Implementation of a Hidden Markov Model-based wheeze recognition algorithm on the PULP Fulmine platform achieved 82.85% sensitivity and 95.61% specificity while reducing average power by ~40% vs ARM Cortex-M4.
Does the implementation of a Hidden Markov Model-based algorithm on PULP Fulmine reduce power consumption compared to ARM Cortex-M4 for asthmatic wheeze recognition?
The PULP Fulmine platform significantly reduces power consumption for automated asthmatic wheeze recognition compared to standard ARM processors, enabling longer autonomy for wearable sensors.
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.
Oletić et al. (Fri,) conducted a other in Asthmatic wheeze. Hidden Markov Model-based algorithm on PULP Fulmine processor vs. ARM Cortex-M4 Blue Gecko processor was evaluated on Wheeze recognition sensitivity, specificity, and power consumption. Implementation of a Hidden Markov Model-based wheeze recognition algorithm on the PULP Fulmine platform achieved 82.85% sensitivity and 95.61% specificity while reducing average power by ~40% vs ARM Cortex-M4.
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