{"title":"智能健康可穿戴技术特刊简介","authors":"D. Kotz, G. Xing","doi":"10.1145/3423967","DOIUrl":null,"url":null,"abstract":"Wearable health-tracking consumer products are gaining popularity, including smartwatches, fitness trackers, smart clothing, and head-mounted devices. These wearable devices promise new opportunities for the study of health-related behavior, for tracking of chronic conditions, and for innovative interventions in support of health and wellness. Next-generation wearable technologies have the potential to transform today’s hospitalcentered healthcare practices into proactive, individualized care. Although it seems new technologies enter the marketplace every week, there is still a great need for research on the development of sensors, sensor-data analytics, wearable interaction modalities, and more. In this special issue, we sought to assemble a set of articles addressing novel computational research related to any aspect of the design or use of wearables in medicine and health, including wearable hardware design, AI and data analytics algorithms, human-device interaction, security/privacy, and novel applications. Here, in Part 1 of a two-part collection of articles on this topic, we are pleased to share seven articles about the use of wearables for emotion sensing, physiotherapy, virtual reality, automated meal detection, a human data model, and a survey of physical-activity tracking. In the first article, “EmotionSense: An Adaptive Emotion Recognition System Based on Wearable Smart Devices”, Wang et al. propose an adaptive emotion recognition system based on smartwatches. The proposed approach first identifies user activities and employs an adaptive emotion-recognition method that extracts finegrained features from multi-mode sensory data and characterizes different emotions. This work demonstrates that wearable devices like smartwatches have made it possible to recognize physiological and behavioral patterns of humans in a convenient and non-invasive manner. In the next article, “Physiotherapy over a Distance: The Use of Wearable Technology for Video Consultations in Hospital Settings”, Aggarwal et al. report the findings of a field evaluation of a wearable technology, called SoPhy, in assessment of lower-limb movements in video consultations. The results show a number of advantages of the wearable systems like SoPhy, including helping physiotherapists in identifying subtle differences in the patient’s movements, increasing the diagnostic confidence of the physiotherapists and guiding more accurate assessment of the patients, and enhancing the overall clinician-patient communication in better understanding the therapy goals to the patients. Based on the findings, the article also presents design implications to guide further development of the video-consultation systems. Next, the article “On Shooting Stars: Comparing CAVE and HMD Immersive Virtual Reality Exergaming for Adults with Mixed Ability”, presents a study that explores the effects of two different iVR systems, the Cave Automated Virtual Environment (CAVE) and HTC Vive Head-Mounted Display (HMD), for use as a physicaltherapy system. Using an exercise game, Project Star Catcher (PSC), the authors conducted a cross-examination between impaired and non-impaired groups of n=40 users. 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These wearable devices promise new opportunities for the study of health-related behavior, for tracking of chronic conditions, and for innovative interventions in support of health and wellness. Next-generation wearable technologies have the potential to transform today’s hospitalcentered healthcare practices into proactive, individualized care. Although it seems new technologies enter the marketplace every week, there is still a great need for research on the development of sensors, sensor-data analytics, wearable interaction modalities, and more. In this special issue, we sought to assemble a set of articles addressing novel computational research related to any aspect of the design or use of wearables in medicine and health, including wearable hardware design, AI and data analytics algorithms, human-device interaction, security/privacy, and novel applications. Here, in Part 1 of a two-part collection of articles on this topic, we are pleased to share seven articles about the use of wearables for emotion sensing, physiotherapy, virtual reality, automated meal detection, a human data model, and a survey of physical-activity tracking. In the first article, “EmotionSense: An Adaptive Emotion Recognition System Based on Wearable Smart Devices”, Wang et al. propose an adaptive emotion recognition system based on smartwatches. The proposed approach first identifies user activities and employs an adaptive emotion-recognition method that extracts finegrained features from multi-mode sensory data and characterizes different emotions. This work demonstrates that wearable devices like smartwatches have made it possible to recognize physiological and behavioral patterns of humans in a convenient and non-invasive manner. In the next article, “Physiotherapy over a Distance: The Use of Wearable Technology for Video Consultations in Hospital Settings”, Aggarwal et al. report the findings of a field evaluation of a wearable technology, called SoPhy, in assessment of lower-limb movements in video consultations. The results show a number of advantages of the wearable systems like SoPhy, including helping physiotherapists in identifying subtle differences in the patient’s movements, increasing the diagnostic confidence of the physiotherapists and guiding more accurate assessment of the patients, and enhancing the overall clinician-patient communication in better understanding the therapy goals to the patients. Based on the findings, the article also presents design implications to guide further development of the video-consultation systems. 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引用次数: 0
