This Thematic Issue on “Situation-aware Intelligent Environments” of JAISE consists of five selected papers from the 14th International Conference on Intelligent Environments 2018, which was held in Rome (Italy) during June 25–28, 2018. Intelligent Environments is one of the main components of the concept of Ambient Intelligence (AmI) and covers some key areas of research including, but not limited to, human activity recognition, humanenvironment interaction, and various machine learning and data mining techniques for applications such as smart home, healthcare, and smart cities. The emerging integration of objects able to sense, reason and communicate is enabling a new generation of services that assist the user in everyday tasks. For being effective, these services must be aware of the current situation, which includes not only location, but also current activity, mood, and social context, just to name a few. Objects in intelligent environments provide extensive low-level data that can be mined for capturing a fine-grained picture of the user’s situation. Integrating and mining those data for situation-awareness is a challenging topic, which involves artificial intelligence, knowledge representation and reasoning, big data analysis, security, trust, and privacy issues. The five papers appearing in this Thematic Issue cover a wide range of important topics for situationaware intelligent environments. In smart manufacturing environments, wearable technologies and sensors bring the opportunity to use the recorded data to deliver timely assistance to the operator and to optimize work processes. However, the continuous acquisition of workers’ sensor data determines relevant trust and privacy issues. The paper “A trust and privacy framework for smart manufacturing environments” by Mannhardt, Peterson, and Oliveira, proposes a framework to address trust and privacy challenges in complex smart manufacturing systems. The rise of self-driving cars is paving the way to novel naturalistic interaction modalities, including feet gesture interaction. The paper “Enabling driver feet gestures using capacitive proximity sensing” by Frank and Kuijper, proposes and evaluates a technique to distinguish among four feet gestures; the technique does not cause privacy issues and has no visible interior design impact. Heart disease and strokes are among the leading causes of death. Currently, electrocardiography monitoring is the only tool that helps physicians diagnose hearth issues. In the work “Utilising fog computing for developing a person-centric heart monitoring system” by Akrivopoulos, Amaxilatis, Mavrommati, and Chatzigiannakis, a novel system is proposed, which combines wearable embedded devices, mobile edge devices, and cloud services to provide reliable and accurate heart monitoring. Today’s low cost robots can perform their specific tasks, but they cannot cooperate to execute complex cooperative tasks. The paper “Tidy up my room: Multi-agent cooperation for service tasks in smart environments” by Rasch, Sprute, Pörtner, Battermann, and König, presents a system supported by cameras and lights to detect pointing gestures from a user and locating objects. The system in-

