Activity-aware Mental Stress Detection Using Physiological
The paper present an
activity-aware mental stress detection scheme.
Electrocardiogram (ECG), galvanic skin response (GSR), and accelerometer data
were gathered from 20 participants (using Shimmer) across three activities:
sitting, standing, and walking. For each activity, we gathered baseline
physiological measurements and measurements while users were subjected to
mental stressors. The activity information derived from the accelerometer
enabled us to achieve 92.4% accuracy of mental stress classication for 10-fold
cross validation and 80.9% accuracy for between subjects classification.
ECG-based patient authentication for healremote th monitoring. The paper presents
novel multimodal biometric authentication system based on wearable human
electrocardiogram (ECG) and accelerometer sensors (Shimmer). The article
demonstrated, on data collected from 17 subjects, that activity-aware
authentication systems can effectively deal with the ECG variability induced by
physical activities performed in the real world. Based on this the author
believes the approached outline in the paper could facilitate ongoing
authentication without requiring frequent and active participation from the
Real-Time Atrial Fibrillation Detection on a Wearable Wireless Sensor Platform
presents an automated real-time atrial fibrillation (AF) detection approach
that relies on the observation of two characteristic irregularities of AF
episodes in the electrocardiogram (ECG) signal. The results generated after the
analysis of these irregularities are subsequently analyzed in real-time using a
new fuzzy classifier. We have optimized this novel AF classification framework
to require very limited processing, memory storage and energy resources, which
makes it able to operate in real-time on a wearable wireless sensor platform.
Moreover, our experimental results indicate that the proposed on-line approach
shows a similar accuracy to stateof- the-art off-line AF detectors, achieving
up to 96% sensitivity and 93% specificity. Finally, we present a detailed
energy study of each component of the target wearable wireless sensor platform,
while executing the automated AF detection approach in a real operating
scenario, in order to evaluate the lifetime of the overall system. This study
indicates that the lifetime of the platform is increased by using the proposed
method to detect AF in real-time and diagnose the patient with respect to a
streaming application that sends the raw signal to a central coordinator (e.g.,
smartphone or laptop) for its ulterior processing.
Disruption-Tolerant Wireless Biomedical Monitoring for
Marathon Runners: a Feasibility Study
Off-the-shelf wireless sensing devices open a wide range of perspectives for tetherless biomedical monitoring. Yet most applications considered to date imply either indoor realtime data streaming or ambulatory data recording. Disruptiontolerant networking is a means to cope with challenging situations where continuous end-to-end connectivity between communicating devices cannot be guaranteed. In this paper we investigate the possibility of using this approach to remotely monitor the cardiac activity of runners during a marathon race, using off-the shelf sensing devices and a limited number of base stations deployed along the marathon route. Preliminary experiments show that such a scenario is indeed viable, although special attention must be paid to balancing the requirements of ECG monitoring with the constraints of episodic, low-rate transmissions.
Diagnosis for a Wireless Body Sensor Platform
In this demo, we
present the development and implementation of a state-of-the-art digital wavelet transform (DWT)-based ECG delineation algorithm into a
commercial Wireless Body Sensor Platform (Shimmer™). The use of this kind of
architecture enables continuous biomedical monitoring and care of patients
while enhancing their autonomy.
Assessment of Custom Fitted Heart Rate Sensing Garments
whilst undertaking Everyday Activities
This study assesses the
accuracy of heart rate detection using custom fit
intelligent garments with integrated textile electrodes. Two single lead
electrocardiograms (ECGs) where recorded from 5 subjects using wet Ag/AgCl (WE) and textile electrodes (TE). During recording the subjects were asked to
perform several tasks. Offline, the ECGs recorded from the WE were examined and the number of R wave peaks for each subject where counted by a human observer. This count served as the gold standard. A computer program was used to detect R peaks from both the WE and TE. Sensitivity of each system was determined by comparing computer program based heart rates from WE and TE to the gold standard. The system with TE obtained a mean sensitivity of 76.47%. This was significantly lower than with WE (mean: 98.19%). Results indicated that the custom skin layers did not perform accurately whilst performing tasks involving movement of the trunk and limbs.
Intra-Body Temperature Monitoring using a Biofeedback
This paper presents a solution for intra-body temperature monitoring based on a new intra-body sensor, communication and desktop application tool. This new
biosensor provides data collection that may be used to study the relation
between temperature variations and women health conditions, such as, ovulation
period (for both naturalcontraception and in vitro fertilization purposes)
among others. The motivation for this work focuses on the creation of this
e-Health solution that will fill the gap we realize in medical technology. The
proposal was tested and validated by a medical team and it was concluded that
this new biosensor performs perfectly.
A WBAN for Human
Movement Kinematics and ECG Measurements
Biomedical applications of body area networks (BANs) are evolving, where taking periodic medical readings of patients via means wireless technologies at home or in the office will aid physicians to periodically supervise the patient’s medical status without having to see the patient. Thus, one important objective of BANs is to provide the doctor with the medical readings that can be collected electronically without being in close proximity to the patient. This is done through the measurement of the patient’s physiological signals via means of wearable sensors. This paper investigates wireless BAN cooperation via actual measurements of human movement kinematics and electrocardiogram (ECG), which are believed to provide patients with easy healthcare for continuous health-monitoring. The collected information will be processed using specially designed software, which in turn will enable the patient to send a full medical chart to the physician’s electronic device.
Energy-efficient Long Term Physiological Monitoring
wireless body sensor-based systems have been proposed for continuous, long-term physiological monitoring. A major challenge in such systems is that a large amount of data is collected, and
transmission of this data incurs signiﬁcant energy consumption at the sensor. In this work, we demonstrate a data reporting method that signiﬁcantly reduces energy consumption while maintaining a
high diagnostic accuracy of the reported physiological signal.
This is achieved by using a generative model of the physiological signal of
interest at the sensor, and suppressing data transmission when sensed data
matches the model. In this demonstration, we implement the proposed technique
for electrocardiogram (ECG) signal and illustrate its performance in terms of
energy savings and accuracy of reported data.
framework for movement activity monitoring of sprinters
The most important
factor for success in short distance track and field running is an optimal combination of the step frequency and step length. Evaluation and
analysis of step parameters allows enhancing the overall performance and
avoiding injuries. This paper presents a low-cost, lightweight wireless
solution. The developed system derives information about step parameters from
accelerometer and gyroscope measurement results and fulfills a real-time
analysis of sprinter's activity. The described instrument supports coaches and
help to optimize the training process.
Multi-lead Wavelet-based ECG Delineation on a Wearable
Embedded Sensor Platform
This paper is dedicated
to the sensible optimization and porting of a multi-lead
(ML) wavelet-transform (WT)- based electrocardiogram (ECG) wave delineator to a state-of-the-art commercial wearable embedded sensor platform with limited
processing and storage resources. The original offline algorithm was recently
proposed and validated in the literature, as an extension of an earlier well-established single-lead (SL) WT-based ECG delineator