Characterization of the 2.4 GHz ISM band electromagnetic interference in a hospital environment

Krishnamoorthy, S.; Reed, Jeffrey H.; Anderson, C.R.; robert, provinah; Srikanteswara, Srikathyayani

doi:10.1109/iembs.2003.1280835

Cited by 24 publications

(15 citation statements)

References 10 publications

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“…Given these reasons, the research on EMI in the scenario of wireless healthcare monitoring starts. Krishnamoorthy and Reed in [14] measure the EMI on medical equipment from patient and doctor devices, which work around the 2.4-GHz frequency bands; the measurement is undertaken in two hospitals. The results show that the maximal EMI record is 0.552 V/m, which is within the acceptable EMI range recommended by the EN60601-1-2 standard.…”

Section: Related Work Of Emi On Medical Sensorsmentioning

confidence: 99%

“…The results show that the maximal EMI record is 0.552 V/m, which is within the acceptable EMI range recommended by the EN60601-1-2 standard. However, the measurement in [14] has not considered the quality of service (QoS) of data transmitted by patient devices and healthcare staff devices. The policy on mobile phone utilization, such as turning off mobile phones, cannot be applicable for patient devices and healthcare staff devices in a wireless healthcare monitoring system [15].…”

Section: Related Work Of Emi On Medical Sensorsmentioning

confidence: 99%

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Internet of Vehicles for E-Health Applications: A Potential Game for Optimal Network Capacity

Lin

Tang

Labeau

et al. 2017

IEEE Systems Journal

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Wireless technologies are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical sensors. In view of EMI on medical sensors, we propose a power control algorithm under a noncooperative game theoretic framework to schedule data transmission. Our objective is to ensure that the noncooperative game of power control can achieve a network-level objective-the optimal network capacity, although the wireless users are selfish and only interested in optimizing their own channel capacity. To obtain this objective, we show that our proposed noncooperative game is a potential game and propose the best-response-dynamics algorithm which can ensure that the game strategy of each user is induced to the optimal solution to the problem of network-level optimal capacity. Numerical results illustrate that the proposed algorithm can achieve an enhancement of 8% of network performance than the existing algorithm against the variations of mobile hospital environments.Index Terms-E-health, game theory, mobile hospital, Nash equilibrium, power control. 1932-8184

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Section: Related Work Of Emi On Medical Sensorsmentioning

confidence: 99%

Section: Related Work Of Emi On Medical Sensorsmentioning

confidence: 99%

Internet of Vehicles for E-Health Applications: A Potential Game for Optimal Network Capacity

Lin

Tang

Labeau

et al. 2017

IEEE Systems Journal

View full text Add to dashboard Cite

show abstract

“…Given these reasons, the research on EMI in the scenario of wireless healthcare monitoring starts. Krishnamoorthy et al in [16] measure the EMI on medical equipment from patient and doctor devices, which work around the 2.4 GHZ frequency bands; the measurement is undertaken in two hospitals. The results show that the maximal EMI record is 0.552 V/m, which is within the acceptable EMI range recommended by the EN60601-1-2 standard.…”

Section: Related Work Of Emi On Medical Sensorsmentioning

confidence: 99%

Internet of Vehicles for E-Health Applications in View of EMI on Medical Sensors

Lin

Labeau

et al. 2015

Journal of Sensors

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Wireless technologies are pervasive to support ubiquitous healthcare applications. However, RF transmission in wireless technologies can lead to electromagnetic interference (EMI) on medical sensors under a healthcare scenario, and a high level of EMI may lead to a critical malfunction of medical sensors. In view of EMI to medical sensors, we propose a joint power and rate control algorithm under game theoretic framework to schedule data transmission at each of wireless sensors. The objective of such a game is to maximize the utility of each wireless user subject to the EMI constraints for medical sensors. We show that the proposed game has a unique Nash equilibrium and our joint power and rate control algorithm would converge to the Nash equilibrium. Numerical results illustrate that the proposed algorithm can achieve robust performance against the variations of mobile hospital environments.

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“…In 1997, the first 24-hour measurements showing temporal dependencies of the measured field within a hospital for frequencies up to 1 GHz were described [16]. In 2003 the electromagnetic field within the ISM band ([2.40 − 2.4835] GHz) was evaluated [17]. In [18] both short-term and long-term field strength measurements were carried out in the frequency range from 9 kHz up to 10 GHz.…”

Section: Introductionmentioning

confidence: 99%

Threshold setting for the evaluation of the aggregate interference in ISM band in hospital environments

Mucchi

Carpini

D'Anna

et al. 2015

2015 9th International Symposium on Medical Information and Communication Technology (ISMICT)

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Estimation of the aggregate interference is required in order to predict the performance of a wireless system in its working environment. Body Area Networks (BANs) for healthcare applications are becoming a reality, allowing patients to be monitored continuously without forcing them to stay in bed or in hospital. The increasing number of wireless medical devices makes the ISM (Industrial, Scientific and Medical) band particularly crowded. If new smart BANs have to correctly operate in hospital, coexistence with the existing wireless devices must be accurately investigated, starting with studying the interference in the operating frequency band. The intensity of the interference is strongly related to the chosen threshold, which defines if a received sample has to be categorized as interference or noise. Adaptive threshold methods outperform the non-adaptive ones, due to flexibility and robustness. Among the adaptive threshold methods, the forward consecutive mean excision (FCME) is one of the most attractive, since it is blind, computationally simple and efficient. When applied to large data set, it may require too long time to be computed, thus median filtering has been proposed (med-FCME). In this paper we compare the performance of fixed vs adaptive threshold methods. The two methods are applied to a set of real measurements taken in a modern city hospital over one week.

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Characterization of the 2.4 GHz ISM band electromagnetic interference in a hospital environment

Cited by 24 publications

References 10 publications

Internet of Vehicles for E-Health Applications: A Potential Game for Optimal Network Capacity

Internet of Vehicles for E-Health Applications: A Potential Game for Optimal Network Capacity

Internet of Vehicles for E-Health Applications in View of EMI on Medical Sensors

Threshold setting for the evaluation of the aggregate interference in ISM band in hospital environments

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