EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory-Visual Statistics and Autonomic Nervous System Function 'in the Wild'

Daylong egocentric recordings in small- and large-scale language communities: A practical introduction

Daylong egocentric (i.e., participant-centered) recordings promise an unprecedented view into the experiences that drive early language learning, impacting both assumptions and theories about how learning happens. Thanks to recent advances in technology, collecting long-form audio, photo, and video recordings with child-worn devices is cheaper and more convenient than ever. These recording methods can be similarly deployed across small- and large-scale language communities around the world, opening up enormous possibilities for comparative research on early language development. However, building new high-quality naturalistic corpora is a massive investment of time and money. In this chapter, we provide a practical look into considerations relevant for developing and managing daylong egocentric recording projects: Is it possible to re-use existing data? How much time will manual annotation take? Can automated tools sufficiently tackle the questions at hand? We conclude by outlining two exciting directions for future naturalistic child language research.

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, N = 16) and infants (Study 2, N = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, N = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, N = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, N = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research.