Clinical indications and protocol considerations for selecting initial body weight support levels in gait rehabilitation: a systematic review.

BACKGROUND: Body weight support (BWS) training devices are frequently used to improve gait in individuals with neurological impairments, but guidance in selecting an appropriate level of BWS is limited. Here, we aim to describe the initial BWS levels used during gait training, the rationale for this selection and the clinical goals aligned with BWS training for different diagnoses. METHOD: A systematic literature search was conducted in PubMed, Embase and Web of Science, including terms related to the population (individuals with neurological disorders), intervention (BWS training) and outcome (gait). Information on patient characteristics, type of BWS device, BWS level and training goals was extracted from the included articles. RESULTS: Thirty-three articles were included, which described outcomes using frame-based (stationary or mobile) and unidirectional ceiling-mounted devices on four diagnoses (multiple sclerosis (MS), spinal cord injury (SCI), stroke, traumatic brain injury (TBI)). The BWS levels were highest for individuals with MS (median: 75%, IQR: 6%), followed by SCI (median: 40%, IQR: 35%), stroke (median: 30%, IQR: 4.75%) and TBI (median: 15%, IQR: 0%). The included studies reported eleven different training goals. Reported BWS levels ranged between 30 and 75% for most of the training goals, without a clear relationship between BWS level, diagnosis, training goal and rationale for BWS selection. Training goals were achieved in all included studies. CONCLUSION: Initial BWS levels differ considerably between studies included in this review. The underlying rationale for these differences was not clearly motivated in the included studies. Variation in study designs and populations does not allow to draw a conclusion on the effectiveness of BWS levels. Hence, it remains difficult to formulate guidelines on optimal BWS settings for different diagnoses, BWS devices and training goals. Further efforts are required to establish clinical guidelines and to experimentally investigate which initial BWS levels are optimal for specific diagnoses and training goals.

Characterization of movement patterns using unsupervised learning neural networks: Exploring a novel approach for monitoring athletes during sidestepping.

The monitoring of athletes is crucial to preventing injuries, identifying fatigue or supporting return-to-play decisions. The purpose of this study was to explore the ability of Kohonen neural network self-organizing maps (SOM) to objectively characterize movement patterns during sidestepping and their association with injury risk. Further, the network's sensitivity to detect limb dominance was assessed. The data of 67 athletes with a total of 613 trials were included in this study. The 3D trajectories of 28 lower-body passive markers collected during sidestepping were used to train a SOM. The network consisted of 1247 neurons distributed over a 43 × 29 rectangular map with a hexagonal neighbourhood topology. Out of 61,913 input vectors, the SOM identified 1247 unique body postures. Visualizing the movement trajectories and adding several hidden variables allows for the investigation of different movement patterns and their association with joint loading. The used approach identified athletes that show significantly different movement strategies when sidestepping with their dominant or non-dominant leg, where one strategy was clearly associated with ACL-injury-relevant risk factors. The results highlight the ability of unsupervised machine learning to monitor an individual athlete's status without the necessity to reduce the complexity of the data describing the movement.

Control strategies during fast turning manoeuvers - the free moment and its contribution to body rotation and joint loading.

The purpose of this study was to identify the relationship between ACL relevant knee joint loading and the free (reaction) moment during 90° sidestepping task. It was hypothesized that the specific movement strategy of an athlete will impact this relationship and therefore contribute to joint loading. Functional principal component and canonical correlation analysis were used to understand the nature of free moments and their interaction with 3D joint loading in 52 athletes. It was observed that the orientation of either a positive or negative free moment is associated with different orientations and location of the foot segment at initial touch down. This impacted the rotational moment that is transferred to the knee joint: A higher internal reaction moment is observed when athletes were exposed to a positive free reaction moment, which potentially increases the load on the ACL. Furthermore, the free moment predicted joint moments and joint reaction forces. The interpretation of the principal components identified the function of the free moment to control body rotation. Free moments of different orientation were generated during the same movement, which highlights the importance of investigating individual movement strategies to understand potential injury risk and control factors.

Artificial Neural Networks in Motion Analysis-Applications of Unsupervised and Heuristic Feature Selection Techniques.

