This study presents a proof of concept for a contactless elevator operation
system aimed at minimizing human intervention while enhancing safety,
intelligence, and efficiency. A microcontroller-based edge device executing
tiny Machine Learning (tinyML) inferences is developed for elevator operation.
Using person detection and keyword spotting algorithms, the system offers
cost-effective and robust units requiring minimal infrastructural changes. The
design incorporates preprocessing steps and quantized convolutional neural
networks in a multitenant framework to optimize accuracy and response time.
Results show a person detection accuracy of 83.34% and keyword spotting
efficacy of 80.5%, with an overall latency under 5 seconds, indicating
effectiveness in real-world scenarios. Unlike current high-cost and
inconsistent contactless technologies, this system leverages tinyML to provide
a cost-effective, reliable, and scalable solution, enhancing user safety and
operational efficiency without significant infrastructural changes. The study
highlights promising results, though further exploration is needed for
scalability and integration with existing systems. The demonstrated energy
efficiency, simplicity, and safety benefits suggest that tinyML adoption could
revolutionize elevator systems, serving as a model for future technological
advancements. This technology could significantly impact public health and
convenience in multi-floor buildings by reducing physical contact and improving
operational efficiency, particularly relevant in the context of pandemics or
hygiene concerns.

本研究提出了一种概念验证，用于最小化人为干预、提高安全性、智能性和效率的非接触式电梯运行系统。使用微控制器边缘设备执行微小机器学习（tinyML）推断，通过人员检测和关键词识别算法，系统提供成本效益高且稳健的单元，无需基础设施的重大改变。该设计在多租户框架中整合了预处理步骤和量化的卷积神经网络，以优化准确性和响应时间。结果表明，人员检测准确率为 83.34％，关键词识别效果为 80.5％，整体延迟时间低于 5 秒，表明在实际场景中具有有效性。与当前高成本和不一致的非接触技术不同，该系统利用 tinyML 提供了一种成本效益高、可靠且可扩展的解决方案，提高用户安全性和运行效率，无需重大基础设施改变。该研究显示了有前景的结果，但仍需要进一步探索可扩展性和与现有系统的集成。所展示的能效、简易性和安全性优势表明，tinyML 的采用可能会改变电梯系统，成为未来技术进步的模型。这项技术可以极大地影响多层建筑中的公共健康和便利性，通过减少身体接触和提高运行效率，在流行病或卫生关注的背景下尤其相关。