Background: The progression of medial knee osteoarthritis seems closely related to a high external knee adduction moment, which could be reduced through gait retraining. We aimed to determine the retraining strategy that reduces this knee moment most effective during gait, and to determine if the same strategy is the most effective for everyone. Methods: Thirty-seven healthy participants underwent 3D gait analysis. After normalwalking was recorded, participants received verbal instructions on four gait strategies (Trunk Lean, Medial Thrust, Reduced Vertical Acceleration, Toe Out). Knee adduction moment and strategy-specific kinematics were calculated for all conditions. Findings: The overall knee adduction moment peak was reduced by Medial Thrust (−0.08 Nm/Bw·Ht) and Trunk Lean (−0.07 Nm/Bw·Ht), while impulse was reduced by 0.03 Nms/Bw·Ht in both conditions. Toeing out reduced late stance peak and impulse significantly but overall peakwas not affected. Reducing vertical acceleration at initial contact did not reduce the overall peak. Strategy-specific kinematics (trunk lean angle, knee adduction angle, first peak of the vertical ground reaction force, foot progression angle) showed that multiple parameters were affected by all conditions. Medial Thrust was the most effective strategy in 43% of the participants, while Trunk Lean reduced external knee adduction moment most in 49%. With similar kinematics, the reduction of the knee adduction moment peak and impulse was significantly different between these groups. Interpretation: Although Trunk Lean and Medial Thrust reduced the external knee adduction moment overall, individual selection of gait retraining strategy seems vital to optimally reduce dynamic knee load during gait.
Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise real-time feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.
The huge number of images shared on the Web makes effective cataloguing methods for efficient storage and retrieval procedures specifically tailored on the end-user needs a very demanding and crucial issue. In this paper, we investigate the applicability of Automatic Image Annotation (AIA) for image tagging with a focus on the needs of database expansion for a news broadcasting company. First, we determine the feasibility of using AIA in such a context with the aim of minimizing an extensive retraining whenever a new tag needs to be incorporated in the tag set population. Then, an image annotation tool integrating a Convolutional Neural Network model (AlexNet) for feature extraction and a K-Nearest-Neighbours classifier for tag assignment to images is introduced and tested. The obtained performances are very promising addressing the proposed approach as valuable to tackle the problem of image tagging in the framework of a broadcasting company, whilst not yet optimal for integration in the business process.
