The aim of this study was to develop a classification procedure for accelerometer data to recognize the mode of children's physical activity (PA) in free-living conditions and to compare it with an established cutoff method. Hip and wrist accelerometer data with an epoch interval of 1 s were collected for 7 days from 24 girls (age: 10.7 ± 1.7 years) and 17 boys (age: 10.6 ± 1.6 years). Videos were recorded during the same 7 days at several points of time at school and during leisure time. Each second of video data was labeled as one of nine activity classes. A classification procedure based on pattern recognition algorithms was trained with the accelerometer data relating to respective video labels of half of the children and tested against the data from the other half of the children. The overall recognition rate of the classification procedure was 67%. The procedure was able to classify 90% of stationary activities, 83% of walking, 81% of running and 61% of jumping activities. The remaining activities could not be recognized by the main classifier. This study developed a classification procedure based on well-accepted accelerometers and video recordings to recognize children's PA in free-living conditions. It has been shown to be valid for the activities of being stationary, walking, running and jumping. In contrast to former measurement and analysis procedures, this method is able to determine the modes of specific activities among children. Consequently, the presented classification procedure provides additional information on the PA behavior in children registered by established accelerometers.
Regular physical activity (PA) is an important contributor to a healthy lifestyle. Currently, standard sensor-based methods to assess PA in field-based research rely on a single accelerometer mounted near the body's center of mass. This paper introduces a wearable system that estimates energy expenditure (EE) based on seven recognized activity types. The system was developed with data from 32 healthy subjects and consists of a chest mounted heart rate belt and two accelerometers attached to a thigh and dominant upper arm. The system was validated with 12 other subjects under restricted lab conditions and simulated free-living conditions against indirect calorimetry, as well as in subjects' habitual environments for 2 weeks against the doubly labeled water method. Our stepwise validation methodology gradually trades reference information from the lab against realistic data from the field. The average accuracy for EE estimation was 88% for restricted lab conditions, 55% for simulated free-living conditions and 87% and 91% for the estimation of average daily EE over the period of 1 and 2 weeks.
Passes are a performance-relevant parameter in many team sports. They must be played in the highly dynamic and unpredictable contexts of interactive team competitions. The difficulty to plan passes in advance requires real-time decisions and highlights the importance of the perceptual information provided by current game contexts. This study estimates the relevance of perceptual information to passing decisions at an ecological scale by analyzing sports data from real competitions. In support of previous findings of a scenario-based investigation, open passing lanes, spatial proximity to the ball carrier, team members’ positions in front of the ball carrier, and loose defense by opposing players all significantly increased team members’ odds for receiving passes. Together, the four kinds of perceptual information enabled the correct prediction of 41% of the passes played. The prediction rate compares to a base rate of 11% and is substantially higher than that for passing decisions made in static game scenarios. The results are interpreted with regard to the relevance of the perceptual information to passing decisions made in time-constrained competitive situations.
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