Smartphone TBI Sensing using Deep Embedded Clustering and Extreme Boosted Outlier Detection

Srikanthan, Srinarayan

Etd

Smartphone TBI Sensing using Deep Embedded Clustering and Extreme Boosted Outlier Detection

Public

Traumatic Brain Injury (TBI) can have long-lasting effects and possibly cause life-long disability of patients, creating a huge economic and social burden. Many TBI patients do not get early and adequate medical care. Sensor-rich, ubiquitously owned smartphones can now be used to passively sense a wide range of ailments, facilitating continuous monitoring of patients and high-risk groups in the real world. In this thesis, we propose a Deep learning approach for distinguishing smartphone users with TBI from health controls within 24-hours of the injury. Our method analyzes smartphone sensor data by first utilizing Deep embedded clustering to identify user clusters with similar smart-phone sensed behaviors. Extreme Gradient based outlier detection is then employed on each of the identified clusters to predict users with TBI. In rigorous evaluation, our method achieves a balanced accuracy of 88% and a sensitivity of 74%. Our proposed method can flag smartphone users with TBI, enabling them to receive early medical attention and improve their prognostic outlook.

Creator