Wireless personal area networks in telemedical environment

Jovanov, Emil; Price, J.; Raskovic, Dejan; Kavi, Krishna M.; Martin, Thomas; Adhami, Reza R.

doi:10.1109/itab.2000.892342

Cited by 55 publications

(39 citation statements)

References 20 publications

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“…A number of wearable medical sensors could communicate using PAN. PAN can be used in telemedical environment and also in intelligent control of medication delivery using wireless sensing [6]. PAN can serve as aids for disabled individuals.…”

Section: Applicationsmentioning

confidence: 99%

Understanding Personal Area Networks

2017

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Abstract-A personal area network (PAN) connects IT devices or gadgets (such as cell phones, PCs, laptop computers, PDAs, printers, pagers, or other wearable computer devices) within the environment of an individual user. Because PAN enables low-power, low-rate communications between the devices near the user, it has received a lot of attention in the recent years. This paper provides a brief introduction to PAN.Keywords-home area networks, smart homes, smart grid I. INTRODUCTIONAs electronic devices become smaller, low power, and low cost, we have begun to adorn our bodies with wearable computer devices such cell phones, personal digital assistants (PDAs), palmtops, pocket video games, wristwatch, pagers, digital camera, and GPS receiver. As our homes continue to contain an increasing number of them, there is a growing demand for networking them together in a manageable fashion [1]. The concept of personal area networks (PANs) has arisen as a means of enabling these devices to share data.A personal area network (PAN) is a computer network that handles interconnection of digital devices at the surrounding of a single user. It involves battery-operated, wearable and portable digital devices that draw very little current and are located within 1 m to 100 m of each other. It allows these devices to communicate with each other. PAN is for personal use only. The reach of a PAN is typically few meters. Every device in a PAN can plug into any other device provided they are in close proximity.A PAN may be either wired or wireless. It is wired when it is carried over wired computer buses such as USB and FireWire. Wireless PAN or WPAN is based on the standard IEEE 802.15 and employs wireless technologies such as infrared, ZigBee, Bluetooth, and ultrawideband. WPAN allows devices such as keyboards, audio head sets, and printers to connect to personal digital assistants, cell phones, or computers wirelessly [2]. It allows connection to the Internet.The concept of personal area network originated at M.I.T.'s Media Lab. PAN is different from a LAN in a sense that it requires little or no infrastructure. The range coverage of PANs is smaller than that of LANs. An example of a WPAN is shown in Figure 1 [3].

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Section: Applicationsmentioning

confidence: 99%

Understanding Personal Area Networks

2017

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“…The WBAN consists of inexpensive, lightweight, and miniature physiological sensors, and allows prolonged, unobtrusive health monitoring, computer-supervised rehabilitation, and even early detection of medical conditions [2][3] [4]. For example, intelligent heart monitors can warn the user about impeding medical conditions [6] or provide information for a specialized service in the case of catastrophic events [7].…”

Section: Introductionmentioning

confidence: 99%

“…Our target application is monitoring of physical activity using WBAN of intelligent motion and ECG sensors [2]. Motion sensors can be deployed on characteristic body points, such as ankle, chest, wrist, and hip.…”

Section: Introductionmentioning

confidence: 99%

An implementation of hierarchical signal processing on wireless sensor in TinyOS environment

Otto

Gober

McMurtrey

et al. 2005

Proceedings of the 43rd Annual Southeast Regional Conference - Volume 2

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Wireless intelligent sensor networks have become a major infrastructure for deeply embedded systems. As these systems typically operate under stringent energy constraints, a careful consideration of computation and communication is crucial for extended system lifetime. To achieve extended lifetime on a limited energy budget the existing sensor platforms feature limited communication bandwidth, processing power, and interfacing capabilities. We propose to augment a general-purpose sensor platform by low-power, intelligent signal processing modules. This approach allows on-sensor parallel signal processing. In this paper we present an implementation of a sensor node featuring 2-tier hierarchical organization. The upper level is realized using the Telos wireless platform, and the lower level is a custom Intelligent Signal Processing Module (ISPM). The ISPM is developed as an application-specific sensor board for health monitoring applications. It features two accelerometers, a bioamplifier, and a microcontroller used for preprocessing and communication with the Telos platform. Telos features ZigBee wireless interface and can communicate to an application server through a network coordinator. The application server represents a third tier in the hierarchical system. The server seamlessly integrates sensors into a wireless body area network of physiological sensors. We describe the system architecture and software support implemented in TinyOS environment.

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“…Many biomedical sensors suffer from similar long-term power issues, and it is agreed that the only way of reducing the device power consumption is the introduction of local signal processing, or intelligence, that runs on the portable device itself [7], [8]. The idea is that in any system setup, some automated processing will be carried out on the collected physiological signal, and that storage or transmission of the signal is power intensive.…”

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confidence: 99%

Toward Online Data Reduction for Portable Electroencephalography Systems in Epilepsy

Casson

Rodriguez‐Villegas

2009

IEEE Trans. Biomed. Eng.

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Abstract-Portable EEG units are key tools in epilepsy diagnosis. Current systems could be made physically smaller and longer lasting by the inclusion of online data reduction methods to reduce the power required for storage or transmission of the EEG data. This paper presents a real-time data reduction algorithm based upon the discontinuous recording of the EEG: noninteresting background sections of EEG are discarded online, with only potentially diagnostically interesting sections being saved. MAT-LAB simulations of the algorithm on an EEG dataset containing 982 expert marked events in 4 days of data show that 90% of events can be correctly recorded while achieving a 50% data reduction. The described algorithm is formulated to have a direct, low power, hardware implementation and similar data reduction strategies could be employed in a range of body-area-network-type applications.

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Wireless personal area networks in telemedical environment

Cited by 55 publications

References 20 publications

Understanding Personal Area Networks

Understanding Personal Area Networks

An implementation of hierarchical signal processing on wireless sensor in TinyOS environment

Toward Online Data Reduction for Portable Electroencephalography Systems in Epilepsy

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