Kush Joseph C
Department of Instruction and Leadership, Duquesne University, Pittsburgh, PA 15282, USA.
Sensors (Basel). 2025 Jan 4;25(1):249. doi: 10.3390/s25010249.
This article examines how sensor technologies (such as environmental sensors, biometric sensors, and IoT devices) intersect with conversational AI models like ChatGPT 4.0. In particular, this article explores how data from different sensors in real time can improve AI models' comprehension of surroundings, user contexts, and physical conditions. Lastly, this article delves into the scientific principles supporting sensor technologies, data processing methods, and their fusion with generative models such as ChatGPT to develop adaptable, dynamic systems that engage with humans intelligently in real time. Some of the specific topics that are investigated include the science behind sensor networks and acquiring real-time data, how ChatGPT can analyze sensor data to generate dialogue that is sensitive to context, instances in healthcare (such as using wearable sensors along with AI chatbots for patient treatment), and smart homes (interaction with AI assistants driven by sensors). These subjects will prove advantageous for researchers in sensor technology as well as AI development, showcasing interdisciplinary progress in smart systems.
本文探讨了传感器技术（如环境传感器、生物识别传感器和物联网设备）如何与ChatGPT 4.0等对话式人工智能模型相互交叉。具体而言，本文探讨了来自不同传感器的实时数据如何提高人工智能模型对周围环境、用户背景和身体状况的理解。最后，本文深入研究了支持传感器技术的科学原理、数据处理方法，以及它们与ChatGPT等生成模型的融合，以开发能够实时与人类智能互动的适应性强、动态的系统。所研究的一些具体主题包括传感器网络背后的科学以及获取实时数据、ChatGPT如何分析传感器数据以生成对上下文敏感的对话、医疗保健领域的实例（如使用可穿戴传感器与人工智能聊天机器人进行患者治疗）以及智能家居（与由传感器驱动的人工智能助手的交互）。这些主题将对传感器技术和人工智能开发领域的研究人员具有优势，展示了智能系统中的跨学科进展。