The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles. Additionally, the dimensions of the problem can be simplified using principal component analysis. Training a long short-term memory neural network on the prediction of 3D lower limb joint angles based on inertial data showed that three sensors placed on the pelvis and both shanks are sufficient. The application of principal component analysis to the data of five sensors did not reveal improved results. The use of longer motion sequences compared to time-normalised gait cycles seems to be advantageous for the prediction accuracy, which bridges the gap to real-time applications of long short-term memory neural networks in the future.

Musculoskeletal modelling of the dragonfly mandible system as an aid to understanding the role of single muscles in an evolutionary context.

Insects show a great variety of mouthpart and muscle configurations; however, knowledge of their mouthpart kinematics and muscle activation patterns is fragmentary. Understanding the role of muscle groups during movement and comparing them between insect groups could yield insights into evolutionary patterns and functional constraints. Here, we developed a mathematical inverse dynamic model including distinct muscles for an insect head-mandible-muscle complex based on micro-computed tomography (µCT) data and bite force measurements. With the advent of µCT, it is now possible to obtain precise spatial information about muscle attachment areas and head capsule construction in insects. Our model shows a distinct activation pattern for certain fibre groups potentially related to a geometry-dependent optimization. Muscle activation patterns suggest that intramandibular muscles play a minor role in bite force generation, which is a potential reason for their loss in several lineages of higher insects. Our model is in agreement with previous studies investigating fast and slow muscle fibres and is able to resolve the spatio-temporal activation patterns of these different muscle types in insects. The model used here has a high potential for large-scale comparative analyses on the role of different muscle setups and head capsule designs in the megadiverse insects in order to aid our understanding of insect head capsule and mouthpart evolution under mechanical constraints.

Surgical treatment for achilles tendinopathy - a systematic review.

BACKGROUND: The purpose of this systematic review is to analyse the results of operative treatment for midportion Achilles tendinopathy and to provide evidence based recommendation for the indication of the individual published techniques. METHODS: MEDLINE, Cochrane Database, ISI Web of Knowledge and Google databases (1945 till September 2014) were electronically searched. The quality of the included articles was evaluated using the Coleman Methodology Score. Success rates, patient satisfaction, and the complication rates were determined. RESULTS: Twenty studies met our inclusion criteria. A total of 801 tendons were treated in 714 patients with open or minimally invasive techniques. The mean success rate was 83.4 %. Complications were reported in 6.3 % of the cases. The articles on minimally invasive techniques and open procedures reported on an average success rate of 83.6 % and 78.9 (p = 0.987). Patient satisfaction rates for minimally invasive techniques and open procedures were 78.5 % and 78.1 % (p = 0.211). The complication rate was 5.3 % for the minimally invasive techniques and 10.5 % for the open procedures (p = 0.053). CONCLUSION: We conclude that success rates of minimally invasive and open treatments are not different and that there is no difference in patient satisfaction but there is a tendency for more complications to occur in open procedures.

Exploring unsupervised feature extraction of IMU-based gait data in stroke rehabilitation using a variational autoencoder.

BACKGROUND: Variational AutoEncoders (VAE) might be utilized to extract relevant information from an IMU-based gait measurement by reducing the sensor data to a low-dimensional representation. The present study explored whether VAEs can reduce IMU-based gait data of people after stroke into a few latent features with minimal reconstruction error. Additionally, we evaluated the psychometric properties of the latent features in comparison to gait speed, by assessing 1) their reliability; 2) the difference in scores between people after stroke and healthy controls; and 3) their responsiveness during rehabilitation. METHODS: We collected test-retest and longitudinal two-minute walk-test data using an IMU from people after stroke in clinical rehabilitation, as well as from a healthy control group. IMU data were segmented into 5-second epochs, which were reduced to 12 latent-feature scores using a VAE. The between-day test-retest reliability of the latent features was assessed using ICC-scores. The differences between the healthy and the stroke group were examined using an independent t-test. Lastly, the responsiveness was determined as the number of individuals who significantly changed during rehabilitation. RESULTS: In total, 15,381 epochs from 107 people after stroke and 37 healthy controls were collected. The VAE achieved data reconstruction with minimal errors. Five latent features demonstrated good-to-excellent test-retest reliability. Seven latent features were significantly different between groups. We observed changes during rehabilitation for 21 and 20 individuals in latent-feature scores and gait speed, respectively. However, the direction of the change scores of the latent features was ambiguous. Only eleven individuals exhibited changes in both latent-feature scores and gait speed. CONCLUSION: VAEs can be used to effectively reduce IMU-based gait assessment to a concise set of latent features. Some latent features had a high test-retest reliability and differed significantly between healthy controls and people after stroke. Further research is needed to determine their clinical applicability.

