{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:20:04Z","timestamp":1792131604701,"version":"4.4.0"},"reference-count":35,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T00:00:00Z","timestamp":1731974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deakin University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable devices have revolutionized real-time health monitoring, yet challenges persist in enhancing their flexibility, weight, and accuracy. This paper presents the development of a wearable device employing a conductive polyacrylamide\u2013lithium chloride\u2013MXene (PLM) hydrogel sensor, an electronic circuit, and artificial intelligence (AI) for gait monitoring. The PLM sensor includes tribo-negative polydimethylsiloxane (PDMS) and tribo-positive polyurethane (PU) layers, exhibiting extraordinary stretchability (317% strain) and durability (1000 cycles) while consistently delivering stable electrical signals. The wearable device weighs just 23 g and is strategically affixed to a knee brace, harnessing mechanical energy generated during knee motion which is converted into electrical signals. These signals are digitized and then analyzed using a one-dimensional (1D) convolutional neural network (CNN), achieving an impressive accuracy of 100% for the classification of four distinct gait patterns: standing, walking, jogging, and running. The wearable device demonstrates the potential for lightweight and energy-efficient sensing combined with AI analysis for advanced biomechanical monitoring in sports and healthcare applications.<\/jats:p>","DOI":"10.3390\/s24227370","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T11:58:07Z","timestamp":1732017487000},"page":"7370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["AI-Aided Gait Analysis with a Wearable Device Featuring a Hydrogel Sensor"],"prefix":"10.3390","volume":"24","author":[{"given":"Saima","family":"Hasan","sequence":"first","affiliation":[{"name":"School of Engineering, Deakin University, Geelong, VIC 3216, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brent G.","family":"D\u2019auria","sequence":"additional","affiliation":[{"name":"School of Engineering, Deakin University, Geelong, VIC 3216, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1905-6800","authenticated-orcid":false,"given":"M. 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(2023). IMU-based motion capture system for rehabilitation applications: A systematic review. Biomim. Intell. Robot., 3.","DOI":"10.1016\/j.birob.2023.100097"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Manupibul, U., Tanthuwapathom, R., Jarumethitanont, W., Kaimuk, P., Limroongreungrat, W., and Charoensuk, W. (2023). Integration of force and IMU sensors for developing low-cost portable gait measurement system in lower extremities. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-37761-2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Larracy, R., Phinyomark, A., and Scheme, E. (2023, January 18\u201320). Gait Representation: From Vision-Based to Floor Sensor-Based Gait Recognition. Proceedings of the 2023 IEEE Sensors Applications Symposium (SAS), Ottawa, ON, Canada.","DOI":"10.1109\/SAS58821.2023.10254014"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mao, Y., Ogata, T., Ora, H., Tanaka, N., and Miyake, Y. (2021). Estimation of stride-by-stride spatial gait parameters using inertial measurement unit attached to the shank with inverted pendulum model. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-81009-w"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16839","DOI":"10.1039\/D4TA02979H","article-title":"A flexible wearable sensor based on anti-swelling conductive hydrogels for underwater motion posture visualization assisted by deep learning","volume":"12","author":"Qi","year":"2024","journal-title":"J. Mater. Chem. A"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3926","DOI":"10.1039\/D3GC04487D","article-title":"Robust hydrogel sensor with good mechanical properties, conductivity, anti-swelling ability, water tolerance and biocompatibility","volume":"26","author":"Xu","year":"2024","journal-title":"Green Chem."