Current medical practice of long-term chronic respiratory diseases treatment lacks a convenient method of empowering the patients and caregivers to continuously quantitatively track the intensity of respiratory symptoms. Such is "asthmatic wheezing", occurring in respiratory sounds. We envision a mobile, personalized asthma monitoring system comprising of a wearable, energy-constrained acoustic sensor and smartphone. In this article we address the energy-burden of acquisition and streaming of acoustic respiratory signal, and lessen it by applying the concept of compressed sensing (CS). First we analyse the adherence of normal and pathologic respiratory sounds frequency representations (DFT, DCT) to the sparse signal model. Given the pseudo-random non-uniform subsampling encoder implemented on MSP430 microcontroller, we review tradeoffs of accuracy and execution time of different CS algorithms, suitable for realtime respiratory spectrum recovery on smartphone. Working CS respiratory spectrum acquisition prototype is demonstrated, and evaluated. Prototype enables for real-time reconstruction of spectra dominated by approximately 8 frequency components with more than 80% accuracy, on Android smartphone using OMP algorithm, from only 25% signal samples (w.r.t. Nyquist rate) acquired and streamed by sensor at 8 kbit/s. Index Terms-M-health, asthmatic wheezing, compressive sensing, non-uniform sampling, orthogonal matching pursuit.1530-437X (c)

Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a sensor is required in order to achieve long autonomy. Considering that the power consumption of its radio is kept minimal if transmitting only upon (rare) occurrences of wheezing, we focus on optimizing the power consumption of the digital signal processor (DSP). Based on a comprehensive review of asthmatic wheeze detection algorithms, we analyze the computational complexity of common features drawn from short-time Fourier transform (STFT) and decision tree classification. Four algorithms were implemented on a low-power TMS320C5505 DSP. Their classification accuracies were evaluated on a dataset of prerecorded respiratory sounds in two operating scenarios of different detection fidelities. The execution times of all algorithms were measured. The best classification accuracy of over 92%, while occupying only 2.6% of the DSP's processing time, is obtained for the algorithm featuring the time-frequency tracking of shapes of crests originating from wheezing, with spectral features modeled using energy.

Quantification of wheezing by a sensor system consisting of a wearable wireless acoustic sensor and smartphone performing respiratory sound classification may contribute to the diagnosis, long-term control, and lowering treatment costs of asthma. In such battery-powered sensor system, compressive sensing (CS) was verified as a method for simultaneously cutting down power cost of signal acquisition, compression, and communication on the wearable sensor. Matching real-time CS reconstruction algorithms, such as orthogonal matching pursuit (OMP), have been demonstrated on the smartphone. However, their lossy performance limits the accuracy of wheeze detection from CS-recovered short-term Fourier spectra (STFT), when using existing respiratory sound classification algorithms. Thus, here we present a novel, robust algorithm tailored specifically for wheeze detection from the CS-recovered STFT. The proposed algorithm identifies occurrence and tracks multiple individual wheeze frequency lines using hidden Markov model. The algorithm yields 89.34% of sensitivity, 96.28% of specificity, and 94.91% of accuracy on Nyquist-rate sampled respiratory sounds STFT. It enables for less than 2% loss of classification accuracy when operating over STFT reconstructed by OMP, at the signal compression ratio of up to 4 $\times$ (classification from only 25% signal samples). It features execution speed comparable to referent algorithms, and offers good prospects for parallelism.

Long-term quantification of asthmatic wheezing envisions an m-Health sensor system consisting of a smartphone and a body-worn wireless acoustic sensor. As both devices are power constrained, the main criterion guiding the system design comes down to minimization of power consumption, while retaining sufficient respiratory sound classification accuracy (i.e., wheeze detection). Crucial for assessment of the system-level power consumption is the understanding of trade-off between power cost of computationally intensive local processing and communication. Therefore, we analyze power requirements of signal acquisition, processing, and communication in three typical operating scenarios: (1) streaming of uncompressed respiratory signal to a smartphone for classification, (2) signal streaming utilizing compressive sensing (CS) for reduction of data rate, and (3) respiratory sound classification onboard the wearable sensor. Study shows that the third scenario featuring the lowest communication cost enables the lowest total sensor system power consumption ranging from 328 to 428 μW. In such scenario, 32-bit ARM Cortex M3/M4 cores typically embedded within Bluetooth 4 SoC modules feature the optimal trade-off between onboard classification performance and consumption. On the other hand, study confirms that CS enables the most power-efficient design of the wearable sensor (216 to 357 μW) in the compressed signal streaming, the second scenario. In such case, a single low-power ARM Cortex-A53 core is sufficient for simultaneous real-time CS reconstruction and classification on the smartphone, while keeping the total system power within budget for uncompressed streaming.

Information on air-quality in urban environments is typically measured only at limited number of sites, due to cost of measurement of atmospheric concentrations of toxic gases (CO, NO2, SO2) within accuracy boundaries defined by regulative bodies. Low spatial resolution of the mentioned environmental parameters hinders their applications in localization of the air-pollution sources, traffic regulation or studies of chronic respiratory diseases related to personal pollution exposure. Thus, we propose complementing the existing air quality monitoring infrastructure by a network of mobile sensors enabling the citizens to participate in measurement (e.g. "crowdsensing"). In this paper, we present the design of such battery-powered, wearable sensor node, housing two electrochemical gas sensors, temperature, relative humidity and atmospheric pressure sensors, with Bluetooth connectivity. Electrical, mechanical and software design are shown. Next, sensor node was characterized by evaluating the sensing accuracy and the autonomy in laboratory conditions. Accuracy within ±1 • C, ±2% RH, ±2 hPa, and ±0.6 ppm CO is shown. Autonomy is estimated at 65 h. Preliminary results of the outdoor functional test are demonstrated.978-1-4799-6117-7/15/$31.00 ©2015 IEEE This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.