MediAlly: A provenance-aware remote health monitoring middleware

Chowdhury, Atanu Roy; Falchuk, Ben; Misra, Archan

doi:10.1109/percom.2010.5466985

Cited by 30 publications

(32 citation statements)

References 16 publications

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“…In one previous work, Medially, middleware for remote health monitoring, collects contextual provenance and triggers collection of high-fidelity data when certain contextual constraints are satisfied [4]. The middleware allows reconstruction of the contextual trigger states to help the data consumer understand why the data was collected.…”

Section: Provenance In Mhealthmentioning

confidence: 99%

“…In the case of health data that is collected using mobile sensors, it is necessary to collect information about the data's origin, so that consumers can determine the accuracy and authenticity of the data; our framework provides these capabilities, unlike the provenance middleware that may exist on the EHR server. Provenance has been identified as one of the challenges for data collected using mobile health devices [3].In one previous work, Medially, middleware for remote health monitoring, collects contextual provenance and triggers collection of high-fidelity data when certain contextual constraints are satisfied [4]. The middleware allows reconstruction of the contextual trigger states to help the data consumer understand why the data was collected.…”

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confidence: 99%

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Provenance framework for mHealth

Prasad

Peterson

Mare

et al. 2013

2013 Fifth International Conference on Communication Systems and Networks (COMSNETS)

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Abstract-Mobile health technologies allow patients to collect their health information outside the hospital and share this information with others. But how can data consumers know whether to trust the sensor-collected and human-entered data they receive? Data consumers might be able to verify the accuracy and authenticity of the data if they have information about its origin and about changes made to it, i.e., the provenance of the data. We propose a provenance framework for mHealth devices, to collect and share provenance metadata and help the data consumer verify whether certain provenance properties are satisfied by the data they receive. This paper describes the programming model for this framework, which describes the rules to be implemented for providing provenance-collecting capabilities to an mHealth application. I. PROVENANCE IN MHEALTHConsider Jane, who is using one or more mobile health (mHealth) devices, continuously or periodically collecting her health-related information into her mobile phone. The phone periodically uploads this information, along with other healthrelated information that Jane manually inputs to her phone, to her electronic health record (EHR). Jane can then share her health information with her health providers, family and friends, peers, employer, insurer, and researchers. But how can these data consumers know whether to trust the sensorcollected and human-entered data they receive? What confidence do they have that it is accurate and authentic? Suppose Jane falls sick, and her husband Jack (her caregiver) collects her health information on her behalf. Jack has no experience using the devices. Can the system help him understand when he is not collecting accurate information?Since personal and home-use devices are not maintained regularly, like hospital devices, users like Jane and Jack might not realize when the devices are malfunctioning or uncalibrated. If other people in Jane's household use similar devices, they might accidentally confuse their own device for Jane's. mHealth devices, being mobile, can be stolen or misplaced -and then used by another person. Jane, or her caregivers, might deliberately fake or hide data collected using mHealth devices, when there is an incentive to do so. In all these scenarios, the data collected and shared by the devices might be inaccurate, or about the wrong person. If so, its use could prove damaging or even fatal, especially if the data is used by health providers for diagnosis or treatment.Data consumers might be able to verify the accuracy and authenticity of the data if they have information about its origin and about changes made to it, i.e., the provenance of the data. Previous research has looked at provenance of electronic health records and our work complements this research [1], [2]. In the case of health data that is collected using mobile sensors, it is necessary to collect information about the data's origin, so that consumers can determine the accuracy and authenticity of the data; our framework provides these capabilities, ...

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Section: Provenance In Mhealthmentioning

confidence: 99%

mentioning

confidence: 99%

Provenance framework for mHealth

Prasad

Peterson

Mare

et al. 2013

2013 Fifth International Conference on Communication Systems and Networks (COMSNETS)

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“…Ding et al (2010) proposed a framework for adaptive software that supports the online reconfiguration of each concern in the adaptation loop. Chowdhury et al (2010) presented a middleware for supporting energy-efficient, long-term remote health monitoring. Kang et al (2010) presented an active resource orchestration framework for mobile context monitoring.…”

Section: Software Designmentioning

confidence: 99%

A Picture of Present Ubicomp Research Exploring Publications from Important Events in the Field

Silva

Celes

Mota

et al. 2012

Journal of Applied Computing Research

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Abstract. In this work we use a dataset of papers published in top conferences focused on ubiquitous computing (ubicomp) to provide an overview and analysis of recent ubiquitous computing research performed internationally and in Brazil. The contributions of this study are twofold. First, we extracted useful information from our dataset such as representativeness of authors and institutions, and the formation of communities. Second, we analyzed all papers published between 2010 and 2011 in all top international conferences, creating a taxonomy of recent ubicomp research performed internationally. After that we mapped SBCUP papers (Brazilian ubicomp conference) according to this taxonomy, which enables the comparison of international and national research. This study is useful to guide novices in the field and it also provides experienced researchers with facts enabling the discussion of ubicomp research.

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“…To further reduce the energy overheads, the MediAlly prototype [8] used such inferred context to dynamically activate the collection of data from other external sensors. In contrast, our ACQUA framework seeks to optimize the data transfer during the process of context determination itself.…”

Section: Related and Prior Workmentioning

confidence: 99%

Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-Based Continuous Event Processing

Misra

Lim

2011

2011 IEEE 12th International Conference on Mobile Data Management

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Abstract-Many pervasive applications, such as activity recognition or remote wellness monitoring, utilize a personal mobile device (aka smartphone) to perform continuous processing of data streams acquired from locally-connected, wearable, sensors. To ensure the continuous operation of such applications on a battery-limited mobile device, it is essential to dramatically reduce the energy overhead associated with the process of sensor data acquisition and processing. To achieve this goal, this paper introduces a technique of 'acquisition-cost' aware continuous query processing, as part of the Acquisition Cost-Aware Query Adaptation (ACQUA) framework. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the smartphone, with a pull-based asynchronous model, where the phone retrieves appropriate blocks of sensor data from individual sensors, only when the stream elements are judged to be relevant to the query being processed. We describe algorithms that dynamically optimize the sequence (for complex stream queries with conjunctive and disjunctive predicates) in which such sensor data streams are retrieved by the phone, based on a combination of the communication cost and selectivity properties of individual sensor streams. Simulation experiments indicate that this approach can result in 70% reduction in the energy overhead of continuous query processing, without affecting the fidelity of the processing logic.

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MediAlly: A provenance-aware remote health monitoring middleware

Cited by 30 publications

References 16 publications

Provenance framework for mHealth

Provenance framework for mHealth

A Picture of Present Ubicomp Research Exploring Publications from Important Events in the Field

Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-Based Continuous Event Processing

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