A respiration sensor for a chest-strap based wireless body sensor

Adams

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

et al. 2016

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Abstract-Energy efficiency is the most outstanding design criterion for wireless sensor nodes and especially wireless body sensors. Because a detailed measurement of the system's power consumption is not possible during the design process and often too complex for already manufactured devices, the power consumption has to be estimated. This leads to the need for a comprehensive and modular model for the power consumption of WSNs, which is proposed in this work. Due to the modular structure of the model the user is able to get a first estimate in an early stage of the design process (e.g. choose components) and to get a more accurate estimation later in the design process by lowering the abstraction level. This tackles the demanding trade-off between accuracy and usability in modeling.

Section: Future Workmentioning

confidence: 99%

Towards a comprehensive power consumption model for wireless sensor nodes

Adams

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

et al. 2016

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“…The heart rate is obtained from a 1-lead ECG based on textile electrodes. Respiration rate is extracted from the thoracic movements utilizing a force-sensing resistor [4]. The upper body acceleration is recorded with a MEMS accelerometer (LIS331H, STMicroelectronics), which integrates three sensitive axes.…”

Section: A Wireless Body Sensormentioning

confidence: 99%

“…It allows to record body movement as well as multiple physiological parameters. In particular we are able to measure a subject's heart rate, respiration rate and upper body acceleration (3-axis) [4,5]. Additionally, extended parameters such as the estimation of the user's velocity (walking and running) [5] or the physical activity level [6] are calculated.…”

Section: Introductionmentioning

confidence: 99%

A software assistant for user-centric calibration of a wireless body sensor

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

Adams

et al. 2016

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Abstract-Body sensors have a promising contribution to health promotion in many areas of daily life (telemedicine, corporate health care or recreational sports). However, the valid measurement of vital signs and kinematic data strongly depends on the signals' quality and the users' compliance (proper usage). Although, there is a lot of research work concerning accuracy and calibration of wireless body sensors the human user is typically not involved. Thus, in this work, we present a software assistant (wizard) that guides users during the process of attaching and setting up a wireless body sensor. Furthermore, insights of the implemented software as well as the utilized quality measures and calibration steps are given (ECG, respiration sensor and accelerometer). With the proposed software assistant, the users are instructed to correctly attach the body sensor and calibrate or verify the operability of the various sensor elements. The primary goal is to encourage compliance and the users' sense of control. In this way, we want to reduce faulty operation and ensure optimal signal quality.

“…1) enables us to determine a person's heart rate (from recording a 1-lead ECG) and respiration rate (from thoracic movements [15]). Furthermore, it allows recording of the skin temperature and the body accelerations ( fig.…”

Section: A Technical Backgroundmentioning

confidence: 99%

“…We used an additional data set from a previously performed experiment as a test set, on which 5 subjects (4 male, 1 female) participated [15]. The design was identical to the here described experiment, but it did not cover the initial sitting phase.…”

Section: Data Set and Data Processingmentioning

confidence: 99%

Robust estimation of physical activity by adaptively fusing multiple parameters

Christ

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

et al. 2015

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Abstract-Raising the awareness of being physically active by utilizing wearable body sensors has become a popular research topic. Recent approaches combine physical and physiological information to obtain a precise prediction of a person's physical activity ratio. However, the error in the determination of physical activity due to invalid physiological values that are resulting from underlying signal disturbances, has so far not been considered. We therefore present a robust measure of activity that fuses accelerometer data, heart rate and other personalized features, and is adaptively responding to missing physiological sensor data. To set up the model, we make use of regression analysis (MARS). Our findings indicate the need for considering signal quality when estimating physical activity. The predictive model shows close agreement (R 2 = 0.97) to the reference from indirect calorimetry, even if the physiological information is partly corrupted.