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14:40   Use of IMU's in motion analysis II Chair: Lloyd Smith 14:40 20 mins A SKILL ACQUISITION BASED FRAMEWORK FOR AIDING LOWER LIMB INJURY REHABILITATION USING A SINGLE INERTIAL SENSOR WITH CONCURRENT VISUAL FEEDBACK Jonathan Shepherd, Daniel James Abstract: Technology to aid the acquisition and performance of motor skills is becoming increasingly commonplace however there is distinct disconnect between these technological interventions and a detailed understanding of how to design technology to best instruct a learner. Using a single inertial sensor with bespoke concurrent visual feedback, based in a MATLAB data visualisation environment, this paper presents a skill acquisition framework to facilitate at home based physiotherapy interventions. When athletes and patients are prescribed at home based physiotherapy interventions the current literature reports low rates of adherence and there are questions on the quality of the rehabilitation program as currently methods for activity tracking, classification and reporting are severely lacking. A trial was conducted randomly assigning twenty uninjured participants to two categories, one with the aid of the rehabilitation software and the other a control group with no feedback. Both groups received the same visual instructions on the three simple leg static stretching tasks that are indicative of lower limb injury physiotherapy interventions. The results showed statically significant improvements in both the program adherence as well as the error mitigation of the feedback group in comparison to the control. Substantiating the skill acquisition framework errors for the feedback group seemed to lessen over time synonymous with an immediate learning effect as a result of the concurrent feedback. The findings suggest that at home physiotherapy interventions could be enhanced by using a concurrent biofeedback skill acquisition based single inertial sensor system. Evidently improving adherence, technique and allowing for the data to be accrued over time and relayed to practitioners and coaching staff ultimately giving them heightened confidence in monitoring physiotherapy progress. The wider implications means this research could be useful in tracking and providing feedback for a range of sports injury circumstances to ultimately improve the outcomes of the physiotherapy interventions. 15:00 20 mins EXPERIMENTAL VALIDATION OF THE TYNDALL PORTABLE LOWER-LIMB ANALYSIS SYSTEM WITH WEARABLE INERTIAL SENSORS Salvatore Tedesco, Andrea Urru, Amanda Clifford, Brendan O'Flynn Abstract: Biomechanics analysis in sport practice is extremely important to assess motion performance and assist athletes during rehabilitation (due to sport injuries, such as ACL reconstruction), and it is mostly done by camera-based motion analysis systems, which provide good results but presents serious drawbacks. Thus, small-size low-cost wearable sensors are an emerging tool for biomechanics monitoring. Aim of the present work is to implement a wireless portable easy-to-use system, with two sensors per leg, suitable for free-living environments and able to provide a complete biomechanics assessment (generated on a report) without the constraints of a laboratory. Validation for the lower-limbs using state-of-the-art camera-based motion capture is here presented. The parameters taken into account for a complete assessment are as follows: • Temporal events: toe-offs, heel-strikes, mid-stance; • Temporal intervals: gait cycle duration, stance phase, swing phase, single and double support, cadence (or step rate), number of cycles, swing symmetry; • Spatial parameters: stride length, stride velocity (or speed), peak angular velocity, shank clearance; • Knee range of motion. For each of those parameters, it is possible to calculate min, max, mean, median, standard deviation values and extrapolate the related variability. The system consists of two Tyndall Wireless Inertial Measurement Units (WIMUs) with 3D accelerometer/gyro (@ 250 Hz) and Wi-Fi/Bluetooth/SD cards. Algorithms are implemented in Matlab, and the scenarios considered (walking, drop and sit-to-stand) simulate a free-living environment and exercises performed in a rehabilitation procedure. The system has been tested with healthy and impaired subjects. The system shows good repeatability and accuracy for all scenarios, with an extremely significant correlation coefficient. Moreover, it is able to discriminate conditions with pathological and not-pathological characteristics. This work presents a wearable inertial system for the implementation of a complete portable wireless lower-limbs analysis system. Overall results present good repeatability and the accuracy is comparable with the state-of-the-art. The implemented system will prove its validity when demonstrating on-field athletes’ biomechanics assessment performance or their enhancements during rehabilitation. 15:20 20 mins IMU-BASED DETERMINATION OF STANCE DURATION DURING SPRINTING Marcus Schmidt, Carl Rheinländer, Kevin Nolte, Sebastian Wille, Norbert Wehn, Thomas Jaitner Abstract: Stride parameters like step length, step rate or stance duration during sprinting represent basic and very useful information for track and field coaches. Contact mats or opto-electrical systems like Optojump allow precise and unobtrusive measurements of theses parameters, but their use is limited in space. Inertial measurement units (IMUs) are not bound to these limitations, and therefore offer challenging opportunities for field diagnosis, especially if combined with wireless data transmission. IMUs have already been used to detect kinematic parameters in track and field (Bergamini et al., 2012), but typically data analysis could only be done offline. In this study, we present an IMU-based wearable measurement system for field-based performance analysis and online monitoring, that allows an accurate detection of stance parameters in sprinting. Twelve track and field athletes (10 male, 2 female) performed maximal sprints wearing the IMU system attached to both ankles. The OptojumpNext was used as reference system. Date acquisition rate for both devices was 1000Hz. Stance durations (tS) during the phase of maximal velocity were extracted onboard from the accelerometer and gyroscope signal and then sent to an external device. Out of 380 contacts, 365 were detected correctly (96%). tS showed a mean difference of -2.5±4.8ms between OptojumpNext and IMU. Bland-Altman plots derive a 95% limit of agreement (LOA) in the range from 6.8 to -11.8 ms for tS. Results show that the IMU device promises a reliable and accurate measurement of stance durations during sprinting. LOA indicate a higher accuracy in stance time detection compared to previous studies (Bergamini et al., 2012). Further research will focus on the optimization of algorithms to avoid miss detections and improve the parameter extraction. Additionally, an ubiquitous group monitoring is supported by the system because of a specific application which has been programmed to allow displaying the data on smartphones or tablets that are driven by the Android operating system. Therefore, the device can be used during training sessions with a group of athletes and provide objective real-time feedback. References Bergamini, E., Picerno, P., Pillet, H., Natta, F., Thoreux, P., Camomilla, V., 2012. Journal of Biomechanics, 45 (6), 1123-1126.