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

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

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

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

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