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Proceedings of the 2014 workshop on physical analytics最新文献

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Socio-physical analytics: challenges & opportunities 社会物理分析:挑战与机遇
Pub Date : 2014-06-11 DOI: 10.1145/2611264.2611265
Archan Misra, Kasthuri Jayarajah, Shriguru Nayak, Philips Kokoh Prasetyo, Ee-Peng Lim
In this paper, we argue for expanded research into an area called Socio-Physical Analytics, that focuses on combining the behavioral insight gained from mobile-sensing based monitoring of physical behavior with the inter-personal relationships and preferences deduced from online social networks. We highlight some of the research challenges in combining these heterogeneous data sources and then describe some examples of our ongoing work (based on real-world data being collected at SMU) that illustrate two aspects of socio-physical analytics: (a) how additional demographic and online analytics based attributes can potentially provide better insights into the preferences and behaviors of individuals or groups (in terms of movement prediction and understanding of physical vs. online interactions), and (b) how online and physical interactions can help us discover latent characteristics of physical spaces and entities.
在本文中,我们主张将研究扩展到一个名为社会物理分析的领域,该领域的重点是将基于移动传感的身体行为监测所获得的行为洞察力与从在线社交网络推断的人际关系和偏好相结合。我们强调了结合这些异构数据源的一些研究挑战,然后描述了我们正在进行的工作的一些例子(基于在新大收集的真实数据),这些例子说明了社会物理分析的两个方面:(a)额外的人口统计和基于在线分析的属性如何能够更好地洞察个人或群体的偏好和行为(在运动预测和对物理与在线交互的理解方面),以及(b)在线和物理交互如何帮助我们发现物理空间和实体的潜在特征。
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引用次数: 11
Physical analytics to model health behaviors 模拟健康行为的物理分析
Pub Date : 2014-06-11 DOI: 10.1145/2611264.2611272
Anmol Madan
Mobile phones are a pervasive platform for opportunistic sensing of social and health related behaviors. In this talk, I discuss how sensor data from mobile phones can be used to model and predict health outcomes. The talk starts with a review of research at the MIT Media Lab, and then transitions into how Ginger.io has built a commercial platform to collect, annotate, analyze and drive healthcare interventions at scale, deployed with major US hospital systems and healthcare providers. The Ginger.io three-part platform -- patient app, behavioral analytics engine, and provider dashboard -- applies this technology to give care providers a window into their patients' health between office visits. Our mobile app uses smartphone sensors to passively collect information about a patient's daily patterns. Using this data, our machine learning models are able to detect at-risk patients significantly better than the standard of care. Any concerning changes in behavior are communicated to the provider through our simple, action-oriented web dashboard. Ginger.io is part of the care solutions at institutions such as Kaiser Permanente, Novant Health, UCSF, Duke Medical and Cincinnati Children's.
移动电话是一个无处不在的平台,机会主义地感知社会和健康相关行为。在这次演讲中,我将讨论如何使用来自移动电话的传感器数据来建模和预测健康结果。演讲以回顾麻省理工学院媒体实验室的研究开始,然后过渡到姜是如何。io建立了一个商业平台,用于大规模收集、注释、分析和推动医疗保健干预措施,并与美国主要的医院系统和医疗保健提供商一起部署。姜。IO由三部分组成的平台——患者应用程序、行为分析引擎和提供商仪表板——应用这项技术,为医护人员提供了一个了解患者就诊间隙健康状况的窗口。我们的移动应用程序使用智能手机传感器被动地收集有关患者日常模式的信息。使用这些数据,我们的机器学习模型能够比标准护理更好地检测出高危患者。任何有关行为的变化都通过我们简单的、面向动作的web仪表板传达给提供商。姜。io是Kaiser Permanente、Novant Health、UCSF、Duke Medical和辛辛那提儿童医院等机构护理解决方案的一部分。
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引用次数: 0
GlimpseData: towards continuous vision-based personal analytics GlimpseData:面向基于视觉的持续个人分析
Pub Date : 2014-06-11 DOI: 10.1145/2611264.2611269
Seungyeop Han, R. Nandakumar, Matthai Philipose, A. Krishnamurthy, D. Wetherall
Emerging wearable devices provide a new opportunity for mobile context-aware applications to use continuous audio/video sensing data as primitive inputs. Due to the high-datarate and compute-intensive nature of the inputs, it is important to design frameworks and applications to be efficient. We present the GlimpseData framework to collect and analyze data for studying continuous high-datarate mobile perception. As a case study, we show that we can use low-powered sensors as a filter to avoid sensing and processing video for face detection. Our relatively simple mechanism avoids processing roughly 60% of video frames while missing only 10% of frames with faces in them.
新兴的可穿戴设备为移动环境感知应用提供了一个新的机会,可以使用连续的音频/视频传感数据作为原始输入。由于输入的高数据量和计算密集型性质,设计框架和应用程序以提高效率非常重要。我们提出了GlimpseData框架来收集和分析数据,以研究连续的高数据率移动感知。作为一个案例研究,我们表明我们可以使用低功率传感器作为滤波器,以避免在人脸检测中感知和处理视频。我们相对简单的机制可以避免处理大约60%的视频帧,同时只丢失10%的有人脸的帧。
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引用次数: 20
Session details: Wireless tracking 会话细节:无线跟踪
Pub Date : 2014-06-11 DOI: 10.1145/3255792
Archan Misra
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引用次数: 0
Session details: Human and social sensing 会议细节:人类和社会感知
Pub Date : 2014-06-11 DOI: 10.1145/3255791
Ramón Cáceres
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引用次数: 0
期刊
Proceedings of the 2014 workshop on physical analytics
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