unstructured":"Al-Qaness, M.A., Ni, S., TCNN-KAN: Optimized CNN by Kolmogorov-Arnold network and pruning techniques for sEMG gesture recognition. IEEE J. Biomedical Health Inf. (2024). https:\/\/doi.org\/10.1109\/JBHI.2024.3467065","journal-title":"IEEE J. Biomedical Health Inf."},{"key":"4934_CR30","doi-asserted-by":"publisher","first-page":"118282","DOI":"10.1016\/j.eswa.2022.118282","volume":"210","author":"S Karheily","year":"2022","unstructured":"Karheily, S., Moukadem, A., Courbot, J.B., Abdeslam, D.O.: sEMG time\u2013frequency features for hand movements classification. Expert Syst. Appl. 210, 118282 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.118282","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"4934_CR31","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1109\/TCDS.2018.2884942","volume":"13","author":"F Duan","year":"2018","unstructured":"Duan, F., Ren, X., Yang, Y.: A gesture recognition system based on time domain features and LDA. IEEE Trans. Cogn. Dev. Syst. 13(1), 200\u2013208 (2018). https:\/\/doi.org\/10.1109\/TCDS.2018.2884942","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"5","key":"4934_CR32","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1109\/THMS.2023.3287594","volume":"53","author":"P Chen","year":"2023","unstructured":"Chen, P., Li, Z., Togo, Jiang, Y.: A layered sEMG\u2013FMG hybrid sensor for hand motion recognition from forearm muscle activities. IEEE Trans. Human-Machine Syst. 53(5), 935\u2013944 (2023). https:\/\/doi.org\/10.1109\/THMS.2023.3287594","journal-title":"IEEE Trans. Human-Machine Syst."},{"key":"4934_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/TMRB.2024.3408896","author":"P Rani","year":"2024","unstructured":"Rani, P., Pancholi, S., Shaw, V., Atzori, M., Kumar, S., Enhanced, E.M.G.: Based hand gesture classification in Real-World scenarios: Mitigating dynamic factors with Tempo-Spatial wavelet transform and deep learning. IEEE Trans. Med. Rob. Bionics. (2024). https:\/\/doi.org\/10.1109\/TMRB.2024.3408896","journal-title":"IEEE Trans. Med. Rob. Bionics"},{"key":"4934_CR34","doi-asserted-by":"publisher","first-page":"107438","DOI":"10.1016\/j.bspc.2024.107438","volume":"103","author":"Z Zhang","year":"2025","unstructured":"Zhang, Z., Zhao, B., Zhang, X., Zhang, Y.: Dilated residual convolutional network for surface electromyographic hand gesture recognition. Biomed. Signal Process. Control. 103, 107438 (2025). https:\/\/doi.org\/10.1016\/j.bspc.2024.107438","journal-title":"Biomed. Signal Process. Control"},{"key":"4934_CR35","doi-asserted-by":"publisher","first-page":"106870","DOI":"10.1016\/j.bspc.2024.106870","volume":"99","author":"D Thakur","year":"2025","unstructured":"Thakur, D., Dangi, S., Lalwani, P.: A novel hybrid DL approach with GWO\u2013WOA optimization technique for human activity recognition. Biomed. Signal Process. Control. 99, 106870 (2025). https:\/\/doi.org\/10.1016\/j.bspc.2024.106870","journal-title":"Biomed. Signal Process. Control"},{"key":"4934_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2024.3487216","author":"F Hu","year":"2024","unstructured":"Hu, F., Qian, M., He, K., Zhang, W., Yang, X.: A novel multi-feature fusion network with Spatial partitioning strategy and cross-attention for armband-based gesture recognition. IEEE Trans. Neural Syst. Rehabil. Eng. (2024). https:\/\/doi.org\/10.1109\/TNSRE.2024.3487216","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"4934_CR37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3517876","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Wang, C., Li, F., et al.: RF-AIRCGR: Lightweight convolutional neural Network-Based RFID Chinese character gesture recognition research. IEEE Access. (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3517876","journal-title":"IEEE Access."},{"issue":"4","key":"4934_CR38","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1007\/s42235-024-00543-1","volume":"21","author":"F Hu","year":"2024","unstructured":"Hu, F., He, K., Qian, M., Gouda, M.A.: TFN-FICFM: sEMG-based gesture recognition using Temporal fusion network and fuzzy integral-based classifier fusion. J. Bionic Eng. 21(4), 1878\u20131891 (2024). https:\/\/doi.org\/10.1007\/s42235-024-00543-1","journal-title":"J. Bionic Eng."},{"issue":"6","key":"4934_CR39","doi-asserted-by":"publisher","first-page":"3743","DOI":"10.3390\/app13063743","volume":"13","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Yuan, B., Yang, Z., Li, Z., Liu, X.: Wi-NN: Human gesture recognition system based on weighted kNN. Appl. Sci. 13(6), 3743 (2023). https:\/\/doi.org\/10.3390\/app13063743","journal-title":"Appl. Sci."},{"issue":"1","key":"4934_CR40","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s11760-024-03664-6","volume":"19","author":"H F\u0131rat","year":"2025","unstructured":"F\u0131rat, H., \u00dczen, H., Atila, O., \u015eeng\u00fcr, A.: Automated efficient traffic gesture recognition using Swin transformer-based multi-input deep network with radar images. Signal. Image Video Process. 19(1), 35 (2025). https:\/\/doi.org\/10.1007\/s11760-024-03664-6","journal-title":"Signal. Image Video Process."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04934-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04934-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04934-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:55:40Z","timestamp":1764723340000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04934-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":40,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4934"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04934-7","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"4 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2025","order":4,"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":"Competing interests"}}],"article-number":"1356"}}