TY - GEN
T1 - Evaluation in a real environment of a trainable cough monitoring app for smartphones
AU - Hoyos-Barceló, Carlos
AU - Garmendia-Leiza, José Ramón
AU - Aguilar-García, María Dolores
AU - Monge-Álvarez, Jesús
AU - Pérez-Alonso, Diego Asay
AU - Alberola-López, Carlos
AU - Casaseca-de-la-Higuera, Pablo
PY - 2019/9/25
Y1 - 2019/9/25
N2 - This paper presents SmartCough, an M-health app for Android smartphones that monitors cough trends in patients with respiratory diseases. The app is designed to be battery-efficient, fast, and robust against noise. It relies on efficiently-implemented machine learning algorithms that have been validated in laboratory conditions. Since these conditions are rarely met in a real situation where the user carries the phone inside their pocket or bag, the app features a self-training module that allows easy adaptation to new environments. In this paper, we have evaluated the app with real patients in an outdoor setting to test the performance in real environments that are hostile to cough detection. Our results show that the average sensitivity obtained in laboratory conditions drops significantly (down to 60%) when the baseline configuration is employed. By activating the built-in self-training module, the median sensitivity raises to 85.87% after a small training step, with a bounded false positive rate. The achieved performance is analogous to the one obtained in laboratory conditions, making the app suitable for use in real life scenarios.
AB - This paper presents SmartCough, an M-health app for Android smartphones that monitors cough trends in patients with respiratory diseases. The app is designed to be battery-efficient, fast, and robust against noise. It relies on efficiently-implemented machine learning algorithms that have been validated in laboratory conditions. Since these conditions are rarely met in a real situation where the user carries the phone inside their pocket or bag, the app features a self-training module that allows easy adaptation to new environments. In this paper, we have evaluated the app with real patients in an outdoor setting to test the performance in real environments that are hostile to cough detection. Our results show that the average sensitivity obtained in laboratory conditions drops significantly (down to 60%) when the baseline configuration is employed. By activating the built-in self-training module, the median sensitivity raises to 85.87% after a small training step, with a bounded false positive rate. The achieved performance is analogous to the one obtained in laboratory conditions, making the app suitable for use in real life scenarios.
KW - Android
KW - Cough detection
KW - M-health
KW - Optimization
U2 - 10.1007/978-3-030-31635-8_142
DO - 10.1007/978-3-030-31635-8_142
M3 - Conference contribution
AN - SCOPUS:85075893462
SN - 9783030316341
T3 - IFMBE Proceedings
SP - 1175
EP - 1180
BT - 15th Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 - Proceedings of MEDICON and Computing and
A2 - Henriques, Jorge
A2 - de Carvalho, Paulo
A2 - Neves, Nuno
PB - Springer
T2 - 15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019
Y2 - 26 September 2019 through 28 September 2019
ER -