NONLINEAR ANALYSIS OF RACE WALKING GAIT: MOVEMENT VARIABILITY, ENTROPY AND MOTOR SKILLS ASSESSMENT

The aim of this study was to explore the issue of motor skills characterisation, by assessing the regularity of motor patterns during race walking gait. Seven competitive race walkers’ action was analysed through an optoelectronic system and a force platform. Sample entropy, a nonlinear dynamics tool, was adopted to evaluate the regularity of a selection of kinematic and kinetic variables. Results allowed to characterise athletic skill and to evidence the changes that may occur over time. In particular, the subtle anomalies of an injured subject were detected. Sample entropy appeared a valid means for individual monitoring in sports and gave clues for possible injury prevention

Phenology-based land cover classification using Landsat 8 time series

This article describes the methodology and results of a new JRC phenology-based classification algorithm able to generate accurate land cover maps in a fully automatic manner from Landsat 8 (L8) remote sensed data available since 12th April 2013 at no charge throughout the USGS website. A preliminary study aiming to bypass the single date classification inaccuracy (mainly due to seasonality) using long term MODIS time series as a “driver” to fill gaps between high resolution data, has been carried out. The high global acquisition frequency (~16 days) and distribution policy are making Landsat 8 product extremely suitable for near real time land cover mapping and monitoring. Five national parks in east Africa have been selected as study areas (Mahale Mountains, Mana Pools, West Lunga, Gorongosa, Tsimanampetsotsa); they are covering diverse eco-regions and vegetation types, from evergreen to deciduous. A buffer of 20 km around each park has been considered as well. Selected single date images were first preprocessed in order to convert raw DN values to top of atmosphere (TOA) reflectance and minimizes spectral differences caused by different acquisition time, sun elevation, sun-earth distance, and after processed by the algorithm to generate a thematic raster map with land cover classes. Is worth noting that the single date classification accuracy is closely related to the acquisition date of the image, the status of the vegetation and weather conditions such as cloud and shadows often present in tropical regions; here the need of developing a phenology based algorithm that considers the vegetation evolution and generates a more accurate land cover map including evergreen and deciduous discrimination on the basis of “frequency” rules. Land cover maps have been created for all parks and an exhaustive accuracy assessment has been carried out on Mahale Mountains and Tsimanampetsotsa. The combined overall accuracy of 82.8% demonstrates the high potentiality of this method and makes it usable at either local or regional scale.JRC.H.3-Forest Resources and Climat

Medio-lateral acceleration of female athletes with an ACL reconstruction in comparison with a healthy population

We assessed change of direction abilities in team sport female athletes. Ten participants returned to full activity from ACL reconstruction (ACLR) for longer than one year and 10 healthy controls (CON) were compared. Twelve maximal effort 45° cutting manoeuvres from the reconstructed side (ACLR) or a random selection of both sides (CON) were analysed for each individual using a motion capture system. CON showed greater medio-lateral centre of mass (COM) acceleration between 51 and 75 % of ground contact. CON reported increased ankle plantar flexor power from 94 to 98 % of foot contact, with also a higher peak. Although cleared to return to full activity, biomechanical differences associated with performance were still present in the ACLR group, which may have implications for better targeting rehabilitation practice

BIOVARIABILlTY: THE STARTING POINT FOR DEVELOPING RELIABLE MOTOR FEED-BACK PROCEDURES IN SPORTS

Modem technologies allow multifactorial analyses of sports movement. Their output might be used as a reliable feed-back for sports motor applications, but individual variability must be assessed first, to gain more insight on treating data, interpreting results and presenting suggestions. Former studies evidenced important aspects of multijoint coordination and successfully interpreted the role of many kinematic/kinetic measures. Nevertheless they didn't examine biovariability extensively. In this work a common, Widely-studied field test (vertical jump) was chosen to evaluate intralintersubject variance. Results showed a considerable variability in many kinematic and kinetic parameters. Some suggestions on treating data are proposed. Some indications of possible applications are presented

