4,036 research outputs found
A Review of Wireless Body Area Networks for Medical Applications
Recent advances in Micro-Electro-Mechanical Systems (MEMS) technology,
integrated circuits, and wireless communication have allowed the realization of
Wireless Body Area Networks (WBANs). WBANs promise unobtrusive ambulatory
health monitoring for a long period of time and provide real-time updates of
the patient's status to the physician. They are widely used for ubiquitous
healthcare, entertainment, and military applications. This paper reviews the
key aspects of WBANs for numerous applications. We present a WBAN
infrastructure that provides solutions to on-demand, emergency, and normal
traffic. We further discuss in-body antenna design and low-power MAC protocol
for WBAN. In addition, we briefly outline some of the WBAN applications with
examples. Our discussion realizes a need for new power-efficient solutions
towards in-body and on-body sensor networks.Comment: 7 pages, 7 figures, and 3 tables. In V3, the manuscript is converted
to LaTe
Wireless body sensor networks for health-monitoring applications
This is an author-created, un-copyedited version of an article accepted for publication in
Physiological Measurement. The publisher is
not responsible for any errors or omissions in this version of the manuscript or any version
derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01
Implementing and Evaluating a Wireless Body Sensor System for Automated Physiological Data Acquisition at Home
Advances in embedded devices and wireless sensor networks have resulted in
new and inexpensive health care solutions. This paper describes the
implementation and the evaluation of a wireless body sensor system that
monitors human physiological data at home. Specifically, a waist-mounted
triaxial accelerometer unit is used to record human movements. Sampled data are
transmitted using an IEEE 802.15.4 wireless transceiver to a data logger unit.
The wearable sensor unit is light, small, and consumes low energy, which allows
for inexpensive and unobtrusive monitoring during normal daily activities at
home. The acceleration measurement tests show that it is possible to classify
different human motion through the acceleration reading. The 802.15.4 wireless
signal quality is also tested in typical home scenarios. Measurement results
show that even with interference from nearby IEEE 802.11 signals and microwave
ovens, the data delivery performance is satisfactory and can be improved by
selecting an appropriate channel. Moreover, we found that the wireless signal
can be attenuated by housing materials, home appliances, and even plants.
Therefore, the deployment of wireless body sensor systems at home needs to take
all these factors into consideration.Comment: 15 page
Wireless Medical Sensor Networks: Design Requirements and Enabling Technologies
This article analyzes wireless communication protocols that could be used in healthcare environments (e.g., hospitals and small clinics) to transfer real-time medical information obtained from noninvasive sensors. For this purpose the features of the three currently most widely used protocols—namely, Bluetooth® (IEEE 802.15.1), ZigBee (IEEE 802.15.4), and Wi-Fi (IEEE 802.11)—are evaluated and compared. The important features under consideration include data bandwidth, frequency band, maximum transmission distance, encryption and authentication methods, power consumption, and current applications. In addition, an overview of network requirements with respect to medical sensor features, patient safety and patient data privacy, quality of service, and interoperability between other sensors is briefly presented. Sensor power consumption is also discussed because it is considered one of the main obstacles for wider adoption of wireless networks in medical applications. The outcome of this assessment will be a useful tool in the hands of biomedical engineering researchers. It will provide parameters to select the most effective combination of protocols to implement a specific wireless network of noninvasive medical sensors to monitor patients remotely in the hospital or at home
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic of many physiological signals. Block sparse
Bayesian learning (BSBL) is an effective approach to recover such signals with
satisfactory recovery quality. However, it is time-consuming in recovering
multichannel signals, since its computational load almost linearly increases
with the number of channels.
This work proposes a spatiotemporal sparse Bayesian learning algorithm to
recover multichannel signals simultaneously. It not only exploits temporal
correlation within each channel signal, but also exploits inter-channel
correlation among different channel signals. Furthermore, its computational
load is not significantly affected by the number of channels. The proposed
algorithm was applied to brain computer interface (BCI) and EEG-based driver's
drowsiness estimation. Results showed that the algorithm had both better
recovery performance and much higher speed than BSBL. Particularly, the
proposed algorithm ensured that the BCI classification and the drowsiness
estimation had little degradation even when data were compressed by 80%, making
it very suitable for continuous wireless telemonitoring of multichannel
signals.Comment: Codes are available at:
https://sites.google.com/site/researchbyzhang/stsb
Assessment of the feasibility of an ultra-low power, wireless digital patch for the continuous ambulatory monitoring of vital signs.
BACKGROUND AND OBJECTIVES: Vital signs are usually recorded at 4–8 h intervals in hospital patients, and deterioration between measurements can have serious consequences. The primary study objective was to assess agreement between a new ultra-low power, wireless and wearable surveillance system for continuous ambulatory monitoring of vital signs and a widely used clinical vital signs monitor. The secondary objective was to examine the system's ability to automatically identify and reject invalid physiological data. SETTING: Single hospital centre. PARTICIPANTS: Heart and respiratory rate were recorded over 2 h in 20 patients undergoing elective surgery and a second group of 41 patients with comorbid conditions, in the general ward. OUTCOME MEASURES: Primary outcome measures were limits of agreement and bias. The secondary outcome measure was proportion of data rejected. RESULTS: The digital patch provided reliable heart rate values in the majority of patients (about 80%) with normal sinus rhythm, and in the presence of abnormal ECG recordings (excluding aperiodic arrhythmias such as atrial fibrillation). The mean difference between systems was less than ±1 bpm in all patient groups studied. Although respiratory data were more frequently rejected as invalid because of the high sensitivity of impedance pneumography to motion artefacts, valid rates were reported for 50% of recordings with a mean difference of less than ±1 brpm compared with the bedside monitor. Correlation between systems was statistically significant (p<0.0001) for heart and respiratory rate, apart from respiratory rate in patients with atrial fibrillation (p=0.02). CONCLUSIONS: Overall agreement between digital patch and clinical monitor was satisfactory, as was the efficacy of the system for automatic rejection of invalid data. Wireless monitoring technologies, such as the one tested, may offer clinical value when implemented as part of wider hospital systems that integrate and support existing clinical protocols and workflows
- …