Recognition of activities in children by two uniaxial accelerometers in free-living conditions

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

Measuring physical load in soccer: strengths and limitations of 3 different methods

To investigate the strengths and limitations of different indicators to measure physical load. Furthermore, indicators were evaluated for discrimination between performance levels and playing positions. Methods: Ninety positional match files from 70 elite players and 91 match files from 69 subelite players were collected during 14 official under-18 matches using a local position measurement system. Indicators are calculated from speed, absolute acceleration (acc-abs), or percentage acceleration (acc-%). The acc-% describes the level of acceleration depending on the maximal voluntary acceleration (amax) for each initial running speed. Effect sizes (ES) were used to determine discriminative ability. Results: The number of high accelerations largely depended on the method (absolute threshold [>3 m·s−2 and >4 m·s−2] 120 and 59 efforts; high percentage threshold [>75% amax] 84 efforts). Only a small number of highly accelerated efforts reached speeds considered high-speed running (>19.8 km·h−1: 32.6%). More high acc-% exists from initial running speed >2 m·s−1 (23.0) compared with acc-abs (>3 m·s−2 14.4, >4 m·s−2 5.9). Elite players achieve higher values in most performance indicators, with ES being highest for the number of high acc-% (ES = 0.91) and high acc-abs (>3 m·s−2 ES = 0.86, >4 m·s−2 ES = 0.87), as well as for covered distance in jogging (ES = 0.94). Conclusions: Estimated physical load, discriminative ability of physical indicators, and positional requirements largely depend on the applied method. A combination of speed-based and acc-% methods is recommended to get a comprehensive view

Probing the interplay between lattice dynamics and short-range magnetic correlations in CuGeO3 with femtosecond RIXS

Investigations of magnetically ordered phases on the femtosecond timescale have provided significant insights into the influence of charge and lattice degrees of freedom on the magnetic sub-system. However, short-range magnetic correlations occurring in the absence of long-range order, for example in spin-frustrated systems, are inaccessible to many ultrafast techniques. Here, we show how time-resolved resonant inelastic X-ray scattering (trRIXS) is capable of probing such short-ranged magnetic dynamics in a charge-transfer insulator through the detection of a Zhang-Rice singlet exciton. Utilizing trRIXS measurements at the O K-edge, and in combination with model calculations, we probe the short-range spin-correlations in the frustrated spin chain material CuGeO3 following photo-excitation, revealing a strong coupling between the local lattice and spin sub-systems

Real-time football analysis with StreamTeam

In the last years, the analysis of data in sports has received considerable attention, especially due to the wide availability of unobtrusive wearable sensors. While most approaches focus on the (post-hoc) monitoring of individuals, a big and still largely unsolved challenge is the monitoring of the tactical behavior and tactical compliance of entire teams in real-time. In this paper, we introduce STREAMTEAM, a novel and extensible workflow-based approach to analyze data streams and to detect complex team events in real-time. We show the application of STREAMTEAM to data sets coming from sensors attached to players of football teams

Measuring Physical Load in Soccer: Strengths and Limitations of 3 Different Methods

Purpose: To investigate the strengths and limitations of different indicators to measure physical load. Furthermore, indicators were evaluated for discrimination between performance levels and playing positions. Methods: Ninety positional match files from 70 elite players and 91 match files from 69 subelite players were collected during 14 official under-18 matches using a local position measurement system. Indicators are calculated from speed, absolute acceleration (acc-abs), or percentage acceleration (acc-%). The acc-% describes the level of acceleration depending on the maximal voluntary acceleration (amax) for each initial running speed. Effect sizes (ES) were used to determine discriminative ability. Results: The number of high accelerations largely depended on the method (absolute threshold [>3 m·s-2 and >4 m·s-2] 120 and 59 efforts; high percentage threshold [>75% amax] 84 efforts). Only a small number of highly accelerated efforts reached speeds considered high-speed running (>19.8 km·h-1: 32.6%). More high acc-% exists from initial running speed >2 m·s-1 (23.0) compared with acc-abs (>3 m·s-2 14.4, >4 m·s-2 5.9). Elite players achieve higher values in most performance indicators, with ES being highest for the number of high acc-% (ES?=?0.91) and high acc-abs (>3 m·s-2 ES?=?0.86, >4 m·s-2 ES?=?0.87), as well as for covered distance in jogging (ES?=?0.94). Conclusions: Estimated physical load, discriminative ability of physical indicators, and positional requirements largely depend on the applied method. A combination of speed-based and acc-% methods is recommended to get a comprehensive view

The SportSense User Interface for Holistic Tactical Performance Analysis in Football

In today's team sports, the effective and user-friendly support of analysts and coaches in analyzing their team's tactics is essential. In this paper, we present an extended version of SportSense, a tool for searching in sports video by means of sketches, for creating and visualizing statistics of individual players and the entire team, and for visualizing the players' off-ball movement. SportSense has been developed in close collaboration with football coaches

StreamTeam-Football: Analyzing Football Matches in Real-Time on the Basis of Position Streams

In recent years, Big Data has become an important topic in many areas of our daily lives, including sports. Almost all professional clubs analyze matches to improve the performance of their teams. However, events are still predominantly captured manually, although many sensor-based and video-based tracking systems exist which provide the positions of the players and the ball in real-time. This manual process is tedious and errorprone. In this paper, we propose STREAMTEAM-FOOTBALL, an open source football analysis application, to fill this gap. STREAMTEAM-FOOTBALL allows to analyze football matches fully automatically and in real-time on the basis of tracked position data using a data stream analysis approach. Our evaluations confirm the effectiveness of our automated analysis and further show the scalability of STREAMTEAM-FOOTBALL by its ability to analyze multiple football matches in parallel