|24||Machine Learning Enabled Wearable Stethoscope
|There have been many recent advances around using several machine learning methods to detect and identify abnormalities in heart beat and lung breath audio. We are proposing a wearable system (to be worn around the chest) which will record audio, and analyze it, looking for abnormalities in the heart beats or lung sounds. This would provide a significant improvement in care for people at risk of issues with heart or lungs because it would be equivalent to having the attention of a doctor at all times. Use cases include examples such as firefighters (who have high rates of heart defects, and are also at risk of smoke inhalation on the job), hospital patients coming out of heart or lung surgery, or people who have history of issues with heart or lungs.
The sensor would comprise of a sensitive microphone (and associated DSP circuitry), which would pick up the sounds from the heart or lungs. This would then get sent to the next sub-unit, which would process the audio. Depending on the complexity of either implementation (and the time and resource limitations) we could either use a microprocessor to implement a k-NN or a CNN algorithm to identify the sounds, or process through a hardware implementation of the k-NN or CNN.
Once a worrisome sound is identified, it is communicated to the relevant party. In the case of hospital patients, it would be communicated to the doctor and nurses station. In the case of at home care, it would be communicated to doctor and emergency services. Finally in the case of emergency personnel (firefighters) it would be communicated to the captain and other emergency personnel. The communication would be implemented through RF. This has many advantages; it can be integrated with existing medical pager system, and since it only communicates when there is an issue it does not always need to be activated (and in the use cases it would only need to activate a handful of times), saving in power requirements.