{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T07:58:06Z","timestamp":1776412686813,"version":"3.51.2"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2026,3,16]]},"abstract":"<jats:p>\n                    This paper presents the design, implementation, and validation of a low-power smart insole system for gait event detection, with a focus on stance, swing, and step durations. Plantar pressure signals, captured via force-sensing resistors embedded in the heel and toe regions, were processed using a centered rolling mean and analyzed through a threshold-based detection algorithm. Ground-truth annotations were obtained from a time synchronized Qualisys motion capture system. Across 14 participants, covering more than 1,200 gait cycles at five controlled walking speeds, the insole-based algorithm demonstrated high accuracy in detecting heel-strike (HS) and toe-off (TO) events. Bland-Altman analysis revealed that stance and swing durations exhibited small speed-dependent biases (mean percent error &lt; 10%) that diminished at faster walking speeds, while step durations showed near-zero bias with tight dispersion (\u03c1 \u00b11%). Correlation analysis confirmed strong agreement with motion capture across all metrics (\n                    <jats:italic toggle=\"yes\">r<\/jats:italic>\n                    &gt; 0.95). These results indicate that despite simplified sensing hardware and cost-efficient design, the system achieves robust gait characterization sufficient for ambulatory monitoring and longitudinal assessment in real-world environments.\n                  <\/jats:p>","DOI":"10.1145\/3789675","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T17:51:14Z","timestamp":1773683474000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Embedded Smart Insole for Accurate Gait Event Detection"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1860-3300","authenticated-orcid":false,"given":"Jean Bosco","family":"Nkurunziza","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0902-4936","authenticated-orcid":false,"given":"Ross","family":"Brancati","sequence":"additional","affiliation":[{"name":"Kinesiology, University of Massachusetts Amherst, Amherst, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3740-2402","authenticated-orcid":false,"given":"Ramzi","family":"Majaj","sequence":"additional","affiliation":[{"name":"Center for human health and performance, Institute for alpplied life sciences, Amherst, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7468-0569","authenticated-orcid":false,"given":"Jeremy","family":"Gummeson","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9681-1902","authenticated-orcid":false,"given":"Michael","family":"Busa","sequence":"additional","affiliation":[{"name":"Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2022.100301"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBIO.2006.340213"},{"key":"e_1_2_1_3_1","volume-title":"Design and implementation of a lightweight smart insole for gait analysis. In 2017 IEEE Trustcom\/BigDataSE\/ICESS","author":"Cho Hyuntae","unstructured":"Hyuntae Cho. 2017. Design and implementation of a lightweight smart insole for gait analysis. In 2017 IEEE Trustcom\/BigDataSE\/ICESS. IEEE, 792\u2013797."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s24154765"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.2196\/rehab.5449"},{"key":"e_1_2_1_6_1","volume-title":"Smart insole: Remote gait detection algorithm using pressure sensors for toe walking rehabilitation. In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","author":"Ershadi Ghazal","unstructured":"Ghazal Ershadi, Migyeong Gwak, Afshin Aminian, Rahul Soangra, Marybeth Grant-Beuttler, and Majid Sarrafzadeh. 2021. Smart insole: Remote gait detection algorithm using pressure sensors for toe walking rehabilitation. In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT). IEEE, 332\u2013337."},{"key":"e_1_2_1_7_1","unstructured":"GAITRite. 2025. The GAITRite Gold Standard. https:\/\/www.gaitrite.com\/"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIA.2007.4295799"},{"key":"e_1_2_1_9_1","unstructured":"StepScan Technologies Inc. 2025. StepScan: Modular Pressure-Sensitive Floor Tiles for Gait and Balance Assessment. https:\/\/stepscan.com\/. Accessed: 2025-11-01."},{"key":"e_1_2_1_10_1","unstructured":"Tekscan Inc. 2025. Strideway Gait Analysis System. https:\/\/www.tekscan.com\/products-solutions\/systems\/strideway-system. Accessed: 2025-11-01."},{"key":"e_1_2_1_11_1","unstructured":"Tekscan Inc. 2025. Ultra-thin in-shoe sensors capture timing & pressure information for foot function & gait analysis. https:\/\/www.tekscan.com\/products-solutions\/systems\/f-scan-system. Accessed: 2025-11-01."},{"key":"e_1_2_1_12_1","unstructured":"Illinois Tool Works Inc. Instron. 2025. ElectroPuls E10000 Linear-Torsion Dynamic Test Instrument. https:\/\/www.instron.com\/wp-content\/uploads\/2024\/07\/e10000-combined-pod-2.pdf"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/cdn\/nzz050.P16-019-19"},{"key":"e_1_2_1_14_1","volume-title":"Design of IoT-Based Orthotic Footwear for Lower Limb Correction and Alignment. In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS)","volume":"1","author":"Mageshkumar G","year":"2024","unstructured":"G Mageshkumar, G Prasanna, G Sakthi Priya, and A Samkilbart. 2024. Design of IoT-Based Orthotic Footwear for Lower Limb Correction and Alignment. In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), Vol. 1. IEEE, 633\u2013638."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1123\/jmpb.2018-0035"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23156712"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.15761\/WCM.1000102"},{"key":"e_1_2_1_18_1","unstructured":"Novel.de. 2025. pedar: Dynamic pressure distribution inside the footwear. https:\/\/novel.de\/products\/pedar\/. Accessed: 2025-11-01."},{"key":"e_1_2_1_19_1","unstructured":"B&K Precision. 2025. Model 5492B (5492B and 5492BGPIB) 5 1\/2 Digit Bench Digital Multimeter. https:\/\/www.bkprecision.com\/products\/multimeters\/5492B"},{"key":"e_1_2_1_20_1","unstructured":"Qualisys. 2025. 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