{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T16:32:21Z","timestamp":1783960341333,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars","award":["LR22A020002"],"award-info":[{"award-number":["LR22A020002"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars","award":["2021C03130"],"award-info":[{"award-number":["2021C03130"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars","award":["LTGY23H040003"],"award-info":[{"award-number":["LTGY23H040003"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars","award":["2022Z196"],"award-info":[{"award-number":["2022Z196"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars","award":["20221JCGY010532"],"award-info":[{"award-number":["20221JCGY010532"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars","award":["20221JCGY010607"],"award-info":[{"award-number":["20221JCGY010607"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars","award":["2021S134"],"award-info":[{"award-number":["2021S134"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars","award":["ZKKY2023001"],"award-info":[{"award-number":["ZKKY2023001"]}]},{"name":"Zhejiang Provincial Key Research and Development Program of China","award":["LR22A020002"],"award-info":[{"award-number":["LR22A020002"]}]},{"name":"Zhejiang Provincial Key Research and Development Program of China","award":["2021C03130"],"award-info":[{"award-number":["2021C03130"]}]},{"name":"Zhejiang Provincial Key Research and Development Program of China","award":["LTGY23H040003"],"award-info":[{"award-number":["LTGY23H040003"]}]},{"name":"Zhejiang Provincial Key Research and Development Program of China","award":["2022Z196"],"award-info":[{"award-number":["2022Z196"]}]},{"name":"Zhejiang Provincial Key Research and Development Program of China","award":["20221JCGY010532"],"award-info":[{"award-number":["20221JCGY010532"]}]},{"name":"Zhejiang Provincial Key Research and Development Program of China","award":["20221JCGY010607"],"award-info":[{"award-number":["20221JCGY010607"]}]},{"name":"Zhejiang Provincial Key Research and Development Program of China","award":["2021S134"],"award-info":[{"award-number":["2021S134"]}]},{"name":"Zhejiang Provincial Key Research and Development Program of China","award":["ZKKY2023001"],"award-info":[{"award-number":["ZKKY2023001"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["LR22A020002"],"award-info":[{"award-number":["LR22A020002"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["2021C03130"],"award-info":[{"award-number":["2021C03130"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["LTGY23H040003"],"award-info":[{"award-number":["LTGY23H040003"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["2022Z196"],"award-info":[{"award-number":["2022Z196"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["20221JCGY010532"],"award-info":[{"award-number":["20221JCGY010532"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["20221JCGY010607"],"award-info":[{"award-number":["20221JCGY010607"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["2021S134"],"award-info":[{"award-number":["2021S134"]}]},{"name":"Zhejiang Provincial Natural Science Foundation","award":["ZKKY2023001"],"award-info":[{"award-number":["ZKKY2023001"]}]},{"name":"Ningbo Key R&amp;D Program","award":["LR22A020002"],"award-info":[{"award-number":["LR22A020002"]}]},{"name":"Ningbo Key R&amp;D Program","award":["2021C03130"],"award-info":[{"award-number":["2021C03130"]}]},{"name":"Ningbo Key R&amp;D Program","award":["LTGY23H040003"],"award-info":[{"award-number":["LTGY23H040003"]}]},{"name":"Ningbo Key R&amp;D Program","award":["2022Z196"],"award-info":[{"award-number":["2022Z196"]}]},{"name":"Ningbo Key R&amp;D Program","award":["20221JCGY010532"],"award-info":[{"award-number":["20221JCGY010532"]}]},{"name":"Ningbo Key R&amp;D Program","award":["20221JCGY010607"],"award-info":[{"award-number":["20221JCGY010607"]}]},{"name":"Ningbo Key R&amp;D Program","award":["2021S134"],"award-info":[{"award-number":["2021S134"]}]},{"name":"Ningbo Key R&amp;D Program","award":["ZKKY2023001"],"award-info":[{"award-number":["ZKKY2023001"]}]},{"name":"Ningbo Natural Science Foundation","award":["LR22A020002"],"award-info":[{"award-number":["LR22A020002"]}]},{"name":"Ningbo Natural Science Foundation","award":["2021C03130"],"award-info":[{"award-number":["2021C03130"]}]},{"name":"Ningbo