A support vector machine algorithm can successfully classify running ability when trained with wearable sensor data from anatomical locations typical of consumer technology

Greater understanding of differences in technique between runners may allow more beneficial feedback related to improving performance and decreasing injury risk. The purpose of this study was to develop and test a support vector machine classifier, which could automatically differentiate running technique between experienced and novice participants using only wearable sensor data. Three-dimensional linear accelerations and angular velocities were collected from six wearable sensors secured to current common smart device locations. Cross-validation was used to test the classification accuracy of models trained with a variety of combinations of sensor locations, with participants running at different speeds. Average classification accuracies ranged from 71.3% to 98.4% across the sensor combinations and running speeds tested. Models trained with only a single sensor location still showed effective classification. With the models trained with only upper arm data achieving an average accuracy of 96.4% across all tested running speeds. A post-hoc comparison of biomechanical variables between the two subgroups showed significant differences in upper body biomechanics throughout the stride. Both the methodology used to perform the classifications and the biomechanical differences identified could prove useful when aiming to shift a novice runner’s technique towards movement patterns more akin to those with greater experience

Supervised machine learning applied to wearable sensor data can accurately classify functional fitness exercises within a continuous workout

Observing, classifying and assessing human movements is important in many applied fields, including human-computer interface, clinical assessment, activity monitoring and sports performance. The redundancy of options in planning and implementing motor programmes, the inter- and intra-individual variability in movement execution, and the time-continuous, high-dimensional nature of motion data make segmenting sequential movements into a smaller set of discrete classes of actions non-trivial. We aimed to develop and validate a method for the automatic classification of four popular functional fitness drills, which are commonly performed in current circuit training routines. Five inertial measurement units were located on the upper and lower limb, and on the trunk of fourteen participants. Positions were chosen by keeping into account the dynamics of the movement and the positions where commercially-available smart technologies are typically secured. Accelerations and angular velocities were acquired continuously from the units and used to train and test different supervised learning models, including k-Nearest Neighbors (kNN) and support-vector machine (SVM) algorithms. The use of different kernel functions, as well as different strategies to segment continuous inertial data were explored. Classification performance was assessed from both the training dataset (k-fold cross-validation), and a test dataset (leave-one-subject-out validation). Classification from different subsets of the measurement units was also evaluated (1-sensor and 2-sensor data). SVM with a cubic kernel and fed with data from 600 ms windows with a 10% overlap gave the best classification performances, yielding to an overall accuracy of 97.8%. This approach did not misclassify any functional fitness movement for another, but confused relatively frequently (2.8–18.9%) a fitness movement phase with the transition between subsequent repetitions of the same task or different drills. Among 1-sensor configurations, the upper arm achieved the best classification performance (96.4% accuracy), whereas combining the upper arm and the thigh sensors obtained the highest level of accuracy (97.6%) from 2-sensors movement tracking. We found that supervised learning can successfully classify complex sequential movements such as those of functional fitness workouts. Our approach, which could exploit technologies currently available in the consumer market, demonstrated exciting potential for future on-field applications including unstructured training

A BIOMECHANICAL COMPARISON BETWEEN RACEWALKING AND NORMAL WALKING STANCE PHASE

The purpose of this study was to understand the peculiarities of lower limbs kinematics and kinetics during racewalking stance, by comparing it with normal walking gait. Four young but skilled athletes were analysed through a motion analysis system and a force platform. Results were expressed in terms of average kinetics and kinematics curves. Many differences emerged between the two walking modes, most of which can be reconducted to the rules that govern this sport. These observations might be useful, both for trainers and practitioners, to gain more insight in racewalking technique and proficiency, but they are just a first step toward an effective application “on the field”. In fact, an individual analysis might discover the athlete’s distinctive biomechanical characteristics and consequently might help in setting better training methodologies