{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:23:27Z","timestamp":1778084607213,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education Collaborative Education Project of China","award":["230703711174142"],"award-info":[{"award-number":["230703711174142"]}]},{"name":"Ministry of Education Collaborative Education Project of China","award":["2023B01064"],"award-info":[{"award-number":["2023B01064"]}]},{"name":"Ministry of Education Collaborative Education Project of China","award":["YJ [2024]-30"],"award-info":[{"award-number":["YJ [2024]-30"]}]},{"name":"Zhanjiang Non-funded Science and Technology Tackling Plan Project","award":["230703711174142"],"award-info":[{"award-number":["230703711174142"]}]},{"name":"Zhanjiang Non-funded Science and Technology Tackling Plan Project","award":["2023B01064"],"award-info":[{"award-number":["2023B01064"]}]},{"name":"Zhanjiang Non-funded Science and Technology Tackling Plan Project","award":["YJ [2024]-30"],"award-info":[{"award-number":["YJ [2024]-30"]}]},{"name":"Lingnan Normal University Teaching Reform Project","award":["230703711174142"],"award-info":[{"award-number":["230703711174142"]}]},{"name":"Lingnan Normal University Teaching Reform Project","award":["2023B01064"],"award-info":[{"award-number":["2023B01064"]}]},{"name":"Lingnan Normal University Teaching Reform Project","award":["YJ [2024]-30"],"award-info":[{"award-number":["YJ [2024]-30"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human\u2013machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% \u00b1 1.4% accuracy, 68.7 ms \u00b1 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (\u03ba = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km\/h), while distributed learning across multiple devices maintains consistent accuracy (variance &lt; 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems.<\/jats:p>","DOI":"10.3390\/fi17070271","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T06:10:27Z","timestamp":1750399827000},"page":"271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["IoT-Edge Hybrid Architecture with Cross-Modal Transformer and Federated Manifold Learning for Safety-Critical Gesture Control in Adaptive Mobility Platforms"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3364-5890","authenticated-orcid":false,"given":"Xinmin","family":"Jin","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1876-989X","authenticated-orcid":false,"given":"Jian","family":"Teng","sequence":"additional","affiliation":[{"name":"College of Education, Sehan University, Yeongam 31746, Jeollanam-do, Republic of Korea"}]},{"given":"Jiaji","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang 524048, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lawitzky, A., Wollherr, D., and Buss, M. 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