Mental object rotation and motor development in 8- and 10-month-old infants

Recent evidence indicates that 6-month-old infants’ mental rotation of objects profits from prior manual experience, whereas observational experience does not have the same beneficial effect (Möhring, W. & Frick, A., 2013, Child Development). The present study investigated whether older infants, at 8 and 10 months of age, succeed in this task after observational experience only, and whether performance is related to infants’ motor development. Using the violation-of-expectation paradigm, infants (N = 40) were presented with an asymmetrical object that was moved straight down behind an occluder. After the occluder was lowered, infants saw either the original object (possible event) or a mirror image of the original object (impossible event) in one of five different orientations (0° to 180°, in steps of 45°). Results indicated that it was not until 10 months of age that infants looked longer at the impossible outcome. Analyses including parent questionnaire data showed that mental rotation performance was related to infants’ motor development emphasizing the importance of action experience for early cognitive development

Opisthenar : hand poses and finger tapping recognition by observing back of hand using embedded wrist camera

We introduce a vision-based technique to recognize static hand poses and dynamic finger tapping gestures. Our approach employs a camera on the wrist, with a view of the opisthenar (back of the hand) area. We envisage such cameras being included in a wrist-worn device such as a smartwatch, fitness tracker or wristband. Indeed, selected off-the-shelf smartwatches now incorporate a built-in camera on the side for photography purposes. However, in this configuration, the fingers are occluded from the view of the camera. The oblique angle and placement of the camera make typical vision-based techniques difficult to adopt. Our alternative approach observes small movements and changes in the shape, tendons, skin and bones on the opisthenar area. We train deep neural networks to recognize both hand poses and dynamic finger tapping gestures. While this is a challenging configuration for sensing, we tested the recognition with a real-time user test and achieved a high recognition rate of 89.4% (static poses) and 67.5% (dynamic gestures). Our results further demonstrate that our approach can generalize across sessions and to new users. Namely, users can remove and replace the wrist-worn device while new users can employ a previously trained system, to a certain degree. We conclude by demonstrating three applications and suggest future avenues of work based on sensing the back of the hand.Postprin

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems