{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:35:44Z","timestamp":1785404144901,"version":"3.56.0"},"reference-count":28,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62501541"],"award-info":[{"award-number":["62501541"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62501541"],"award-info":[{"award-number":["62501541"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["GZC20241576"],"award-info":[{"award-number":["GZC20241576"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"award":["GZC20241576"],"award-info":[{"award-number":["GZC20241576"]}],"id":[{"id":"https:\/\/ror.org\/0426zh255","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Shanxi Province Basic Research Program","award":["202403021212341"],"award-info":[{"award-number":["202403021212341"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>Flexible strain sensors have attracted considerable attention in gait recognition owing to their ability to adhere directly to the skin near joints and transduce local deformation. In existing work, however, sensor placement and orientation are largely determined by anatomical experience, while multi-channel classification still relies on back-end digital processors, whose power consumption and latency constrain system practicality in wearable scenarios. This paper presents an integrated design path that proceeds from skin-mechanics theory through sensor-layout optimization to analog-domain front-end inference. On the layout side, the lines-of-non-extension (LoNE) theory is employed to convert the selection of sensor attachment angles from empirical judgment into a calculable mechanics problem; guided by the spatial course of LoNE in the ankle and knee regions, the positions and angles of the nine sensors are determined individually\u2014channels perpendicular to the LoNE capture maximum strain, channels offset by 45 degrees supplement non-sagittal-plane information, and a channel aligned along the LoNE provides a near-zero-strain reference. On the circuit side, the mathematical equivalence between the weighted summation of a linear classifier and Kirchhoff\u2019s current law (KCL) nodal current superposition is exploited to map the classification operation onto current aggregation in an analog circuit, yielding an in-sensor computing (ISC) front end in which the nine-channel weighted summation is completed in a single analog step. The sensors are fabricated by screen-printing a liquid-metal\u2013polymer composite conductive ink onto a TPU film substrate, with a gauge factor RSD of 6.8% and a tensile linearity R2&gt;0.99. Using walking, running, and stair descent as verification targets, the analog classifier reaches 99% accuracy at the circuit-level functional-verification stage. On real multi-subject data, it achieves 87.0%\u00b18.4% accuracy under intra-subject cross-session validation, with an analog-domain inference response faster than 100\u03bcs. This design path is not bound to a specific joint or sensor material; when the layout methodology is extended to additional joint regions and the circuit architecture incorporates multiple outputs to cover more classification categories, the same workflow remains applicable, offering a promising low-power, lightweight technical solution for wearable motion monitoring.<\/jats:p>","DOI":"10.3390\/mi17060710","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:50:58Z","timestamp":1781157058000},"page":"710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Flexible In-Sensor Computing Strain Sensor for Lower-Limb Gait Recognition"],"prefix":"10.3390","volume":"17","author":[{"given":"Jiayu","family":"Ma","sequence":"first","affiliation":[{"name":"School of Semiconductor and Physics, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyu","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Semiconductor and Physics, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4182-0729","authenticated-orcid":false,"given":"Ye","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Semiconductor and Physics, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Semiconductor and Physics, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongmin","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Semiconductor and Physics, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lam, M.H., Fong, D.T.P., Yung, P.S.H., and Chan, K.M. (2009). Knee stability assessment on anterior cruciate ligament injury: Clinical and biomechanical approaches. BMC Sports Sci. Med. Rehabil., 1.","DOI":"10.1186\/1758-2555-1-20"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, S., Francisco, G.E., and Zhou, P. (2018). Post-stroke hemiplegic gait: New perspective and insights. Front. Physiol., 9.","DOI":"10.3389\/fphys.2018.01021"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"ii37","DOI":"10.1093\/ageing\/afl084","article-title":"Falls in older people: Epidemiology, risk factors and strategies for prevention","volume":"35","author":"Rubenstein","year":"2006","journal-title":"Age Ageing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1743-0003-9-21","article-title":"A review of wearable sensors and systems with application in rehabilitation","volume":"9","author":"Patel","year":"2012","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1251\/bpo115","article-title":"Techniques of EMG signal analysis: Detection, processing, classification and applications","volume":"8","author":"Reaz","year":"2006","journal-title":"Biol. Proced. Online"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1002\/adfm.201504755","article-title":"Stretchable, skin-mountable, and wearable strain sensors and their potential applications: A review","volume":"26","author":"Amjadi","year":"2016","journal-title":"Adv. Funct. Mater."