The incentive spirometer is used after abdominal or thoracic surgery. This talk is about an alternative music-focused electronic device. The incentive spirometer is a device prescribed to patients following abdominal or thoracic surgery, to facilitate deep breathing and reduce or prevent postoperative complications. Disengagement with the incentive spirometer has been reported to contribute to lowered patient compliance, hence expected respiratory benefits have not been realised. To potentially improve respiratory outcomes and satisfaction during therapy, a music-focused electronic therapeutic device has been modelled from the incentive spirometer. The development process of both the hardware design and accompanying mobile app will be discussed. In addition, the next steps for the project will be outlined, including insights into how machine learning could be incorporated into future iterations. Bio: Thomas Beltrame is a Data Scientist at Consilium Technology, applying machine learning models in the agricultural, mining and defence sector, with two years of experience programming in Python to perform Machine Learning and Data Analytics. Thomas was first introduced to Artificial Intelligence after receiving the New Colombo Plan Mobility Grant to develop facial detection algorithms in C++, in Singapore. Thomas was awarded First Class Honours and the Flinders University Medal after completing a Master’s in Biomedical Engineering. For his master’s, Thomas combined his musical interest with his strong passion for rehabilitation and assistive technology, by converting breath velocity into an electronic trumpet. Thomas’ research was presented in Melbourne, at the Australian Biomedical Engineering Conference in 2019. Thomas is also passionate about STEM education for school students, developing and delivering interactive workshops that incorporate programming, electronics, robotics and assistive technology concepts. Co-sponsored by: Society for Medical and Biological Engineering (SMBE) Speaker(s): Thomas Beltrame, Virtual: https://events.vtools.ieee.org/m/294757