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Khan, S.A., Ahmad, H., Zhu, G., Pang, H., and Zhang, Y. (2024). Three-Dimensional Printing of Hydrogels for Flexible Sensors: A Review. Gels, 10.","DOI":"10.3390\/gels10030187"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cao, J., Wu, B., Yuan, P., Liu, Y., and Hu, C. (2024). Progress of Research on Conductive Hydrogels in Flexible Wearable Sensors. Gels, 10.","DOI":"10.3390\/gels10020144"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9371","DOI":"10.1039\/D3TA08069B","article-title":"Conductive nanocomposite hydrogels for flexible wearable sensors","volume":"12","author":"Guo","year":"2024","journal-title":"J. Mater. Chem. A"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Jin, T., Cai, J., Xu, L., He, T., Tian, Y., Li, L., Peng, Y., and Lee, C. (2022). Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications. Adv. Sci., 9.","DOI":"10.1002\/advs.202103694"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Marimon, X., Mengual, I., Celis, C., Portela, A., Sanz, J., Herraez, I., and Bellmunt, A. (2024). Kinematic Analysis of Human Gait in Healthy Young Adults Using IMU Sensors: Exploring Relevant Machine Learning Features for Clinical Applications. Bioengineering, 11.","DOI":"10.3390\/bioengineering11020105"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Willi, R., Werner, C., Demko, L., Be, L., Filli, B., Zorner, B., Curt, A., and Bolliger, M. (2024). Reliability of patient-specific gait profiles with inertial measurement units during the 2-min walk test in incomplete spinal cord injury. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-53301-y"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xue, H., Mi, Z., Shi, L., Yang, X., Chen, R., Luo, X., and Guan, Y. (2023). A high-toughness, tailorable, wearable multifunctional sensor based on multisynergistic fabric-hydrogel constructed via dual-function boric acid bridge. Mater. Today Chem., 33.","DOI":"10.1016\/j.mtchem.2023.101696"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, T., Xu, C., Lu, C., Ye, Y., Sun, Z., Wang, B., and Luo, Z. (2022). Conductive polymer hydrogels crosslinked by electrostatic interaction with PEDOT dopant for bioelectronics application. Chem. Eng. J., 429.","DOI":"10.1016\/j.cej.2021.132430"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hina, M., Bashir, S., Kamran, K., Iqbal, J., Ramesh, S., and Ramesh, K. (2021). Fabrication of aqueous solid-state symmetric supercapacitors based on self-healable poly (acrylamide)\/PEDOT composite hydrogel electrolytes. Mater. Chem. Phys., 273.","DOI":"10.1016\/j.matchemphys.2021.125125"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ni, Y., Zang, X., Yang, Y., Gong, Z., Li, H., Chen, J., Wu, C., Huang, J., and Lai, Y. (2024). Environmental Stability Stretchable Organic Hydrogel Humidity Sensor for Respiratory Monitoring with Ultrahigh Sensitivity. Adv. Funct. Mater., 34.","DOI":"10.1002\/adfm.202402853"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.jcis.2023.12.039","article-title":"Fast gelling, high performance MXene hydrogels for wearable sensors","volume":"658","author":"Zhang","year":"2024","journal-title":"J. Colloid Interface Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chae, A., Murali, G., Lee, S.-Y., Gwak, J., Kim, S., Jeong, Y., Kang, H., Park, S., Lee, A., and Koh, D.-Y. (2023). Highly Oxidation-Resistant and Self-Healable MXene-Based Hydrogels for Wearable Strain Sensor. Adv. Funct. Mater., 33.","DOI":"10.1002\/adfm.202213382"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, W., Zeng, Y.P., Tian, B., and Liang, J. (2024). MXene-based Dual Network Hydrogel as Flexible Strain Sensor for Human Actions Recognition. IEEE J. Flex. Electron., 1.","DOI":"10.1109\/JFLEX.2024.3412875"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"10991","DOI":"10.1039\/D4TA00038B","article-title":"MXene\/CdS photothermal\u2013photocatalytic hydrogels for efficient solar water evaporation and synergistic degradation of VOC","volume":"12","author":"Wang","year":"2024","journal-title":"J. Mater. Chem. A"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, B., Wong, P.W., and An, A.K. (2022). Photothermally enabled MXene hydrogel membrane with integrated solar-driven evaporation and photodegradation for efficient water