Natural Science Foundation","award":["LTGY23H040003"],"award-info":[{"award-number":["LTGY23H040003"]}]},{"name":"Ningbo Natural Science Foundation","award":["2022Z196"],"award-info":[{"award-number":["2022Z196"]}]},{"name":"Ningbo Natural Science Foundation","award":["20221JCGY010532"],"award-info":[{"award-number":["20221JCGY010532"]}]},{"name":"Ningbo Natural Science Foundation","award":["20221JCGY010607"],"award-info":[{"award-number":["20221JCGY010607"]}]},{"name":"Ningbo Natural Science Foundation","award":["2021S134"],"award-info":[{"award-number":["2021S134"]}]},{"name":"Ningbo Natural Science Foundation","award":["ZKKY2023001"],"award-info":[{"award-number":["ZKKY2023001"]}]},{"name":"Public Welfare Science &amp; Technology Project of Ningbo, China","award":["LR22A020002"],"award-info":[{"award-number":["LR22A020002"]}]},{"name":"Public Welfare Science &amp; Technology Project of Ningbo, China","award":["2021C03130"],"award-info":[{"award-number":["2021C03130"]}]},{"name":"Public Welfare Science &amp; Technology Project of Ningbo, China","award":["LTGY23H040003"],"award-info":[{"award-number":["LTGY23H040003"]}]},{"name":"Public Welfare Science &amp; Technology Project of Ningbo, China","award":["2022Z196"],"award-info":[{"award-number":["2022Z196"]}]},{"name":"Public Welfare Science &amp; Technology Project of Ningbo, China","award":["20221JCGY010532"],"award-info":[{"award-number":["20221JCGY010532"]}]},{"name":"Public Welfare Science &amp; Technology Project of Ningbo, China","award":["20221JCGY010607"],"award-info":[{"award-number":["20221JCGY010607"]}]},{"name":"Public Welfare Science &amp; Technology Project of Ningbo, China","award":["2021S134"],"award-info":[{"award-number":["2021S134"]}]},{"name":"Public Welfare Science &amp; Technology Project of Ningbo, China","award":["ZKKY2023001"],"award-info":[{"award-number":["ZKKY2023001"]}]},{"name":"Zhejiang Rehabilitation Medical Association Scientific Research Special Fund","award":["LR22A020002"],"award-info":[{"award-number":["LR22A020002"]}]},{"name":"Zhejiang Rehabilitation Medical Association Scientific Research Special Fund","award":["2021C03130"],"award-info":[{"award-number":["2021C03130"]}]},{"name":"Zhejiang Rehabilitation Medical Association Scientific Research Special Fund","award":["LTGY23H040003"],"award-info":[{"award-number":["LTGY23H040003"]}]},{"name":"Zhejiang Rehabilitation Medical Association Scientific Research Special Fund","award":["2022Z196"],"award-info":[{"award-number":["2022Z196"]}]},{"name":"Zhejiang Rehabilitation Medical Association Scientific Research Special Fund","award":["20221JCGY010532"],"award-info":[{"award-number":["20221JCGY010532"]}]},{"name":"Zhejiang Rehabilitation Medical Association Scientific Research Special Fund","award":["20221JCGY010607"],"award-info":[{"award-number":["20221JCGY010607"]}]},{"name":"Zhejiang Rehabilitation Medical Association Scientific Research Special Fund","award":["2021S134"],"award-info":[{"award-number":["2021S134"]}]},{"name":"Zhejiang Rehabilitation Medical Association Scientific Research Special Fund","award":["ZKKY2023001"],"award-info":[{"award-number":["ZKKY2023001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional methods for collecting ground reaction forces (GRFs) mainly use lab force plates. Previous research broke this pattern by predicting GRFs with deep learning and data from IMUs like joint acceleration. Joint angle, as a geometric, is easier to collect than acceleration outdoors with cameras. LSTM is one of the deep learning models that have shown good performance in biomechanical studies. xLSTM, as an optimized version of LSTM, has not been used in biomechanical studies and no research has predicted GRFs during running solely using lower limb joint angles. This study collected lower-limb joint angle and vertical ground reaction force data at five speeds from 12 healthy male runners with Xsens sensors. Datasets including three joints and three planes were set as the inputs of four deep learning models for vertical-GRF prediction. CNN-xLSTM consistently performed best in the four deep learning models when different datasets were input (R2 = 0.909 \u00b1 0.064, MAPE = 2.18 \u00b1 0.09, rMSE = 0.061 \u00b1 0.008), and the performance was at a relatively high level at the five speeds. The current findings may contribute to a new GRF measurement and provide a reference for future real-time motion detection and sport injury prediction.