{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T15:47:02Z","timestamp":1782748022930,"version":"3.54.5"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032293640","type":"print"},{"value":"9783032293657","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"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":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-29365-7_9","type":"book-chapter","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T15:27:13Z","timestamp":1782746833000},"page":"137-153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Compact Spiking Neural Network for\u00a0Real-Time Swimming Style Recognition on\u00a0Low-Power FPGA"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7441-1425","authenticated-orcid":false,"given":"Matteo Antonio","family":"Scrugli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8106-4641","authenticated-orcid":false,"given":"Paolo","family":"Meloni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","unstructured":"Brunner, G., Melnyk, D., Sigf\u00fasson, B., Wattenhofer, R.: Swimming style recognition and lap counting using a smartwatch and deep learning. In: Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 23\u201331. ISWC \u201919, Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3341163.3347719","DOI":"10.1145\/3341163.3347719"},{"key":"9_CR2","doi-asserted-by":"publisher","unstructured":"Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 46(3) (2014). https:\/\/doi.org\/10.1145\/2499621","DOI":"10.1145\/2499621"},{"issue":"3","key":"9_CR3","doi-asserted-by":"publisher","first-page":"873","DOI":"10.3390\/s18030873","volume":"18","author":"V Camomilla","year":"2018","unstructured":"Camomilla, V., Bergamini, E., Fantozzi, S., Vannozzi, G.: Trends supporting the in-field use of wearable inertial sensors for sport performance evaluation: a systematic review. Sensors 18(3), 873 (2018). https:\/\/doi.org\/10.3390\/s18030873","journal-title":"Sensors"},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Chen, J., Yang, Y., Deng, S., Teng, D., Pan, L.: SpikMamba: when SNN meets mamba in event-based human action recognition. In: Proceedings of the 6th ACM International Conference on Multimedia in Asia (2024). https:\/\/doi.org\/10.1145\/3696409.3700204","DOI":"10.1145\/3696409.3700204"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Delhaye, E., Bouvet, A., Nicolas, G., Vilas-Boas, J.a.P., Bideau, B., Bideau, N.: Automatic swimming activity recognition and lap time assessment based on a single IMU: a deep learning approach. Sensors 22(15), 5786 (2022). https:\/\/doi.org\/10.3390\/s22155786","DOI":"10.3390\/s22155786"},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"Fang, W., et al.: SpikingJelly: an open-source machine learning infrastructure platform for spike-based intelligence. Sci. Adv. 9(40), eadi1480 (2023). https:\/\/doi.org\/10.1126\/sciadv.adi1480","DOI":"10.1126\/sciadv.adi1480"},{"key":"9_CR7","doi-asserted-by":"publisher","unstructured":"Fang, Z., Li, J., Wang, W.: CASNN: continuous adaptive SNN for human activity recognition. In: 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), pp. 1\u20136 (2024). https:\/\/doi.org\/10.1109\/ICIEA61579.2024.10665045","DOI":"10.1109\/ICIEA61579.2024.10665045"},{"issue":"8","key":"9_CR8","doi-asserted-by":"publisher","first-page":"3144","DOI":"10.1109\/JSEN.2013.2262933","volume":"13","author":"RM Hagem","year":"2013","unstructured":"Hagem, R.M., O\u2019Keefe, S.G., Fickenscher, T., Thiel, D.V.: Self contained adaptable optical wireless communications system for stroke rate during swimming. IEEE Sens. J. 13(8), 3144\u20133151 (2013). https:\/\/doi.org\/10.1109\/JSEN.2013.2262933","journal-title":"IEEE Sens. J."},{"key":"9_CR9","doi-asserted-by":"publisher","unstructured":"Hamidi Rad, M.: SmartSwim: a novel approach to swimming analysis and coaching assistance based on wearable inertial sensors. Ph.D. thesis, EPFL, Lausanne (2022). https:\/\/doi.org\/10.5075\/epfl-thesis-9931","DOI":"10.5075\/epfl-thesis-9931"},{"key":"9_CR10","unstructured":"Kindgren, O.: SERV - the serial RISC-V CPU (2019). https:\/\/github.com\/olofk\/serv. Accessed 17 Feb 2026"},{"issue":"2","key":"9_CR11","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1109\/TCSI.2024.3450966","volume":"72","author":"G Leone","year":"2025","unstructured":"Leone, G., Scrugli, M.A., Badas, L., Martis, L., Raffo, L., Meloni, P.: SYNtzulu: a tiny RISC-V-controlled SNN processor for real-time sensor data analysis on low-power FPGAs. IEEE Trans. Circuits Syst. I Regul. Pap. 72(2), 790\u2013801 (2025). https:\/\/doi.org\/10.1109\/TCSI.2024.3450966","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"9_CR12","doi-asserted-by":"publisher","unstructured":"Li, Y., Yin, R., Kim, Y., Panda, P.: Efficient human activity recognition with spatio-temporal spiking neural networks. Front. Neurosci. 