purification. Chem. Eng. J., 430.","DOI":"10.1016\/j.cej.2021.133054"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zeng, W., Ye, X., Dong, Y., Zhang, Y., Sun, C., Zhang, T., Guan, X., and Guo, L. (2024). MXene for photocatalysis and photothermal conversion: Synthesis, physicochemical properties, and applications. Coord. Chem. Rev., 508.","DOI":"10.1016\/j.ccr.2024.215753"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6102","DOI":"10.1039\/D4TB00809J","article-title":"A flexible, stretchable and wearable strain sensor based on physical eutectogels for deep learning-assisted motion identification","volume":"12","author":"Liu","year":"2024","journal-title":"J. Mater. Chem. B"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tang, C., Xu, M., Yi, W., Zhang, Z., Occhipinti, E., Dong, C., Ravenscroft, D., Jung, S.-M., Lee, S., and Gao, J. (2024). Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency. NPJ Flex. Electron., 8.","DOI":"10.1038\/s41528-024-00315-1"},{"key":"ref_25","first-page":"23761","article-title":"Bionic Spider Web Flexible Strain Sensor Based on CF-L and Machine Learning","volume":"16","author":"Zou","year":"2024","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhou, L., Fischer, E., Brahms, C.M., Granacher, U., and Arnrich, B. (2023). DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units. Sci. Data, 10.","DOI":"10.1038\/s41597-023-02391-w"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Santos, G., Wanderley, M., Tavares, T., and Rocha, A. (2022). A multi-sensor human gait dataset captured through an optical system and inertial measurement units. Sci. Data, 9.","DOI":"10.1038\/s41597-022-01638-2"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sotirakis, C., Su, Z., Brzezicki, M., Conway, N., Tarassenko, L., FitzGerald, J., and Antoniades, C. (2023). Identification of motor progression in Parkinson\u2019s disease using wearable sensors and machine learning. NPJ Park. Dis., 9.","DOI":"10.1038\/s41531-023-00581-2"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zadka, A., Rabin, N., Gazit, E., Mirelman, A., Nieuwboer, A., Rochester, L., Din, S., Pelosin, E., Avanzino, L., and Bloem, B. (2024). A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders. NPJ Digit. Med., 7.","DOI":"10.1038\/s41746-024-01136-2"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hang, C.-Z., Zhao, X.-F., Xi, S.-Y., Shang, Y.-H., Yuan, K.-P., Yang, F., Wang, Q.-G., Wang, J.-C., Zhang, D.W., and Lu, H.-L. (2020). Highly stretchable and self-healing strain sensors for motion detection in wireless human-machine interface. Nano Energy, 76.","DOI":"10.1016\/j.nanoen.2020.105064"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1039\/D2TC05038B","article-title":"A facilely prepared notch-insensitive nanocomposite organohydrogel-based flexible wearable device for long-term outdoor human motion monitoring and recognition","volume":"11","author":"He","year":"2023","journal-title":"J. Mater. Chem. C"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Weiss, G.M., Timko, J.L., Gallagher, C.M., Yoneda, K., and Schreiber, A.J. (2016, January 24\u201327). Smartwatch-based activity recognition: A machine learning approach. Proceedings of the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Las Vegas, NV, USA.","DOI":"10.1109\/BHI.2016.7455925"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ile\u0219an, R.R., Cordo\u0219, C.-G., Mih\u0103il\u0103, L.-I., Fle\u0219ar, R., Popescu, A.-S., Perju-Dumbrav\u0103, L., and Farag\u00f3, P. (2022). Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson\u2019s Disease Management Optimization. Biosensors, 12.","DOI":"10.3390\/bios12040189"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, S., Suresh, L., Yang, J., Zhang, X., and Tan, S.C. (2022). Augmenting Sensor Performance with Machine Learning Towards Smart Wearable Sensing Electronic Systems. Adv. Intell. Syst., 4.","DOI":"10.1002\/aisy.202270016"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jbi.2018.11.003","article-title":"A Smartwatch-Based Framework for Real-Time and Online Assessment and Mobility Monitoring","volume":"89","author":"Kheirkhahan","year":"2019","journal-title":"J. Biomed. 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