Hassle free fitness monitoring

Jea, David; Liu, Jason; Schmid, Thomas; Srivastava, Mani

doi:10.1145/1515747.1515756

Cited by 16 publications

(10 citation statements)

References 3 publications

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“…As stated in [31], at low data rates of 60 bytes/s and transmission frequency of about once every 0.5-1 s (the usual required data rate for transmitting physiological data like ECG signals and other vital body parameters) would result in a cell phone's battery being drained completely within 10-12 h-even while transmitting data over a strong connection [40,41]. In other words, this implies a recharge cycle of every half a day for a user.…”

Section: Simulation Results With Some Real-life Datamentioning

confidence: 99%

“…For evaluation of energy savings generated by CNS for healthcare applications, we assumed that such applications would involve the transmission of ECG signals, JPEG images of blood samples and ASCII text data representing various physiological parameters like blood pressure, blood sugar level, temperature, etc. [31,40]. In this context, we benchmarked the performance of CNS on the 50 real-life ECG signal samples used in [31], that were obtained from the QT database in [42].…”

Section: Simulation Results With Some Real-life Datamentioning

confidence: 99%

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CNS: a new energy efficient transmission scheme for wireless sensor networks

Sinha

Datta

2010

Wireless Netw

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In this project we propose an Energy efficient transmission scheme for wireless sensor networks using Compression with Null Symbol (CNS). In this scheme we combine the power of data compression and communication through silent symbol. The average theoretical energy saving is more at the transmitter by CNS when calculated with an ideal channel and for equal likelihood of all possible binary strings of a given length. Next aim of our project is to propose a receiver design that uses a hybrid modulation scheme FSK and ASK so as to reduce a ratio of cost/complexity. In this method considering an additive white Gaussian noise (AWGN) channel and non-coherent detection based receiver, CNS shows saving in transmitter energy by 30%. There is a 50% saving at the receiver for all type of data modulation due to halving of the transmitted data duration, compared to binary encoding. The energy savings for transmission of text data is 42.6% and for JPEG image files is found to be 33% for CNS. We also compare the different parameters with CNS, results clearly establish that CNS can be a suitable candidate for communication in low power wireless sensor networks Keywords-Energy-efficient communication, Wireless sensor networks, Non-coherent detection, Communication through Silent symbol I. INTRODUCTION Inwireless sensor networks (WSNs) we face the problem in regular replacement or recharging of the batteries of the sensor devices as such networks are continuously in use for monitoring or surveillance purposes. Communication being a major consumer of energy, design of energy efficient communication schemes for WSNs is important. In order to reduce energy consumption we look into MAC layer designing for a survey of routing, power control and energy optimization. Communication through silence (cts) method uses the silent time period and another method for saving energy is variable-Base Tacit communication which uses variable radix-based coding. But for noisy channel both methods are not suitable as they are exponential in communication time. By using appropriate source coding method and digital modulation technique we can enhance the energy saving measures. We can convert the given binary data into 'm' possible symbols (m>2) which contains one symbol having higher probability of occurrence. By keeping transmitter silent for that dominant symbol one can save energy for transmission. In CNS different modulation scheme like OOK, ASK, FSK can be used but FSK is selected due to low bit error rate (BER) in a noisy channel. ASK demodulation is used to detect the null symbol at the receiver, with this instantaneous power levels of the (m-1) non-silent symbols have to be raised, due to the relatively inferior (higher) signal to noise ratio (SNR) requirements of ASK for the same BER value. II. BASIC IDEA

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Section: Simulation Results With Some Real-life Datamentioning

confidence: 99%

Section: Simulation Results With Some Real-life Datamentioning

confidence: 99%

CNS: a new energy efficient transmission scheme for wireless sensor networks

Sinha

Datta

2010

Wireless Netw

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“…Chan et al 2009;Demiris 2009;Jea et al 2008;Mitseva et al 2008;Mittelstadt et al 2011;Tentori et al 2006;Tiwari et al 2010;Garde-Perik et al 2006;van Hoof et al 2007). At its narrowest, informational privacy can be equated with hiding personally identifiable data from unauthorised parties (Garcia-Morchon et al 2011;Ahamed et al 2007), and can be quantifiable (Srinivasan et al 2008).…”

Section: Informational Privacymentioning

confidence: 99%

Ethics of the health-related internet of things: a narrative review

Mittelstadt

2017

Ethics Inf Technol

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“…A service scenario supporting the telemanagement of children with asthma has been embedded in a home monitoring platform as described by Traganitis et al 10 Promising results of the stress monitoring using distributed wireless intelligent sensor systems have been presented by Jovanov et al 3 A simple fitness monitoring is presented by Jea et al 11 However, in the processing phase of reducing the variance within physiological information data (weight, blood pressure, and heartbeat), simple algorithms that do not take into account other facts are applied. In order to handle problems related to noise and missing physiological data, Kumar et al 12 have proposed a fuzzy filtering algorithm for the preprocessing of the physiological signal.…”

Section: Introductionmentioning

confidence: 99%

Stress and Fitness Monitoring Embedded on a Modern Telematics Platform

Takenga¹,

Berndt²,

Kuehn³

et al. 2012

Telemedicine and e-Health

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Lack of regular physical activity and high stress levels are the leading causes of several illnesses. There is thus a real need for a personal low-cost and mobile monitoring solution over extended periods to prevent health risks. Based on the above fact, this article presents a system capable of estimating and monitoring both stress and fitness levels without a physical consultation of a medical specialist. The system consists of three main subcomponents: a mobile real-time acquisition of physiological as well as subjective data, an expert model for stress and fitness estimations based on physiological signals collected from wireless vital sensors, and a secure and scalable telematics platform on which the entire system is embedded. Features and tasks performed by the telematics platform will be presented. The experimental part of the work involved a representative number of subjects. Results for 110 subjects whose fitness levels were assessed at different periods of the year and 50 individuals whose stress scores were assessed at different times of the day showed a high correlation of the estimated values with the true ones. The application of such a low-cost monitoring system will improve the quality of service in preventive medicine.

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Hassle free fitness monitoring

Cited by 16 publications

References 3 publications

CNS: a new energy efficient transmission scheme for wireless sensor networks

CNS: a new energy efficient transmission scheme for wireless sensor networks

Ethics of the health-related internet of things: a narrative review

Stress and Fitness Monitoring Embedded on a Modern Telematics Platform

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