BioscoreNet: Traumatic Brain Injury (TBI) detection using a multimodal self-attention fusion neural network and a passive bioscore monitoring framework from smartphone sensor data

Abstract

Traumatic Brain Injury (TBI) affects millions of individuals globally and can cause motor, cognitive and emotional deficits. Emerging research in the field of mobile health has demonstrated that smartphone sensor data can be used to monitor various chronic diseases, which can facilitate early ailment detection and continuous monitoring of recovering patients. The goal of the paper’s contributions is two-fold: Proposing (1) A Deep Learning framework that uses smartphone sensor data mobility data (accelerometer, magnetometer, gyroscope, pedometer, pressure, altitude, accessibility) to detect whether a subject has TBI after a head injury. (2) The concept of a TBI Bioscore, a number that quantifies the certainty (0–1) that a subject has TBI. The TBI deep learning detection model uses a self-attention mechanism for multimodal feature fusion and a stacked LSTM for prediction. This model achieves a Balanced Accuracy (BA) of 90.2% and a True Positive Rate (TPR) of 83.3% in correctly identifying TBI instances. The Bioscore is generated using Monte-Carlo Dropout uncertainty estimation. When a users’ TBI Bioscores are visualized throughout, it steadily decreases for users who have normal recoveries. The proposed TBI detection and Bioscore framework facilitates population-level passive, continuous and remote TBI screening and monitoring. A glanceable display of color-coded Bioscores can enable a small team of medical staff to monitor a large pool of patients and intervene preemptively. • This paper proposed a deep learning framework that uses smartphone sensor data (accelerometer, magnetometer, gyroscope, pedometer, pressure, altitude, accessibility) to detect whether a subject has Traumatic Brain Injury (TBI) after a head injury. • This paper also proposed the concept of a TBI Bioscore, a number that quantifies the certainty (0–1) that a subject has TBI, generated using Monte-Carlo Dropout uncertainty estimation that facilitates populationlevel monitoring. • The TBI deep learning detection model uses a self-attention mechanism for multimodal feature fusion and a stacked LSTM for prediction. • The TBI detection model achieves a Balanced Accuracy (BA) of 90.2% and a True Positive Rate (TPR) of 83.3% in correctly identifying TBI instances.