Hidden Markov Model-Based Asthmatic Wheeze Recognition Algorithm Leveraging the Parallel Ultra-Low-Power Processor (PULP) (CROSBI ID 674027)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Oletic, Dinko ; Matijascic, Marko ; Magno, Michele ; Bilas, Vedran
engleski
Hidden Markov Model-Based Asthmatic Wheeze Recognition Algorithm Leveraging the Parallel Ultra-Low-Power Processor (PULP)
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.
wearable ; asthmatic wheeze recognition ; Hidden Markov model ; low-power ; embedded ; parallel processing
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Podaci o prilogu
1-6.
2019.
objavljeno
Podaci o matičnoj publikaciji
IEEE Sensors Applications Symposium 2019 Conference Proceedings
Institute of Electrical and Electronics Engineers (IEEE)
978-1-5386-7713-1
Podaci o skupu
15th Advanced International Conference on Telecommunications (AICT 2019)
predavanje
11.03.2019-13.03.2019
Nica, Francuska