Due to a lack of transparency in both algorithm and validation methodology, it is diffcult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (+/- 10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.
It is unknown how movement patterns that are learned carry over to the field. The objective was to deter- mine whether training during a jump-landing task would transfer to lower extremity kinematics and kinetics during sidestep cutting.Methods Forty healthy athletes were assigned to the ver- bal internal focus (IF, n = 10), verbal external focus (EF, n = 10), video (VI, n = 10) or control (CTRL, n = 10) group. A jump-landing task was performed as baseline followed by training blocks (TR1 and TR2) and a post-test. Group-spe- cific instructions were given in TR1 and TR2. In addition, participants in the IF, EF and VI groups were free to ask for feedback after every jump during TR1 and TR2. Retention was tested after 1 week. Transfer of learned skill was deter- mined by having participants perform a 45° unanticipated sidestep cutting task. 3D hip, knee and ankle kinematics and kinetics were the main outcome measures.Results During sidestep cutting, the VI group showed greater hip flexion ROM compared to the EF and IF groups (p < 0.001). The EF (p < 0.036) and VI (p < 0.004) groups had greater knee flexion ROM compared to the IF group. Conclusions Improved jump-landing technique car- ried over to sidestep cutting when stimulating an external attentional focus combined with self-controlled feedback. Transfer to more sport-specific skills may demonstrate potential to reduce injuries on the field. Clinicians and practitioners are encouraged to apply instructions that stimulate an external focus of attention, of which visual instructions seem to be very powerful.
Background: In team handball an anterior cruciate ligament (ACL) injury often occurs during landing after a jump shot. Many intervention programs try to reduce the injury rate by instructing the athletes to land safer. Video feedback is an effective way to provide feedback although little is known about its influence on landing technique in sport-specific situations. Objective: To test the effectiveness of a video overlay feedback method on landing technique in elite handball players. Method: Sixteen elite female handball players were assigned to a Control or Video Group. Both groups performed jump shots in a pre-test, two training sessions (TR1 & TR2) and a post-test. The Video Group received video feedback of an expert model with an overlay of their own jump shots in TR1 and TR2 whilst the Control Group did not. Main outcome measures were sagittal ankle, knee and hip angles during initial contact (IC), maximum (MAX) and range of motion (ROM), in addition to the Landing Error Scoring System (LESS) score. One 2x4 repeated measures ANOVA was conducted to analyze group, time and interaction effects of all kinematic outcome measures and the LESS score. Results: The Video Group displayed significant improvement in knee and hip flexion at IC, MAX and ROM. In addition, MAX ankle flexion and their LESS score improved an average of 8.1 in the pre-test to 4.0 in the post-test. When considering performance variables, no differences between Control Group and Video Group were found in shot accuracy or vertical jump height, whilst horizontal jump distance in the Video Group became greater over time. Conclusion: Overlay visual feedback is an effective method to improve landing kinematics during a sport-specific jump shot. Further research is now warranted to determine the long-term effects and transfer to training and game situations.