Implications of the choice of distance-based measures in assessing and investigating tumble turn performance.

Although the tumble turn in swimming has been studied extensively, no consensus exists about which measure is best suited to capture its performance. The aim of this study was to better understand the implications of choosing a particular distance-based performance measure for assessing and investigating tumble turn performance in freestyle swimming. To this end, a large set of retrospective turn data consisting of 2,813 turns performed by 160 swimmers was analyzed statistically in three steps. First, a mixed-effects model was derived for the entire data set, which showed that both performance level and sex had clear effects on the distance-based performance measures and performance determining variables studied in the literature. Second, repeated measures correlations were calculated for the entire data set and four performance level- and sex-based subgroups to determine the level of association between the performance measures. This analysis revealed that the performance measures were strongly correlated (r > 0.84 and p < 0.05 for all possible pairs), largely independent of performance level and sex. This finding implies that the choice of performance measure is not very critical when one is interested solely in the overall performance. In the third and last step, mixed-effects models were derived for the performance measures of interest to establish the importance of different turn-related actions for each measure, again for both the entire data set and the four subgroups separately. The results of this analysis revealed that performance measures with short(er) distances are more sensitive to changes in the adaptation time and reflect the wall contact time better than performance measures with long(er) distances, which in contrast are more useful if the focus is on the approach speed prior to the turn. In this final analysis, various effects of performance level and sex were found on the technical execution of the tumble turn.

Breaking new grounds in injury risk screening in soccer by deploying unsupervised learning with a special focus on sex and fatigue effects.

In injury prevention, a vertical drop jump (DJ) is often used for screening athletes at risk for injury; however, the large variation in individual movement patterns might mask potentially relevant strategies when analysed on a group-based level. Two movement strategies are commonly discussed as predisposing athletes to ACL injuries: a deficient leg axis and increased leg stiffness during landing. This study investigated the individual movement pattern of 39 female and male competitive soccer players performing DJs at rest and after being fatigued. The joint angles were used to train a Kohonen self-organising map. Out of 19,596 input vectors, the SOM identified 700 unique postures. Visualising the movement trajectories and adding the latent parameters contact time, medial knee displacement (MKD) and knee abduction moment allow identification of zones with presumably increased injury risk and whether the individual movement patterns pass these zones. This information can be used, e.g., for individual screening and for feedback purposes. Additionally, an athlete's reaction to fatigue can be explored by comparing the rested and fatigued movement trajectories. The results highlight the ability of unsupervised learning to visualise movement patterns and to give further insight into an individual athlete's status without the necessity of a priori assumptions.

Improving tumble turn performance in swimming-the impact of wall contact time and tuck index.

Race time can be shortened by improving turn performance in competitive swimming, but this requires insight into the optimal turn technique. The aim of the present study was to examine the effect of Wall Contact Time (WCT) and Tuck Index on tumble turn performance and their interrelations by experimentally manipulating both variables, which has not been done in previous research. Eighteen Dutch national level swimmers (FINA points 552 ± 122) performed tumble turns with three different WCTs (shorter, preferred, longer) and three different Tuck Indices (higher, preferred, lower), which were recorded by four underwater cameras and a wall-mounted force plate. Linear kinematic and kinetic variables, including the approach velocity (Vin), wall adaptation time (Tadapt), percentage of active WCT (aWCT), peak push-off force (FPeak) and exit velocity (Vexit), were extracted from the recordings and analyzed statistically, using the 5 m round trip time (5mRTT) as performance measure. The results indicated that the WCT should be sufficiently long to generate a high push-off force at the end of wall contact when the body is in a streamlined position. This led to a significantly shorter 5mRTT than a shorter or longer WCT. A linear mixed effect model yielded negative significant effects of WCT (-4.22, p < 0.001), FPeak (-2.18, p = 0.04), Vin (-4.83, p = 0.02), Tadapt (-2.68, p = 0.002), and Vexit (-9.52, p < 0.001) on the 5mRTT. The best overall turning performance was achieved with a Tuck Index of 0.7, which suggests that some of the participating swimmers could benefit from adapting their distance to the wall while turning, as was exemplified by calculating the optimal Tuck Index for individual swimmers. These results underscore the importance of WCT and Tuck Index vis-à-vis tumble turn performance, as well as their interrelations with other performance determining variables in this regard.