<\/jats:p>","DOI":"10.3390\/s25041249","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T12:16:37Z","timestamp":1739880997000},"page":"1249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Prediction of Vertical Ground Reaction Forces Under Different Running Speeds: Integration of Wearable IMU with CNN-xLSTM"],"prefix":"10.3390","volume":"25","author":[{"given":"Tianxiao","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1918-0756","authenticated-orcid":false,"given":"Datao","family":"Xu","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"},{"name":"Faculty of Engineering, University of Pannonia, 8200 Veszprem, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirui","family":"Shao","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2187-9440","authenticated-orcid":false,"given":"Yaodong","family":"Gu","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"},{"name":"Faculty of Engineering, University of Szeged, 6720 Szeged, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1080\/14763141.2021.1873411","article-title":"The biomechanics of running and running styles: A synthesis","volume":"23","author":"Beek","year":"2024","journal-title":"Sports Biomech."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.pmr.2015.08.006","article-title":"An evidence-based videotaped running biomechanics analysis","volume":"27","author":"Souza","year":"2016","journal-title":"Phys. Med. Rehabil. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.gaitpost.2006.05.017","article-title":"Rear-foot, mid-foot and fore-foot motion during the stance phase of gait","volume":"25","author":"Leardini","year":"2007","journal-title":"Gait Posture"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1080\/02640414.2016.1266018","article-title":"Is there really an eccentric action of the hamstrings during the swing phase of high-speed running? Part I: A critical review of the literature","volume":"35","author":"Bosch","year":"2017","journal-title":"J. Sports Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"127","DOI":"10.5334\/paah.45","article-title":"Is there a relationship between strike pattern and injury during running: A review","volume":"3","author":"Zhou","year":"2019","journal-title":"Phys. Act. Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s40279-016-0474-4","article-title":"Is there an economical running technique? A review of modifiable biomechanical factors affecting running economy","volume":"46","author":"Moore","year":"2016","journal-title":"Sports Med."},{"key":"ref_7","first-page":"2037","article-title":"Foot speed, foot-strike and footwear: Linking gait mechanics and running ground reaction forces","volume":"217","author":"Clark","year":"2014","journal-title":"J. Exp. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, P., Cen, X., Mei, Q., Wang, A., Gu, Y., and Fernandez, J. (2024). Differences in intra-foot movement strategies during locomotive tasks among chronic ankle instability, copers and healthy individuals. J. Biomech., 162.","DOI":"10.1016\/j.jbiomech.2023.111865"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.gaitpost.2017.04.037","article-title":"A comparison of the ground reaction force frequency content during rearfoot and non-rearfoot running patterns","volume":"56","author":"Gruber","year":"2017","journal-title":"Gait Posture"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1007\/s40279-013-0084-3","article-title":"The biomechanical differences between barefoot and shod distance running: A systematic review and preliminary meta-analysis","volume":"43","author":"Hall","year":"2013","journal-title":"Sports Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"315","DOI":"10.5948\/UPO9781614442004.026","article-title":"Biomechanics of running and walking","volume":"43","author":"Tongen","year":"2010","journal-title":"Math. Sports"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/0966-6362(96)82863-3","article-title":"Possibilities and limitation of today\u2019s force plate technology","volume":"4","author":"Mizoguchi","year":"1995","journal-title":"Gait Posture"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1016\/j.jbiomech.2013.07.036","article-title":"Prediction of ground reaction forces during gait based on kinematics and a neural network model","volume":"46","author":"Oh","year":"2013","journal-title":"J. Biomech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1016\/j.jbiomech.2014.04.030","article-title":"Prediction of ground reaction forces and moments during various activities of daily living","volume":"47","author":"Fluit","year":"2014","journal-title":"J. Biomech."