17 (2023). https:\/\/doi.org\/10.3389\/fnins.2023.1233037","DOI":"10.3389\/fnins.2023.1233037"},{"issue":"9","key":"9_CR13","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","volume":"10","author":"W Maass","year":"1997","unstructured":"Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659\u20131671 (1997). https:\/\/doi.org\/10.1016\/S0893-6080(97)00011-7","journal-title":"Neural Netw."},{"issue":"11","key":"9_CR14","first-page":"39","volume":"10","author":"Y Meng","year":"2024","unstructured":"Meng, Y.: Swimming style recognition with convolutional neural network with single IMU. World Sci. Res. J. 10(11), 39\u201343 (2024)","journal-title":"World Sci. Res. J."},{"key":"9_CR15","doi-asserted-by":"publisher","unstructured":"Nunes, J.a.D., Carvalho, M., Carneiro, D., Cardoso, J.S.: Spiking neural networks: a survey. IEEE Access 10, 60738\u201360764 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3179968","DOI":"10.1109\/ACCESS.2022.3179968"},{"issue":"1","key":"9_CR16","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/TBCAS.2024.3456552","volume":"19","author":"MA Scrugli","year":"2025","unstructured":"Scrugli, M.A., Leone, G., Busia, P., Raffo, L., Meloni, P.: Real-time sEMG processing with spiking neural networks on a low-power 5K-LUT FPGA. IEEE Trans. Biomed. Circuits Syst. 19(1), 68\u201381 (2025). https:\/\/doi.org\/10.1109\/TBCAS.2024.3456552","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"9_CR17","doi-asserted-by":"publisher","unstructured":"Tarasevi\u010dius, D., Serackis, A.: Deep learning model for sensor based swimming style recognition. In: 2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), pp. 1\u20134 (2020). https:\/\/doi.org\/10.1109\/eStream50540.2020.9108849","DOI":"10.1109\/eStream50540.2020.9108849"},{"key":"9_CR18","doi-asserted-by":"publisher","unstructured":"Yeon, T., Xu, V., Hoffmann, H., Ahuja, K.: WatchHAR: real-time on-device human activity recognition system for smartwatches, pp. 387\u2013394. Association for Computing Machinery, New York, NY, USA (2025). https:\/\/doi.org\/10.1145\/3716553.3750775","DOI":"10.1145\/3716553.3750775"},{"key":"9_CR19","doi-asserted-by":"publisher","unstructured":"Zhang, G., Xi, B., Hu, M., Zhu, X.: Wearable inertial sensor-based recognition of swimming movements and postures. In: Proceedings of the 2025 2nd International Conference on Big Data Analytics and Artificial Intelligence Application, pp. 178\u2013183. Association for Computing Machinery, New York, NY, USA (2025). https:\/\/doi.org\/10.1145\/3788108.3788525","DOI":"10.1145\/3788108.3788525"},{"issue":"22","key":"9_CR20","doi-asserted-by":"publisher","first-page":"5001","DOI":"10.3390\/s19225001","volume":"19","author":"Z Zhuang","year":"2019","unstructured":"Zhuang, Z., Xue, Y.: Sport-related human activity detection and recognition using a smartwatch. Sensors 19(22), 5001 (2019). https:\/\/doi.org\/10.3390\/s19225001","journal-title":"Sensors"}],"container-title":["Lecture Notes in Computer Science","Applied Reconfigurable Computing. Architectures, Tools, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29365-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T15:27:22Z","timestamp":1782746842000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29365-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"ISBN":["9783032293640","9783032293657"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29365-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"30 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ARC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Applied Reconfigurable Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari, Sardinia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"arc2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/arc2026.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}