Context-aware physiological data acquisition and processing with wireless sensor networks

Marzencki, M.; Hung, Benny; Lin, Pao Tai; Huang, Yifeng; Cho, T.; Chuo, Yindar; Kaminska, Bożena

doi:10.1109/memea.2010.5480203

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…One of the improvements in this work compared to [13] is the selectable bandwidth of the analog frontend in addition to the variable sampling rate. A full bandwidth of 130 Hz fulfills requirements for high HRV accuracy [18] and beat classification for adults [26].…”

Section: Discussionmentioning

confidence: 99%

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Scalable ECG hardware and algorithms for extended runtime of wearable sensors

Tobola

Espig

Streit

et al. 2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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Everything in nature tries to reach the lowest possible energy level. Therefore any natural or artificial system must have the ability to adjust itself to the changing requirements of its surrounding environment. In this paper we address this issue by an ECG sensor designed to be adjustable during runtime, having the ability to reduce the power consumption at cost of the informational content. Accessible for everyone, standard ECG hardware and open source software has been used to realize an ECG processing system for wearable applications. The average power consumption has been measured for each mode of operation. Finally we take conclusion to conciser context-aware scaling as key feature to address the energy issue of wearable sensor systems

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Section: Discussionmentioning

confidence: 99%

“…An introduction to Context Aware Sensing is given in [12]. A related concept with focus on power efficiency through context awareness has been evaluated by Marzencki et al in [13]. We extended the number of scalable properties and compared the effects on power consumption qualitatively.…”

Section: Introductionmentioning

confidence: 99%

Scalable ECG hardware and algorithms for extended runtime of wearable sensors

Tobola

Espig

Streit

et al. 2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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“…The definition of the best positioning for the sensors improves the reliability of the data acquisition methods implemented by the mobile operating systems, improving the data acquisition and data processing techniques. The selection of the best data acquisition methods depends on the purpose of use, the type of data acquired and their environment [ 41 , 42 , 43 , 44 ].…”

Section: Sensorsmentioning

confidence: 99%

“…The type of application also influences the use of data processing methods. Data processing may be executed locally, using the capabilities of the mobile device, or at the server side, sending the collected data to a remote server, where the data processing methods are executed [ 42 , 45 , 46 ]. For server-side processing, the mobile device is only required to acquire the sensors data and to present the results to the user.…”

Section: Sensorsmentioning

confidence: 99%

“…[ 41 ] developed an open software architecture for patient monitoring, which supports semantic data processing and (soft) real-time reasoning. Usually, in medical environments, the context awareness and decision making are performed in a remote server, which returns the results to the mobile device [ 42 ]. Another option used for processing the collected data is the use of a cloud-based server that processes the data in real time.…”

Section: Sensorsmentioning

confidence: 99%

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From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Pires

García

Flórez-Revuelta

2016

Sensors

138

105

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This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

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Challenges in Physiological Signal Extraction from Cognitive Radio Wireless Body Area Networks for Emotion Recognition

Roshini

Kiran

2020

Computational Vision and Bio-Inspired Computing

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Context-aware physiological data acquisition and processing with wireless sensor networks

Cited by 9 publications

References 6 publications

Scalable ECG hardware and algorithms for extended runtime of wearable sensors

Scalable ECG hardware and algorithms for extended runtime of wearable sensors

From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Challenges in Physiological Signal Extraction from Cognitive Radio Wireless Body Area Networks for Emotion Recognition

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