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xu, D., Zhou, H., Quan, W., Gusztav, F., Baker, J.S., and Gu, Y. (2023). Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements. Comput. Methods Programs Biomed., 242.","DOI":"10.1016\/j.cmpb.2023.107848"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chaaban, C.R., Berry, N.T., Armitano-Lago, C., Kiefer, A.W., Mazzoleni, M.J., and Padua, D.A. (2021). Combining inertial sensors and machine learning to predict vGRF and knee biomechanics during a double limb jump landing task. Sensors, 21.","DOI":"10.3390\/s21134383"},{"key":"ref_17","first-page":"225","article-title":"Predicting impact shock magnitude: Which ground reaction force variable should we use","volume":"6","author":"Greenhalgh","year":"2012","journal-title":"Int. J. Sports Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3072","DOI":"10.1177\/0363546520950731","article-title":"Impact-related ground reaction forces are more strongly associated with some running injuries than others","volume":"48","author":"Johnson","year":"2020","journal-title":"Am. J. Sports Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, D., Quan, W., Zhou, H., Sun, D., Baker, J.S., and Gu, Y. (2022). Explaining the differences of gait patterns between high and low-mileage runners with machine learning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-07054-1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, D., Zhou, H., Quan, W., Gusztav, F., Wang, M., Baker, J.S., and Gu, Y. (2023). Accurately and effectively predict the ACL force: Utilizing biomechanical landing pattern before and after-fatigue. Comput. Methods Programs Biomed., 241.","DOI":"10.1016\/j.cmpb.2023.107761"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.gaitpost.2023.10.019","article-title":"A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis","volume":"107","author":"Xu","year":"2024","journal-title":"Gait Posture"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, D., Zhou, H., Quan, W., Ma, X., Chon, T.-E., Fernandez, J., Gusztav, F., Kov\u00e1cs, A., Baker, J.S., and Gu, Y. (2024). New insights optimize landing strategies to reduce lower limb injury risk. Cyborg Bionic Syst., 5.","DOI":"10.34133\/cbsystems.0126"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbiomech.2018.09.009","article-title":"Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities","volume":"81","author":"Halilaj","year":"2018","journal-title":"J. Biomech."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Scheltinga, B.L., Kok, J.N., Buurke, J.H., and Reenalda, J. (2023). Estimating 3D ground reaction forces in running using three inertial measurement units. Front. Sports Act. Living, 5.","DOI":"10.3389\/fspor.2023.1176466"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.medengphy.2020.02.002","article-title":"A neural network method to predict task-and step-specific ground reaction force magnitudes from trunk accelerations during running activities","volume":"78","author":"Pogson","year":"2020","journal-title":"Med. Eng. Phys."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bogaert, S., Davis, J., and Vanwanseele, B. (2024). Predicting vertical ground reaction force characteristics during running with machine learning. Front. Bioeng. Biotechnol., 12.","DOI":"10.3389\/fbioe.2024.1440033"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1109\/LRA.2019.2895266","article-title":"Bio-lstm: A biomechanically inspired recurrent neural network for 3-d pedestrian pose and gait prediction","volume":"4","author":"Du","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e12752","DOI":"10.7717\/peerj.12752","article-title":"Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution","volume":"10","author":"Alcantara","year":"2022","journal-title":"PeerJ"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Donahue, S.R., and Hahn, M.E. (2023). Estimation of ground reaction force waveforms during fixed pace running outside the laboratory. Front. Sports Act. Living, 5.","DOI":"10.3389\/fspor.2023.974186"},{"key":"ref_31","unstructured":"Beck, M., P\u00f6ppel, K., Spanring, M., Auer, A., Prudnikova, O., Kopp, M., Klambauer, G., Brandstetter, J., and Hochreiter, S. (2024). xLSTM: Extended Long Short-Term Memory. arXiv."