摘要
可穿戴式健康追踪消费产品越来越受欢迎,包括智能手表、健身追踪器、智能服装和头戴式设备。这些可穿戴设备为研究与健康相关的行为、跟踪慢性病以及支持健康和保健的创新干预提供了新的机会。下一代可穿戴技术有可能将今天以医院为中心的医疗保健实践转变为主动的个性化护理。尽管似乎每周都有新技术进入市场,但仍然非常需要对传感器、传感器数据分析、可穿戴交互模式等的发展进行研究。在本期特刊中,我们试图收集一组文章,讨论与可穿戴设备在医疗和健康领域的设计或使用的任何方面相关的新颖计算研究,包括可穿戴硬件设计、人工智能和数据分析算法、人机交互、安全/隐私和新颖应用。在本文的第1部分,我们将分享七篇关于可穿戴设备在情感感知、物理治疗、虚拟现实、自动膳食检测、人类数据模型和身体活动跟踪调查方面的应用的文章。在第一篇文章“EmotionSense:基于可穿戴智能设备的自适应情绪识别系统”中,Wang等人提出了一种基于智能手表的自适应情绪识别系统。该方法首先识别用户活动,并采用自适应情绪识别方法,从多模式感官数据中提取细粒度特征,并表征不同的情绪。这项工作表明,像智能手表这样的可穿戴设备已经能够以一种方便和非侵入性的方式识别人类的生理和行为模式。在下一篇文章“远程物理治疗:在医院环境中使用可穿戴技术进行视频会诊”中,Aggarwal等人报告了一种名为SoPhy的可穿戴技术的现场评估结果,该技术用于评估视频会诊中的下肢运动。结果显示,像SoPhy这样的可穿戴系统有许多优势,包括帮助物理治疗师识别患者运动中的细微差异,提高物理治疗师的诊断信心,指导更准确的患者评估,以及加强临床与患者的整体沟通,更好地了解患者的治疗目标。基于研究结果,本文还提出了指导视频咨询系统进一步发展的设计启示。接下来,文章“On Shooting Stars: comparative CAVE and HMD Immersive Virtual Reality Exergaming for Adults with Mixed Ability”,提出了一项研究,探讨了两种不同的iVR系统,CAVE自动化虚拟环境(CAVE)和HTC Vive头戴式显示器(HMD)作为物理治疗系统的效果。利用一种名为Project Star Catcher (PSC)的运动游戏,作者在n=40名受损用户和非受损用户之间进行了交叉检查。结果表明,HMD - iVR系统在提高运动的身体表现和生理反应方面要有效得多
Introduction to the Special Issue on the Wearable Technologies for Smart Health
Wearable health-tracking consumer products are gaining popularity, including smartwatches, fitness trackers, smart clothing, and head-mounted devices. These wearable devices promise new opportunities for the study of health-related behavior, for tracking of chronic conditions, and for innovative interventions in support of health and wellness. Next-generation wearable technologies have the potential to transform today’s hospitalcentered healthcare practices into proactive, individualized care. Although it seems new technologies enter the marketplace every week, there is still a great need for research on the development of sensors, sensor-data analytics, wearable interaction modalities, and more. In this special issue, we sought to assemble a set of articles addressing novel computational research related to any aspect of the design or use of wearables in medicine and health, including wearable hardware design, AI and data analytics algorithms, human-device interaction, security/privacy, and novel applications. Here, in Part 1 of a two-part collection of articles on this topic, we are pleased to share seven articles about the use of wearables for emotion sensing, physiotherapy, virtual reality, automated meal detection, a human data model, and a survey of physical-activity tracking. In the first article, “EmotionSense: An Adaptive Emotion Recognition System Based on Wearable Smart Devices”, Wang et al. propose an adaptive emotion recognition system based on smartwatches. The proposed approach first identifies user activities and employs an adaptive emotion-recognition method that extracts finegrained features from multi-mode sensory data and characterizes different emotions. This work demonstrates that wearable devices like smartwatches have made it possible to recognize physiological and behavioral patterns of humans in a convenient and non-invasive manner. In the next article, “Physiotherapy over a Distance: The Use of Wearable Technology for Video Consultations in Hospital Settings”, Aggarwal et al. report the findings of a field evaluation of a wearable technology, called SoPhy, in assessment of lower-limb movements in video consultations. The results show a number of advantages of the wearable systems like SoPhy, including helping physiotherapists in identifying subtle differences in the patient’s movements, increasing the diagnostic confidence of the physiotherapists and guiding more accurate assessment of the patients, and enhancing the overall clinician-patient communication in better understanding the therapy goals to the patients. Based on the findings, the article also presents design implications to guide further development of the video-consultation systems. Next, the article “On Shooting Stars: Comparing CAVE and HMD Immersive Virtual Reality Exergaming for Adults with Mixed Ability”, presents a study that explores the effects of two different iVR systems, the Cave Automated Virtual Environment (CAVE) and HTC Vive Head-Mounted Display (HMD), for use as a physicaltherapy system. Using an exercise game, Project Star Catcher (PSC), the authors conducted a cross-examination between impaired and non-impaired groups of n=40 users. The results suggest that the HMD iVR system was far more effective in increasing both the physical performance and physiological response of the exercise