We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals’ daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400 participants from two different datasets, and we verify the results against ground truth from dedicated armband sleep trackers. We show that the model is able to produce reliable sleep estimates with an accuracy of 0.89, both at the individual and at the collective level. Moreover the Bayesian model is able to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient.

Gaze and head tracking, or pointing, in head-mounted displays enables new input modalities for point-select tasks. We conducted a Fitts' law experiment with 41 subjects comparing head pointing and gaze pointing using a 300 ms dwell (n = 22) or click (n = 19) activation, with mouse input providing a baseline for both conditions. Gaze and head pointing were equally fast but slower than the mouse; dwell activation was faster than click activation. Throughput was highest for the mouse (3.24 bits/s), followed by head pointing (2.47 bits/s) and gaze pointing (2.13 bits/s). Dwell activation was faster than click activation. The effective target width for gaze (≈ 94 pixels; about 3 •) was larger than for head and mouse (≈ 72 pixels; about 2.5 •). Subjective feedback rated the physical workload less for gaze pointing than head pointing.

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Lab's binocular gaze tracking unit (used for gaze input). The Fitts' multi-directional selection task that the participant is performing inside the headset is also shown on the monitor. The headset also tracks orientation of the head (used for head input). B. Feet on a 3DRudder foot mouse with 360 • of movement control (used for foot input). Tilting the 3DRudder moves the cursor in that direction.

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Mental fatigue is a common problem associated with neurological disorders. Until now, there has not been a method to assess mental fatigue on a continuous scale. Camera-based eye-typing is commonly used for communication by people with severe neurological disorders. We designed a working memory-based eye-typing experiment with 18 healthy participants, and obtained eye-tracking and typing performance data in addition to their subjective scores on perceived effort for every sentence typed and mental fatigue, to create a model of mental fatigue for eye-typing. The features of the model were the eye-based blink frequency, eye height and baseline-related pupil diameter. We predicted subjective ratings of mental fatigue on a six-point Likert scale, using random forest regression, with 22% lower mean absolute error than using simulations. When additionally including task difficulty (i.e. the difficulty of the sentences typed) as a feature, the variance explained by the model increased by 9%. This indicates that task difficulty plays an important role in modelling mental fatigue. The results demonstrate the feasibility of objective and non-intrusive measurement of fatigue on a continuous scale.

ACHIEVING ROBUST ADAPTIVE SYNCHRONIZATION OF MULTIMODAL BIOMETRIC INPUTS: The recent arrival of wireless EEG headsets that enable mobile real-time 3D brain imaging on smartphones, and low cost eye trackers that provide gaze control of tablets, will radically change how biometric sensors might be integrated into next generation user interfaces. In experimental lab settings EEG neuroimaging and eye tracking data are traditionally combined using external triggers to synchronize the signals. However, with biometric sensors increasingly being applied in everyday usage scenarios, there will be a need for solutions providing a continuous alignment of signals. In the present paper we propose using spontaneous eye blinks, as a means to achieve near real-time synchronization of EEG and eye tracking. Analyzing key parameters that define eye blink signatures across the two domains, we outline a probability function based algorithm to correlate the signals. Comparing the accuracy of the method against a state of the art EYE-EEG plug-in for offline analysis of EEG and eye tracking data, we propose our approach could be applied for robust synchronization of biometric sensor data collected in a mobile context.

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