Fatigue Assessment from Facial Videos using Deep Neural Networks and Engineered Features Informed by Domain Knowledge.

Fatigue impairs cognitive and motor function, potentially leading to mishaps in high-pressure occupations such as aviation and emergency medical services. The current approach is primarily based on self-assessment, which is subjective and error-prone. An objective method is needed to detect severe and likely dangerous levels of fatigue quickly and accurately. Here, we present a quantitative evaluation tool that uses less than two minutes of facial video, captured using an iPad, to assess fatigue vs. alertness. The tool is fast, easy to use, and scalable since it uses cameras readily available on consumer-electronic devices. We compared the classification performance between a Long Short-Term Memory (LSTM) deep neural network and a Random Forest (RF) classifier applied to engineered features informed by domain knowledge. The preliminary results on an 11-subject dataset show that RF outperforms LSTM, with added interpretability on the features used. For the RF classifiers, the average areas under the receiver operating characteristic curve, based on the 11-fold and individualized 11-fold cross validations, are 0.72 ± 0.16 and 0.8 ± 0.12, respectively. Equal error rates are 0.34 and 0.26, respectively. This study presents a promising approach for rapid fatigue detection. Additional data will be collected to assess the generalizability across populations.

Assessment of speech and fine motor coordination in children with autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by difficulty in communication, which includes a high incidence of speech production errors. We hypothesize that these errors are partly due to underlying deficits in motor coordination and control, which are also manifested in degraded fine motor control of facial expressions and purposeful hand movements. In this pilot study, we computed correlations of acoustic, video, and handwriting time-series derived from five children with ASD and five children with neurotypical development during speech and handwriting tasks. These correlations and eigenvalues derived from the correlations act as a proxy for motor coordination across articulatory, laryngeal, and respiratory speech production systems and for fine motor skills. We utilized features derived from these correlations to discriminate between children with and without ASD. Eigenvalues derived from these correlations highlighted differences in complexity of coordination across speech subsystems and during handwriting, and helped discriminate between the two subject groups. These results suggest differences in coupling within speech production and fine motor skill systems in children with ASD. Our long-term goal is to create a platform assessing motor coordination in children with ASD in order to track progress from speech and motor interventions administered by clinicians.