We present Captivates, an open-source smartglasses system designed for long-term, in-the-wild psychophysiological monitoring at scale. Captivates integrate many underutilized physiological sensors in a streamlined package, including temple and nose temperature measurement, blink detection, head motion tracking, activity classification, 3D localization, and head pose estimation. Captivates were designed with an emphasis on: (1) manufacturing and scalability, so we can easily support large scale user studies for ourselves and offer the platform as a generalized tool for ambulatory psychophysiology research; (2) robustness and battery life, so long-term studies result in trustworthy data individual's entire day in natural environments without supervision or recharge; and (3) aesthetics and comfort, so people can wear them in their normal daily contexts without self-consciousness or changes in behavior. Captivates are intended to enable large scale data collection without altering user behavior. We validate that our sensors capture useful data robustly for a small set of beta testers. We also show that our additional effort on aesthetics was imperative to meet our goals; namely, earlier versions of our prototype make people uncomfortable to interact naturally in public, and our additional design and miniaturization effort has made a significant impact in preserving natural behavior. There is tremendous promise in translating psychophysiological laboratory techniques into real-world insight. Captivates serve as an open-source bridge to this end. Paired with an accurate underlying model, Captivates will be able to quantify the long-term psychological impact of our design decisions and provide real-time feedback for technologists interested in actuating a cognitively adaptive, user-aligned future.

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Ambulatory monitoring of real-world voice characteristics and behavior has the potential to provide important assessment of voice and speech disorders and psychological and emotional state. In this paper, we report on the novel development of a lightweight, wireless voice monitor that synchronously records dual-channel data from an acoustic microphone and a necksurface accelerometer embedded on a flex circuit. In this paper, Lombard speech effects were investigated in pilot data from four adult speakers with normal vocal function who read a phonetically balanced paragraph in the presence of different ambient acoustic noise levels. Whereas the signal-to-noise ratio (SNR) of the microphone signal decreased in the presence of increasing ambient noise level, the SNR of the accelerometer sensor remained high. Lombard speech properties were thus robustly computed from the accelerometer signal and observed in all four speakers who exhibited increases in average estimates of sound pressure level (+2.3 dB), fundamental frequency (+21.4 Hz), and cepstral peak prominence (+1.3 dB) from quiet to loud ambient conditions. Future work calls for ambulatory data collection in naturalistic environments, where the microphone acts as a sound level meter and the accelerometer functions as a noise-robust voicing sensor to assess voice disorders, neurological conditions, and cognitive load.