中文翻译：

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情境感知型智能环境

JAISE的“情境感知智能环境”主题问题包括2018年6月25日至28日在罗马（意大利）举行的第14届国际智能环境会议2018的五篇论文。智能环境是其中之一。环境智能（AmI）概念的主要组成部分，涵盖了一些关键研究领域，包括但不限于人类活动识别，人类环境交互以及针对诸如智能家居，医疗保健和医疗保健等应用的各种机器学习和数据挖掘技术智慧城市。能够感知，推理和交流的对象的新兴集成使新一代服务能够帮助用户执行日常任务。为了提高效率，这些服务必须了解当前情况，不仅包括位置，以及当前的活动，情绪和社交环境，仅举几例。智能环境中的对象提供了广泛的低级数据，可对其进行挖掘以捕获用户情况的细粒度图片。集成和挖掘这些数据以增强态势感知是一个具有挑战性的主题，其中涉及人工智能，知识表示和推理，大数据分析，安全性，信任和隐私问题。本专题发行的五篇论文涵盖了情境感知智能环境的众多重要主题。在智能制造环境中，可穿戴技术和传感器使人们有机会使用记录的数据为操作人员提供及时的帮助并优化工作流程。然而，连续获取工人的传感器数据决定了相关的信任和隐私问题。Mannhardt，Peterson和Oliveira的论文“智能制造环境的信任和隐私框架”提出了一个框架，以解决复杂的智能制造系统中的信任和隐私挑战。自动驾驶汽车的兴起为新型自然主义交互方式（包括脚手势交互）铺平了道路。Frank and Kuijper的论文“使用电容式接近感应启用驾驶员脚部手势”，提出并评估了一种区分四个脚部手势的技术。该技术不会引起隐私问题，也不会对室内设计产生明显影响。心脏病和中风是导致死亡的主要原因。目前，心电图监测是唯一可帮助医生诊断炉膛问题的工具。Akrivopoulos，Amaxilatis，Mavrommati和Chatzigiannakis撰写的“利用雾计算开发以人为中心的心脏监控系统”一文中，提出了一种新颖的系统，该系统结合了可穿戴嵌入式设备，移动边缘设备和云服务，可提供可靠的准确的心脏监护。当今的低成本机器人可以执行其特定任务，但无法协作执行复杂的协作任务。Rasch，Sprute，Pörtner，Battermann和König撰写的论文“整理我的房间：在智能环境中完成服务任务的多主体协作”提出了一种由照相机和照明灯支持的系统，用于检测用户的指向手势和定位对象。系统输入- Akrivopoulos，Amaxilatis，Mavrommati和Chatzigiannakis撰写的“利用雾计算开发以人为中心的心脏监控系统”一文中，提出了一种新颖的系统，该系统结合了可穿戴嵌入式设备，移动边缘设备和云服务，可提供可靠的准确的心脏监护。当今的低成本机器人可以执行其特定任务，但无法协作执行复杂的协作任务。Rasch，Sprute，Pörtner，Battermann和König撰写的论文“整理我的房间：在智能环境中完成服务任务的多主体协作”提出了一种由照相机和照明灯支持的系统，用于检测用户的指向手势和定位对象。系统输入- Akrivopoulos，Amaxilatis，Mavrommati和Chatzigiannakis撰写的“利用雾计算开发以人为中心的心脏监控系统”一文中，提出了一种新颖的系统，该系统结合了可穿戴嵌入式设备，移动边缘设备和云服务，可提供可靠的准确的心脏监护。当今的低成本机器人可以执行其特定任务，但无法协作执行复杂的协作任务。Rasch，Sprute，Pörtner，Battermann和König撰写的论文“整理我的房间：在智能环境中完成服务任务的多主体协作”提出了一种由照相机和照明灯支持的系统，用于检测用户的指向手势和定位对象。系统输入- 它结合了可穿戴嵌入式设备，移动边缘设备和云服务，以提供可靠而准确的心脏监护。当今的低成本机器人可以执行其特定任务，但无法协作执行复杂的协作任务。Rasch，Sprute，Pörtner，Battermann和König撰写的论文“整理我的房间：在智能环境中完成服务任务的多主体协作”提出了一种由照相机和照明灯支持的系统，用于检测用户的指向手势和定位对象。系统输入- 它结合了可穿戴嵌入式设备，移动边缘设备和云服务，以提供可靠而准确的心脏监护。当今的低成本机器人可以执行其特定任务，但无法协作执行复杂的协作任务。Rasch，Sprute，Pörtner，Battermann和König撰写的论文“整理我的房间：在智能环境中完成服务任务的多主体协作”提出了一种由照相机和照明灯支持的系统，用于检测用户的指向手势和定位对象。系统输入- Rasch，Sprute，Pörtner，Battermann和König的Ras，Sprute，Pörtner，Battermann和König提出的多智能体合作，解决了智能环境中的服务任务，提出了一个由摄像头和灯光支持的系统，用于检测用户的指向手势和定位对象。系统输入- Rasch，Sprute，Pörtner，Battermann和König的Ras，Sprute，Pörtner，Battermann和König提出的多智能体合作，解决了智能环境中的服务任务，提出了一个由摄像头和灯光支持的系统，用于检测用户的指向手势和定位对象。系统输入-