Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors

Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy, autism spectrum disorders). Previously, novel wearable technology has shown promise for offering efficient, scalable and automated methods for movement assessment in adults. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscope data collection during movements. Using this suit, we first recorded play sessions of 22 typically developing infants of approximately 7 months of age. These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group. A machine learning algorithm, based on deep convolutional neural networks (CNNs) was then trained for automatic detection of posture and movement classes using the data and annotations. Our experiments show that the setup can be used for quantitative tracking of infant movement activities with a human equivalent accuracy, i.e., it meets the human inter-rater agreement levels in infant posture and movement classification. We also quantify the ambiguity of human observers in analyzing infant movements, and propose a method for utilizing this uncertainty for performance improvements in training of the automated classifier. Comparison of different sensor configurations also shows that four-limb recording leads to the best performance in posture and movement classification.Peer reviewe

Exploring influencing factors of technology use for active and healthy ageing support in older adults

Aim of this study is to investigate the influence of technological and social cognitive factors for the use of sensor-based technologies for active and healthy ageing (AHA) support by older adults. In a mixed methods approach, data was initially obtained from an online questionnaire completed by older health technology users and used in a regression analysis, where factors from the Technology Acceptance Model (TAM) and the Social Cognitive Theory (SCT) served as predictors for health technology use (HTU). Further, in-depth interviews were conducted with older adults to gain insights into technology use and physical activity behaviour of older adults. The regression analysis showed that the TAM and SCT factors accounted for a significant proportion of variance (39.5%) in HTU. Significant predictors of HTU were physical activity (.399**), social support (.287*), and expectations regarding individual health (.440*) and physical appearance (−.470**), indicating physical activity as mediator for HTU. The qualitative analysis indicated the conflation of technology support with social environments as key for physical activity behaviour in older adults. The findings indicate physical activity as a mediator in HTU by older adults and suggest that the consideration of social factors in health technology design may facilitate the uptake of AHA technologies