},{"key":"ref_7","first-page":"10908","article-title":"Wearable strain sensors using light transmittance change of carbon nanotube-embedded elastomers with microcracks","volume":"11","author":"Gu","year":"2019","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.compositesb.2018.12.107","article-title":"Direct printing of highly sensitive, stretchable, and durable strain sensor based on silver nanoparticles\/multi-walled carbon nanotubes composites","volume":"161","author":"Min","year":"2019","journal-title":"Compos. Part B Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"eadw3044","DOI":"10.1126\/sciadv.adw3044","article-title":"High-resolution liquid metal\u2013based stretchable electronics enabled by colloidal self-assembly and microtransfer printing","volume":"11","author":"Li","year":"2025","journal-title":"Sci. Adv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1038\/s41563-021-00921-8","article-title":"Highly stretchable multilayer electronic circuits using biphasic gallium-indium","volume":"20","author":"Liu","year":"2021","journal-title":"Nat. Mater."},{"key":"ref_11","unstructured":"Iberall, A.S. (1964). The Use of Lines of Nonextension to Improve Mobility in Full-Pressure Suits, Wright-Patterson Air Force Base. AMRL-TR-64-118."},{"key":"ref_12","unstructured":"Bethke, K. (2005). The Second Skin Approach: Skin Strain Field Analysis and Mechanical Counter Pressure Prototyping for Advanced Spacesuit Design. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3432","DOI":"10.1109\/TBME.2012.2215859","article-title":"Dynamic understanding of human-skin movement and strain-field analysis","volume":"59","author":"Wessendorf","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","unstructured":"Marreiros, S.S.P. (2010). Skin Strain Field Analysis of the Human Ankle Joint. [Master\u2019s Thesis, University of Lisbon]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.apergo.2014.11.016","article-title":"3D skin length deformation of lower body during knee joint flexion for the practical application of functional sportswear","volume":"48","author":"Choi","year":"2015","journal-title":"Appl. Ergon."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Obropta, E.W., and Newman, D.J. (2015, January 7\u201314). A comparison of human skin strain fields of the elbow joint for mechanical counter pressure space suit development. Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2015.7119176"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"167830","DOI":"10.1109\/ACCESS.2020.3022818","article-title":"Latest research trends in gait analysis using wearable sensors and machine learning: A systematic review","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2203830","DOI":"10.1002\/adma.202203830","article-title":"In-sensor computing: Materials, devices, and integration technologies","volume":"35","author":"Wan","year":"2023","journal-title":"Adv. Mater."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nature14441","article-title":"Training and operation of an integrated neuromorphic network based on metal-oxide memristors","volume":"521","author":"Prezioso","year":"2015","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1038\/s41586-020-2038-x","article-title":"Ultrafast machine vision with 2D material neural network image sensors","volume":"579","author":"Mennel","year":"2020","journal-title":"Nature"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1038\/s41378-025-01076-1","article-title":"Temperature field ultrafast detection and identification quantum sensor based on diamond array","volume":"11","author":"Gao","year":"2025","journal-title":"Microsyst. Nanoeng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3228","DOI":"10.1364\/PRJ.571967","article-title":"Diamond-neural-network magnetic sensors for ultrafast circuit fault detection and identification","volume":"13","author":"Gao","year":"2025","journal-title":"Photonics Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2300471","DOI":"10.1002\/advs.202300471","article-title":"Near-sensor reservoir computing for gait recognition via a multi-gate electrolyte-gated transistor","volume":"10","author":"Liu","year":"2023","journal-title":"Adv. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hasan, S., D\u2019auria, B.G., Mahmud, M.A.P., and Chowdhury, M.E.H. (2024). AI-aided gait analysis with a wearable device featuring a hydrogel sensor. Sensors, 24.","DOI":"10.3390\/s24227370"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alarfaj, M., Al Madini, A., Alsafran, A., Alruwaili, M., and Almutairi, N. (2024). Wearable sensors based on artificial intelligence models for human activity recognition. Front. Artif. Intell., 7.","DOI":"10.3389\/frai.2024.1424190"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shi, L.F., Liu, Z.Y., Zhou, K.J., and Wang, Y. (2023). Novel deep learning network for gait recognition using multimodal inertial sensors. Sensors, 23.","DOI":"10.3390\/s23020849"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bhat, G., Deb, R., Chaurasia, V.V., and Ogras, U.Y. (2018, January 5\u20138). Online human activity recognition using low-power wearable devices. Proceedings of the IEEE\/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, CA, USA.","DOI":"10.1145\/3240765.3240833"}],"container-title":["Micromachines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-666X\/17\/6\/710\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:04:02Z","timestamp":1781157842000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-666X\/17\/6\/710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,10]]},"references-count":28,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["mi17060710"],"URL":"https:\/\/doi.org\/10.3390\/mi17060710","relation":{},"ISSN":["2072-666X"],"issn-type":[{"value":"2072-666X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,10]]}}}