Periodic assessments of motor function in children with Cerebral Palsy can enable clinicians to make more informed decisions about the type and timing of treatment interventions. Current clinical practice is limited to sporadic assessments performed in a clinical environment and hence, not suitable for capturing small changes that occur longitudinally. We have developed a shoe-based wearable sensor system that allows unobtrusive long-term collection of center of pressure data in the home setting. So far the shoe-based system has been used to collect data from 15 subjects under supervised and semi-supervised settings. In this paper, we present a novel methodology, based on the analysis of center of pressure trajectories using Active Shape Models, for automated clinical assessment of gait deviations in children with Cerebral Palsy. We show that Active Shape Models can be used to effectively model characteristics of the center of pressure trajectories that are associated with specific aspects of gait deviations. A support vector machine classifier, trained on features derived from the Active Shape Models, is able to achieve an accuracy of greater than 90% at classifying clinical scores of gait deviation severity.

Millions of people run. Movement scientists investigate the relationship of running kinematics to fatigue, injury, or running economy mainly using optical motion capture. It was found that running kinematics are highly individual and often cannot be summarized by single variables. We thus present a data-driven analysis of running technique using wearable technology, combining statistical features and machine learning techniques, which allows to identify non-linear, complex relationships. Wearable technology enables running kinematic analysis to a broad mass in unconstrained environments. 20 runners wore 12 sensor units during two experiments: an all out test and a fatiguing run. We used a Support Vector Machine (SVM) to distinguish skill level groups and achieved an accuracy of 76.92% with an acceleration sensor on the upper body. Sensor positions were ranked according to the movement change with fatigue using a feature selection. This ranking was consistent with visual annotations of a movement scientist. We propose a quantitative measure of movement change using a principal component analysis (PCA) and found an average correlation of 0.8369 for all runners with their perceived rating of fatigue.

In this paper, we investigate monitoring of kinematic changes evoked by fatigue in running using wearable technology. Movement data were recorded with ETHOS devices. ETHOS is the ETH Orientation Sensor, a customized inertial measurement unit for unconstrained monitoring of human movement. We perform two real-world experiments, in which 21 runners of different skill levels participated. The real-world experiments capture two exhausting 45-min runs: one on a treadmill and one on a conventional outdoor track. We describe and evaluate algorithms to extract kinematic parameters from the sensor data. We identified parameters that change with fatigue for all runners, ones that change for runners of distinct skill levels, and ones that are dependent on an individual's running technique. Overall, we found that observations from treadmill running are not always generalizable to outdoor running. We, thus, argue for using wearable technology to provide athletes and trainers with continuous, quantitative objective measurements of running technique. These could be used to further gain insight into the complex relationship of running kinematics, injury risk, fatigue, and running economy.

Injuries in running are often provoked by fatigue or improper technique, which are both reflected in the runner's kinematics. State of the art research on kinematics in sports is using optical motion capture systems that are inaccessible to most athletes. This paper demonstrates the potential of wearable sensors for runners' kinematics analysis. We present a user study including 21 subjects of different running experience that performed an exhausting run on a conventional outside track wearing ETHOS units. For performance level assessment, training assistance, and fatigue monitoring we extracted the foot contact duration, the foot strike type, and the heel lift as kinematic parameters. A questionnaire revealed that subjects perceived the sensors as comfortable to wear and would use them on a regular basis. We concluded that wearable sensors provide a valuable tool for runners, from beginners to experts, for running technique assessment.