Fatigue Induced by Repeated Changes of Direction in Élite Female Football (Soccer) Players: Impact on Lower Limb Biomechanics and Implications for ACL Injury Prevention.

BACKGROUND: The etiology of Anterior Cruciate Ligament (ACL) injury in women football results from the interaction of several extrinsic and intrinsic risk factors. Extrinsic factors change dynamically, also due to fatigue. However, existing biomechanical findings concerning the impact of fatigue on the risk of ACL injuries remains inconsistent. We hypothesized that fatigue induced by acute workload in short and intense game periods, might in either of two ways: by pushing lower limbs mechanics toward a pattern close to injury mechanism, or alternatively by inducing opposed protective compensatory adjustments. AIM: In this study, we aimed at assessing the extent to which fatigue impact on joints kinematics and kinetics while performing repeated changes of direction (CoDs) in the light of the ACL risk factors. METHODS: This was an observational, cross-sectional associative study. Twenty female players (age: 20-31 years, 1st-2nd Italian division) performed a continuous shuttle run test (5-m) involving repeated 180°-CoDs until exhaustion. During the whole test, 3D kinematics and ground reaction forces were used to compute lower limb joints angles and internal moments. Measures of exercise internal load were: peak post-exercise blood lactate concentration, heart rate (HR) and perceived exertion. Continuous linear correlations between kinematics/kinetics waveforms (during the ground contact phase of the pivoting limb) and the number of consecutive CoD were computed during the exercise using a Statistical Parametric Mapping (SPM) approach. RESULTS: The test lasted 153 ± 72 s, with a rate of 14 ± 2 CoDs/min. Participants reached 95% of maximum HR and a peak lactate concentration of 11.2 ± 2.8 mmol/L. Exercise duration was inversely related to lactate concentration (r = -0.517, p < 0.01), while neither%HR max nor [La-] b nor RPE were correlated with test duration before exhaustion (p > 0.05). Alterations in lower limb kinematics were found in 100%, and in lower limb kinetics in 85% of the players. The most common kinematic pattern was a concurrent progressive reduction in hip and knee flexion angle at initial contact (10 players); 5 of them also showed a significantly more adducted hip. Knee extension moment decreased in 8, knee valgus moment increased in 5 players. A subset of participants showed a drift of pivoting limb kinematics that matches the known ACL injury mechanism; other players displayed less definite or even opposed behaviors. DISCUSSION: Players exhibited different strategies to cope with repeated CoDs, ranging from protective to potentially dangerous behaviors. While the latter was not a univocal effect, it reinforces the importance of individual biomechanical assessment when coping with fatigue.

Prediction of ground reaction force and joint moments based on optical motion capture data during gait.

The standard camera- and force plate-based set-up for motion analysis suffers from the disadvantage of being limited to laboratory settings. Since adaptive algorithms are able to learn the connection between known inputs and outputs and generalise this knowledge to unknown data, these algorithms can be used to leverage motion analysis outside the laboratory. In most biomechanical applications, feedforward neural networks are used, although these networks can only work on time normalised data, while recurrent neural networks can be used for real time applications. Therefore, this study compares the performance of these two kinds of neural networks on the prediction of ground reaction force and joint moments of the lower limbs during gait based on joint angles determined by optical motion capture as input data. The accuracy of both networks when generalising to new data was assessed using the normalised root-mean-squared error, the root-mean-squared error and the correlation coefficient as evaluation metrics. Both neural networks demonstrated a high performance and good capabilities to generalise to new data. The mean prediction accuracy over all parameters applying a feedforward network was higher (r = 0.963) than using a recurrent long short-term memory network (r = 0.935).

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network.

