Perceptual narrowing is a highly significant development associated with the first year of life. It conventionally refers to an orientation toward nativeness whereby infant's perceptual sensitivities begin to align with the phonetic properties of their native environment. Nativeness effects, such as perceptual narrowing, have been observed in several domains, most notably, in face discrimination within other-race faces and speech discrimination of non-native phonemes. Thus, far, nativeness effects in the face and speech perception have been theoretically linked, but have mostly been investigated independently. An important caveat to nativeness effects is that diversifying experiences, such as bilingualism or multiracial exposure, can lead to a reduction or postponement in attunement to the native environment. The present study was designed to investigate whether bilingualism influences nativeness effects in phonetic and face perception. Eleven-month-old monolingual and bilingual infants were tested on their abilities to discriminate native and non-native speech contrasts as well as own-race and other-race face contrasts. While monolingual infants demonstrated nativeness effects in face and speech perception, bilingual infants demonstrated nativeness effects in the face perception but demonstrated flexibility in speech perception. Results support domain-specific effects of bilingual experience on nativeness effects.
Fitness activity classification on wearable devices can provide activity-specific information and generate more accurate performance metrics. Recently, optical head-mounted displays (OHMD) like Google Glass, Sony SmartEyeglass and Recon Jet have emerged. This paper presents a novel method to classify fitness activities using head-worn accelerometer, barometric pressure sensor and GPS, with comparisons to other common mounting locations on the body. Using multiclass SVM on head-worn sensors, we obtained an average F-score of 96.66% for classifying standing, walking, running, ascending/descending stairs and cycling. The best sensor location combinations were found to be on the ankle plus another upper body location. Using three or more sensors did not show a notable improvement over the best two-sensor combinations.
Orientation of human body segments is an important quantity in many biomechanical analyses. To get robust and drift-free 3-D orientation, raw data from miniature body worn MEMS-based inertial measurement units (IMU) should be blended in a Kalman filter. Aiming at less computational cost, this work presents a novel cascaded two-step Kalman filter orientation estimation algorithm. Tilt angles are estimated in the first step of the proposed cascaded Kalman filter. The estimated tilt angles are passed to the second step of the filter for yaw angle calculation. The orientation results are benchmarked against the ones from a highly accurate tactical grade IMU. Experimental results reveal that the proposed algorithm provides robust orientation estimation in both kinematically and magnetically disturbed conditions.
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