},{"key":"ref_32","unstructured":"Alkin, B., Beck, M., P\u00f6ppel, K., Hochreiter, S., and Brandstetter, J. (2024). Vision-LSTM: xLSTM as Generic Vision Backbone. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.3390\/ai5030071","article-title":"xlstmtime: Long-term time series forecasting with xlstm","volume":"5","author":"Alharthi","year":"2024","journal-title":"AI"},{"key":"ref_34","unstructured":"Chen, T., Ding, C., Zhu, L., Xu, T., Yan, W., Ji, D., Li, Z., and Zang, Y. (2024, January 10\u201313). xLSTM-UNet can be an Effective Backbone for 2D & 3D Biomedical Image Segmentation Better than its Mamba Counterparts. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics, Houston, TX, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fan, X., Tao, C., and Zhao, J. (2024, January 22\u201323). Advanced stock price prediction with xlstm-based models: Improving long-term forecasting. Proceedings of the 2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI), Melbourne, Australia.","DOI":"10.1109\/ISCMI63661.2024.10851608"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"42573","DOI":"10.1109\/JSEN.2024.3492004","article-title":"Sensor-Based Gymnastics Action Recognition Using Time-Series Images and a Lightweight Feature Fusion Network","volume":"24","author":"Wang","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Giarmatzis, G., Zacharaki, E.I., and Moustakas, K. (2020). Real-time prediction of joint forces by motion capture and machine learning. Sensors, 20.","DOI":"10.3390\/s20236933"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Stetter, B.J., Ringhof, S., Krafft, F.C., Sell, S., and Stein, T. (2019). Estimation of knee joint forces in sport movements using wearable sensors and machine learning. Sensors, 19.","DOI":"10.3390\/s19173690"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"82","DOI":"10.5334\/paah.41","article-title":"Effects of unstable shoes on lower limbs with different speeds","volume":"3","author":"Wang","year":"2019","journal-title":"Phys. Act. Health"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/S0021-9290(99)00192-X","article-title":"Biomechanical analysis of the stance phase during barefoot and shod running","volume":"33","author":"Aerts","year":"2000","journal-title":"J. Biomech."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"223","DOI":"10.5334\/paah.382","article-title":"Randomized Controlled Trial of Gastrocnemius Muscle Analysis Using Surface Electromyography and Ultrasound in Different Striking Patterns of Young Women\u2019s Barefoot Running","volume":"8","author":"Liu","year":"2024","journal-title":"Phys. Act. Health"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1504\/IJBET.2024.138706","article-title":"Can running technique modification benefit patellofemoral pain improvement in runners? A systematic review and meta-analysis","volume":"45","author":"Chang","year":"2024","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_44","first-page":"200","article-title":"Temporal kinematic and kinetics differences throughout different landing ways following volleyball spike shots","volume":"236","author":"Xu","year":"2022","journal-title":"Proc. Inst. Mech. Eng. Part P"},{"key":"ref_45","unstructured":"Fang, Z., Shi, K., and Han, Q. (2024). When Mamba Meets xLSTM: An Efficient and Precise Method with the XLSTM-VMUNet Model for Skin lesion Segmentation. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Cai, Y., and Fan, L. (2024). Seg-LSTM: Performance of xLSTM for Semantic Segmentation of Remotely Sensed Images. arXiv.","DOI":"10.1016\/j.heliyon.2024.e38495"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/4\/1249\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:37:10Z","timestamp":1760027830000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/4\/1249"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,18]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["s25041249"],"URL":"https:\/\/doi.org\/10.3390\/s25041249","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,18]]}}}