Enhancement of activity is one major topic related to the aging society. Therefore, it is necessary to understand people's motion and identify possible risk factors during activity. Technology can be used to monitor motion patterns during daily life. Especially the use of artificial intelligence combined with wearable sensors can simplify measurement systems and might at some point replace the standard motion capturing using optical measurement technologies. Therefore, this study aims to analyze the estimation of 3D joint angles and joint moments of the lower limbs based on IMU data using a feedforward neural network. The dataset summarizes optical motion capture data of former studies and additional newly collected IMU data. Based on the optical data, the acceleration and angular rate of inertial sensors was simulated. The data was augmented by simulating different sensor positions and orientations. In this study, gait analysis was undertaken with 30 participants using a conventional motion capture set-up based on an optoelectronic system and force plates in parallel with a custom IMU system consisting of five sensors. A mean correlation coefficient of 0.85 for the joint angles and 0.95 for the joint moments was achieved. The RMSE for the joint angle prediction was smaller than 4.8° and the nRMSE for the joint moment prediction was below 13.0%. Especially in the sagittal motion plane good results could be achieved. As the measured dataset is rather small, data was synthesized to complement the measured data. The enlargement of the dataset improved the prediction of the joint angles. While size did not affect the joint moment prediction, the addition of noise to the dataset resulted in an improved prediction accuracy. This indicates that research on appropriate augmentation techniques for biomechanical data is useful to further improve machine learning applications.

Prediction of lower limb joint angles and moments during gait using artificial neural networks.

In recent years, gait analysis outside the laboratory attracts more and more attention in clinical applications as well as in life sciences. Wearable sensors such as inertial sensors show high potential in these applications. Unfortunately, they can only measure kinematic motions patterns indirectly and the outcome is currently jeopardized by measurement discrepancies compared with the gold standard of optical motion tracking. The aim of this study was to overcome the limitation of measurement discrepancies and the missing information on kinetic motion parameters using a machine learning application based on artificial neural networks. For this purpose, inertial sensor data-linear acceleration and angular rate-was simulated from a database of optical motion tracking data and used as input for a feedforward and long short-term memory neural network to predict the joint angles and moments of the lower limbs during gait. Both networks achieved mean correlation coefficients higher than 0.80 in the minor motion planes, and correlation coefficients higher than 0.98 in the sagittal plane. These results encourage further applications of artificial intelligence to support gait analysis. Graphical Abstract The graphical abstract displays the processing of the data: IMU data is used as input to a feedforward and a long short-term memory neural network to predict the joint kinematics and kinetics of the lower limbs during gait.

Does hamstrings co-contraction constrain knee internal rotation in patients with knee endoprosthesis during decline slope walking?

BACKGROUND: Medial and lateral hamstrings are known for their capacity to promote internal or external rotation of the knee. Apart from implant geometry, increased co-contraction to a larger share of either the medial or lateral hamstrings has the potential to contribute to the restricted knee internal rotation especially under consideration of cruciate ligament substituting compared to cruciate ligament retaining knee endoprosthesis designs. Hence, the purpose of the study was to evaluate, whether increased co-contraction of the hamstrings contribute to the impaired knee internal rotation in total and unicondylar knee arthroplasty patients during level and decline walking. METHODS: Knee joint angles were calculated using an inverse kinematics model in Anybody. Muscle activity was examined of the semitendinosus and biceps femoris. FINDINGS: Knee internal rotation was constraint in the operated compared to the non-operated limb only in the total knee arthroplasty group during decline slope walking. Co-contraction values revealed no statistically significant differences between the operated and non-operated limb during the limited knee internal rotation period of time (59-94% of stance). Biceps femoris activity was significantly reduced (69-71% of stance) in the operated limb in the total knee arthroplasty group during decline slope walking. INTERPRETATION: Contrary to the proposed mechanism, aspects other than co-contraction between semitendinosus and biceps femoris are involved in the impaired transverse plane knee motion. These include implant congruency and probably friction. Unexpectedly, the biceps femoris did not compensate the absence of the anterior cruciate ligament with increased muscular activity in the operated limb of the total knee arthroplasty group.

Agreement of measured and calculated muscle activity during highly dynamic movements modelled with a spherical knee joint.

The inclusion of muscle forces into the analysis of joint contact forces has improved their accuracy. But it has not been validated if such force and activity calculations are valid during highly dynamic multidirectional movements. The purpose of this study was to validate calculated muscle activation of a lower extremity model with a spherical knee joint for running, sprinting and 90°-cutting. Kinematics, kinetics and lower limb muscle activation of ten participants were investigated in a 3D motion capture setup including EMG. A lower extremity rigid body model was used to calculate the activation of these muscles with an inverse dynamics approach and a cubic cost function. Correlation coefficients were calculated to compare measured and calculated activation. The results showed good correlation of the modelled and calculated data with a few exceptions. The highest average correlations were found during walking (r = 0.81) and the lowest during cutting (r = 0.57). Tibialis anterior had the lowest average correlation (r = 0.33) over all movements while gastrocnemius medius had the highest correlation (r = 0.9). The implementation of a spherical knee joint increased the agreement between measured and modelled activation compared to studies using a hinge joint knee. Although some stabilizing muscles showed low correlations during dynamic movements, the investigated model calculates muscle activity sufficiently.

