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Machine learning development of an App-based quality assessment module for home spirometry
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Summary
Home spirometry devices are widely used to assess lung functionality. However, unlike clinical spirometry machines where an expert supervisor is present, the data created by the home devices can be prone to user errors or other factors that influence the quality. The Health Technologies group in collaboration with Norwich Medical School is developing novel AI methods to recognise occurrences of these factors that renders the data not acceptable for further diagnostic assessment. This is done first in analysing the waveforms created by the spirometer devices but also by utilizing the sensor capacity of paired devices such as smartphones to detect common user behaviours that gives context to how the device was used. This work stems from the TIPAL project at Norwich Clinical Trials Unit.
PGR Poster (PDF, 1.58MB)
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Funding
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