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

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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.

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