Intelligent prediction of kinetic parameters during cutting manoeuvres.

Due to its capabilities in analysing injury risk, the ability to analyse an athlete's ground reaction force and joint moments is of high interest in sports biomechanics. However, using force plates for the kinetic measurements influences the athlete's performance. Therefore, this study aims to use a feed-forward neural network to predict hip, knee and ankle joint moments as well as the ground reaction force from kinematic data during the execution and depart contact of a maximum effort 90° cutting manoeuvre. A total number of 525 cutting manoeuvres performed by 55 athletes were used to train and test neural networks. Either marker trajectories or joint angles were used as input data. The correlation coefficient between the measured and predicted data indicated strong correlations. By using joint angles as the input parameters, slightly but not significantly higher accuracy was found in joint moments predictions. The prediction of the ground reaction force showed significantly higher accuracy when using marker trajectories. Hence, the proposed feed-forward neural network method can be used to predict motion kinetics during a fast change of direction. This may allow for the simplification of cutting manoeuvres experimental set-ups for and through the use of inertial sensors. Graphical abstract The left part of the graphical abstract displays the angle progression of the hip, knee and ankle joint as an example of the kinematic input data and is supported by a stick figure of the motion task, a 90° cutting manoeuvre. This data is used to train a feed-forward neural network, which is displayed in the middle. The neural network's output is displayed on the right. As an example of the kinetic data, the joint moments of hip, knee and ankle joint are displayed and supported by a stick figure.

Assessment of the measurement accuracy of inertial sensors during different tasks of daily living.

The low cost and ease of use of inertial measurement units (IMUs) make them an attractive option for motion analysis tasks that cannot be easily measured in a laboratory. To date, only a limited amount of research has been conducted comparing commercial IMU systems to optoelectronic systems, the gold standard, for everyday tasks like stair climbing and inclined walking. In this paper, the 3D joint angles of the lower limbs are determined using both an IMU system and an optoelectronic system for twelve participants during stair ascent and descent, and inclined, declined and level walking. Three different datasets based on different hardware and anatomical models were collected for the same movement in an effort to determine the cause and quantify the errors involved with the analysis. Firstly, to calculate software errors, two different anatomical models were compared for one hardware system. Secondly, to calculate hardware errors, results were compared between two different measurement systems using the same anatomical model. Finally, the overall error between both systems with their native anatomical models was calculated. Statistical analysis was performed using statistical parametric mapping. When both systems were evaluated based on the same anatomical model, the number of trials with significant differences decreased markedly. Thus, the differences in joint angle measurement can mainly be attributed to the variability in the anatomical models used for calculations and not to the IMU hardware.

Understanding cutting maneuvers - The mechanical consequence of preparatory strategies and foot strike pattern.

OBJECTIVES: This study investigated the relation of different previously reported preparatory strategies and musculo-skeletal loading during fast preplanned 90° cutting maneuvers (CM). The aim was to increase the understanding of the connection between whole body orientation, preparatory actions and the solution strategy to fulfil the requirements of a CM. METHODS: Three consecutive steps of anticipated 90° CMs were investigated in a 3D movement analysis setup. Pelvis orientation clustered the subjects in two groups, with minor and major pre-orientation. To understand the impact of body orientation on the specific movement strategy, joint angles, moments and energy as well as spatio-temporal parameters of the movement were analysed. RESULTS: Early rotation of the body was initiated by a small step width during braking resulting in a more constant path velocity of the centre of mass and less demands on the hip- and knee surrounding muscles. Minor pre-orientation required increased work of the hip muscles to decelerate, reaccelerate and in particular to rotate the body. This resulted in an increase of contact time. While pre-orientation in combination with fore-foot striking led to a strategy where energy absorption and generation is mainly generated by the ankle plantar flexors, less pre-orientation and rear-foot striking resulted in a knee- and hip dominant strategy. CONCLUSION: Step width before transition strongly determined pre-orientation and overall body position. Both strategies fulfil the requirements of a CM but induce different demands regarding muscular capacities. Pelvis orientation and step width are easy-to-use assessment parameters in the practical field.