Validity of an upper-body-mounted accelerometer to measure peak vertical and resultant force during running and change-of-direction tasks

This study assessed the validity of a tri-axial accelerometer worn on the upper body to estimate peak forces during running and change-of-direction tasks. Seventeen participants completed four different running and change-of-direction tasks (0°, 45°, 90°, and 180°; five trials per condition). Peak crania-caudal and resultant acceleration was converted to force and compared against peak force plate ground reaction force (GRF) in two formats (raw and smoothed). The resultant smoothed (10 Hz) and crania-caudal raw (except 180°) accelerometer values were not significantly different to resultant and vertical GRF for all running and change-of-direction tasks, respectively. Resultant accelerometer measures showed no to strong significant correlations (r = 0.00–0.76) and moderate to large measurement errors (coefficient of variation [CV] = 11.7–23.9%). Crania-caudal accelerometer measures showed small to moderate correlations (r = ? 0.26 to 0.39) and moderate to large measurement errors (CV = 15.0–20.6%). Accelerometers, within integrated micro-technology tracking devices and worn on the upper body, can provide a relative measure of peak impact force experienced during running and two change-of-direction tasks (45° and 90°) provided that resultant smoothed values are used.     

Intermodal Perception of Adult and Child Faces and Voices by Infants

This research investigated the ability of 4- and 7-month-old infants to match unfamiliar, dynamic faces and voices on the basis of age or maturity. In Experiment 1, infants received videotaped trials of an adult and a child of the same gender, side by side, speaking a nursery rhyme in synchrony with one another. The voice to one and then the other face was played in synchrony with the movements of both faces in a random order across 12 trials. On one block of 6 trials a man and a boy were presented, and on the other block a woman and a girl. Results indicated significant matching of the faces and voices at both ages, and the infant's prior experience with children appeared to facilitate matching at 7 months. Further, a visual preference for the children's faces was found. Experiment 2 assessed matching to the same events by 7-month-olds, only with the faces inverted. Results indicated no evidence of matching; however, the visual preference for the children's faces was replicated. Together, the findings suggest that infants are able to detect invariant intermodal relations specifying the age or maturity of a person's face and voice. This matching was most likely based on information that was degraded by inverting the faces, including invariant relations between the sound of the voice and configurational aspects of the face, or between temporal aspects of the voice and the relative motion of facial features.     

Improved similarity-based modeling for the classification of rotating-machine failures

Similarity-based modeling (SBM) is a technique whereby the normal operation of a system is modeled in order to detect faults by analyzing their similarity to the normal system states. First proposed around two decades ago, SBM has been successfully used for fault detection in varied systems. In spite of this success, there is not much study performed in the literature regarding its design, that encompasses both similarity metrics and model training. This work aims at contributing with an in-depth study of SBM for fault detection considering these two design aspects. This is done in the context of proposing a novel system to identify rotating-machinery faults based on SBM, that is employed either as a standalone classifier or to generate features for a random forest classifier. New approaches for training the model and new similarity metrics are investigated. Experimental results are shown for the recently developed Machinery Fault Database (MaFaulDa) that has an extensive set of sequences and fault types, and for the Case Western Reserve University (CWRU) bearing database. Results for both databases indicate that the proposed techniques increase the generalization power of the similarity model and of the associated classifier, achieving accuracies of 98.5% on MaFaulDa and 98.9% on CWRU database.