Energy Efficient Telemonitoring of Wheezes

IEEE Transactions on Biomedical Engineering, 2000

Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote.

2011 IEEE International Symposium of Circuits and Systems (ISCAS), 2011

Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric tele-cardiology systems. A WBSNenabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly report cardiac signals to a WBSN coordinator, which is responsible for reporting them to the tele-health provider. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization and energy efficiency. Among others, energy efficiency can be significantly improved through embedded ECG compression, which reduces airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low complexity energy-aware ECG compression on the state-of-the-art Shimmer TM WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in terms of overall energy efficiency and Shimmer TM node lifetime extension.

IEEE Journal of Biomedical and Health Informatics, 2014

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.

IEEE Transactions on Mobile Computing, 2019

Activity recognition, as an important component of behavioral monitoring and intervention, has attracted enormous attention, especially in Mobile Cloud Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently resource constrained wearable devices have been gaining popularity, their battery life is limited and constrained by the frequent wireless transmission of data to more computationally powerful back-ends. This paper proposes an ultra-low power activity recognition system using a novel adaptive compressed sensing technique that aims to minimize transmission costs. Coarse-grained on-body sensor localization and unsupervised clustering modules are devised to autonomously reconfigure the compressed sensing module for further power saving. We perform a thorough heuristic optimization using Grammatical Evolution (GE) to ensure minimal computation overhead of the proposed methodology. Our evaluation on a real-world dataset and a low power wearable sensing node demonstrates that our approach can reduce the energy consumption of the wireless data transmission up to 81.2% and 61.5%, with up to 60.6% and 35.0% overall power savings in comparison with baseline and a naive state-of-the-art approaches, respectively. These solutions lead to an average activity recognition accuracy of 89.0%-only 4.8% less than the baseline accuracy-while having a negligible energy overhead of on-node computation.

Circuits, Systems, and Signal Processing, 2016

Among the existing applications of wireless body sensor networks (WBSNs), a wearable health monitoring system (WHMS) is the most important. In a typical WHMS, miniature wireless biosensors, attached to or implanted in the human body, collect bio-signals such as the electrocardiogram (ECG), blood pressure or electromyogram (EMG) to provide real time and continuous health monitoring. In this paper, we present a compressed sensing (CS)-based approach to compress and recover the sensed bio-signals from the wireless biosensors of a WBSN. The CS encoding process has a low computational complexity and is suitable for use in power-constrained systems such as WHMS. We propose a simple deterministic measurement matrix, which is easy to implement in hardware. We design a digital CS encoder implementing the proposed measurement matrix and use it to compress the bio-signals in EMG and ECG wireless biosensors. The simulations and experimental B Andrianiaina Ravelomanantsoa

Electronics, 2024

This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring, the system has the potential to give clinicians access to assessments from which they could make decisions about modifying therapy in real-time and communicate changes directly to patients. The system comprises a wearable wireless microphone array interfaced with a programmable microcontroller with embedded signal conditioning. Experiments on the phantom model were conducted to demonstrate the feasibility of reconstructing acoustic lung images for detecting obstructions in the airway and provided controlled validation of noise resilience and imaging capabilities. An optimized denoising technique and design innovations provided 7 dB more SNR and 7% more imaging accuracy for the proposed system, benchmarked against digital stethoscopes. While further clinical studies are warranted, initial results suggest potential benefits over single-point digital stethoscopes for internet-enabled remote lung monitoring needing noise immunity and regional specificity. The flexible architecture aims to bridge critical technical gaps in frequent and connected respiratory function at home or in busy clinical settings challenged by ambient noise interference.

Wireless Communications and Mobile Computing

Wireless body area networks (WBAN) are becoming a promising solution for health care applications. WBAN allows monitoring of patients continuously in their own comfort zone. These devices use the industrial, scientific, and medical band (ISM) for communication. This band is overcrowded due to the increasing number of wireless medical devices and other wireless devices occupying this band. This causes interference, which can be damaging and could result in a change in received power. However, WBAN also needs minimum and reliable energy communication for a longer lifetime and improved quality of service. This work addresses both problems and proposes solutions for the same. A cognitive radio controller is employed as a centralized controller with dynamic spectrum allocation properties to mitigate the interference. The sensing of the spectrum is based on compressed sensing with a nonreconstruction model to save energy. To quantify interference measurement, the interference mitigation f...

ACTA IMEKO

The paper presents an Internet of Things (IoT) prototype which consists of a data acquisition device wirelessly connected to Internet via Wi-Fi, for continuous electrocardiogram (ECG) monitoring. The proposed system performs a novel Compressed Sensing (CS) based method on ECG signal with the aim of reducing the amount of transmitted data, thus realizing an efficient way to increase the battery life of such devices. For the assessment of the energy consumption of the device, an experimental setup was arranged and its description is presented. The evaluation of the reconstruction quality of the ECG signal in terms of Percentage of Root-mean-squared Difference (PRD) is reported for several Compression Ratios (CRs). The obtained experimental results clearly demonstrate the robustness and usefulness of the Wi-Fi based IoT devices adopting the considered CS-method for data compression of ECG signals. Furthermore, it allows reducing the energy consumption of the IoT device, by increasing t...

Compressed sensing (CS) is a novel idea wherein a signal can be sampled at sub-Nyquist rates and still be effectively reconstructed. Many natural signals such as the ECG signal are sparse and have sparse representation when expressed in a suitable basis. Compressed sensing exploits the sparsity by acquiring a small number of projections on to random vectors which are sufficient to recover the signal. This theory enables an effective implementation of patient-centric telecardiology or mobile cardiology systems. It guarantees improvement in performance by drastically reducing the number of samples that requires to be either stored or transmitted. Specifically in the context of ECG monitoring systems, CS signal acquisition revolutionizes miniaturization of the hardware and improves its energy efficiency. This work proposes to create a Compressed sensing based mathematical framework for the acquisition and recovery of an ECG signal which has been further classified into normal and abnormal categories. During the compression stage, a sensing matrix which results in a low PRD (Percentage root mean square distortion) is used and an efficient reconstruction algorithm is employed to retrieve the most valuable information from the ECG signal. The classification of the ECG signal is done by studying the region of interest i.e. the QRS complex. The heart rate , the interval between two R peaks and the amplitude of the R peaks contributes chiefly to determining an abnormality in the ECG signal. This model can be implemented with a wireless body sensor network (WBSN) which may be used to alert a doctor in emergency situations.

arXiv: Signal Processing, 2018

This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight sleep (a total of 160 hours), during which contact sensors record ground-truth breathing data, patient position is recorded, and four different RF breathing monitoring systems simultaneously record measurements. We evaluate published methods and algorithms. We find that WiFi channel state information measurements provide the most robust respiratory rate estimates of the four RF systems tested. However, all four RF systems have periods during which RF-based breathing estimates are not reliable.