Naslov
Hidden Markov Model-Based Asthmatic Wheeze Recognition Algorithm Leveraging the Parallel Ultra-Low-Power Processor (PULP)

Autori
Oletic, Dinko ; Matijascic, Marko ; Magno, Michele ; Bilas, Vedran

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
IEEE Sensors Applications Symposium 2019 Conference Proceedings / - : Institute of Electrical and Electronics Engineers (IEEE), 2019, 1-6

ISBN
978-1-5386-7713-1

Skup
15th Advanced International Conference on Telecommunications (AICT 2019)

Mjesto i datum
Nica, Francuska, 11.03.2019. - 13.03.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
wearable ; asthmatic wheeze recognition ; Hidden Markov model ; low-power ; embedded ; parallel processing

Sažetak
Asthmatic symptoms can be quantified by a wearable sensor system, recording respiratory sounds on patient’s skin surface, and performing automated asthmatic wheeze recognition based on time-frequency features. In order to enable long-term autonomy of such sensor system, a crucial design requirement is ensuring energy-efficient yet accurate wheeze recognition performance. We presented a Hidden Markov Model based algorithm for recognition of wheezing intervals durations, by sequentially extracting individual wheezing-frequency lines from the spectrogram of respiratory sounds. In this paper we compare its implementation on an ARM Cortex-M4 processor and an emerging parallel ultra-low-power processing platform PULP Fulmine. It is shown that the algorithm enables wheeze recognition with 82.85% of sensitivity and 95.61% specificity, for only 0.9-1.6 mW of power. It is experimentally verified that algorithm benefits from a multi- core architectures such as PULP Fulmine. The implementation on this platform brings up to around 40% reduction of average power spent on processing, compared to the ARM Cortex-M4 Blue Gecko.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

POVEZANOST RADA