New Considerations for Wearable Technology Data: Changes in Running Biomechanics During a Marathon.

The purpose of this study was to use wearable technology data to quantify alterations in subject-specific running patterns throughout a marathon race and to determine if runners could be clustered into subgroups based on similar trends in running gait alterations throughout the marathon. Using a wearable sensor, data were collected for cadence, braking, bounce, pelvic rotation, pelvic drop, and ground contact time for 27 runners. A composite index was calculated based on the "typical" data (4-14 km) for each runner and evaluated for 14 individual 2-km sections thereafter to detect "atypical" data (ie, higher indices). A cluster analysis assigned all runners to a subgroup based on similar trends in running alterations. Results indicated that the indices became significantly higher starting at 20 to 22 km. Cluster 1 exhibited lower indices than cluster 2 throughout the marathon, and the only significant difference in characteristics between clusters was that cluster 1 had a lower age-grade performance score than cluster 2. In summary, this study presented a novel method to investigate the effects of fatigue on running biomechanics using wearable technology in a real-world setting. Recreational runners with higher age-grade performance scores had less atypical running patterns throughout the marathon compared with runners with lower age-grade performance scores.

Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis.

BACKGROUND: Muscle strengthening exercises consistently demonstrate improvements in the pain and function of adults with knee osteoarthritis, but individual response rates can vary greatly. Identifying individuals who are more likely to respond is important in developing more efficient rehabilitation programs for knee osteoarthritis. Therefore, the purpose of this study was to determine if pre-intervention multi-sensor accelerometer data (e.g., back, thigh, shank, foot accelerometers) and patient reported outcome measures (e.g., pain, symptoms, function, quality of life) can retrospectively predict post-intervention response to a 6-week hip strengthening exercise intervention in a knee OA cohort. METHODS: Thirty-nine adults with knee osteoarthritis completed a 6-week hip strengthening exercise intervention and were sub-grouped as Non-Responders, Low-Responders, or High-Responders following the intervention based on their change in patient reported outcome measures. Pre-intervention multi-sensor accelerometer data recorded at the back, thigh, shank, and foot and Knee Injury and Osteoarthritis Outcome Score subscale data were used as potential predictors of response in a discriminant analysis of principal components. RESULTS: The thigh was the single best placement for classifying responder sub-groups (74.4%). Overall, the best combination of sensors was the back, thigh, and shank (81.7%), but a simplified two sensor solution using the back and thigh was not significantly different (80.0%; p = 0.27). CONCLUSIONS: While three sensors were best able to identify responders, a simplified two sensor array at the back and thigh may be the most ideal configuration to provide clinicians with an efficient and relatively unobtrusive way to use to optimize treatment.

Predicting ground contact events for a continuum of gait types: An application of targeted machine learning using principal component analysis.

An ongoing challenge in the application of gait analysis to clinical settings is the standardized detection of temporal events, with unobtrusive and cost-effective equipment, for a wide range of gait types. The purpose of the current study was to investigate a targeted machine learning approach for the prediction of timing for foot strike (or initial contact) and toe-off, using only kinematics for walking, forefoot running, and heel-toe running. Data were categorized by gait type and split into a training set (∼30%) and a validation set (∼70%). A principal component analysis was performed, and separate linear models were trained and validated for foot strike and toe-off, using ground reaction force data as a gold-standard for event timing. Results indicate the model predicted both foot strike and toe-off timing to within 20ms of the gold-standard for more than 95% of cases in walking and running gaits. The machine learning approach continues to provide robust timing predictions for clinical use, and may offer a flexible methodology to handle new events and gait types.

Gender differences in gait kinematics for patients with knee osteoarthritis.

BACKGROUND: Females have a two-fold risk of developing knee osteoarthritis (OA) as compared to their male counterparts and atypical walking gait biomechanics are also considered a factor in the aetiology of knee OA. However, few studies have investigated sex-related differences in walking mechanics for patients with knee OA and of those, conflicting results have been reported. Therefore, this study was designed to examine the differences in gait kinematics (1) between male and female subjects with and without knee OA and (2) between healthy gender-matched subjects as compared with their OA counterparts. METHODS: One hundred subjects with knee OA (45 males and 55 females) and 43 healthy subjects (18 males and 25 females) participated in this study. Three-dimensional kinematic data were collected during treadmill-walking and analysed using (1) a traditional approach based on discrete variables and (2) a machine learning approach based on principal component analysis (PCA) and support vector machine (SVM) using waveform data. RESULTS: OA and healthy females exhibited significantly greater knee abduction and hip adduction angles compared to their male counterparts. No significant differences were found in any discrete gait kinematic variable between OA and healthy subjects in either the male or female group. Using PCA and SVM approaches, classification accuracies of 98-100% were found between gender groups as well as between OA groups. CONCLUSIONS: These results suggest that care should be taken to account for gender when investigating the biomechanical aetiology of knee OA and that gender-specific analysis and rehabilitation protocols should be developed.

Effects of Simulated Marker Placement Deviations on Running Kinematics and Evaluation of a Morphometric-Based Placement Feedback Method.

In order to provide effective test-retest and pooling of information from clinical gait analyses, it is critical to ensure that the data produced are as reliable as possible. Furthermore, it has been shown that anatomical marker placement is the largest source of inter-examiner variance in gait analyses. However, the effects of specific, known deviations in marker placement on calculated kinematic variables are unclear, and there is currently no mechanism to provide location-based feedback regarding placement consistency. The current study addresses these disparities by: applying a simulation of marker placement deviations to a large (n = 411) database of runners; evaluating a recently published method of morphometric-based deviation detection; and pilot-testing a system of location-based feedback for marker placements. Anatomical markers from a standing neutral trial were moved virtually by up to 30 mm to simulate deviations. Kinematic variables during running were then calculated using the original, and altered static trials. Results indicate that transverse plane angles at the knee and ankle are most sensitive to deviations in marker placement (7.59 degrees of change for every 10 mm of marker error), followed by frontal plane knee angles (5.17 degrees for every 10 mm). Evaluation of the deviation detection method demonstrated accuracies of up to 82% in classifying placements as deviant. Finally, pilot testing of a new methodology for providing location-based feedback demonstrated reductions of up to 80% in the deviation of outcome kinematics.

A comparison of different over-the-counter foot orthotic devices on multi-segment foot biomechanics.

BACKGROUND: Over-the-counter foot orthoses are a cost-effective alternative to custom-made devices. However, few studies have compared over-the-counter devices and most biomechanical research involving orthoses has focused on rearfoot biomechanics. OBJECTIVES: To determine changes in multi-segment foot biomechanics during shod walking in three commercially available over-the-counter devices: SOLE, SuperFeet and Powerstep when compared to no orthotic. STUDY DESIGN: Repeated measures, cross-sectional study. METHODS: Retroreflective markers were placed on the right limb of 18 participants representing forefoot, midfoot, rearfoot and shank segments. Three-dimensional kinematics were recorded using an eight-camera motion capture system while participants walked on a treadmill and the order of condition was randomized between four conditions: SOLE, SuperFeet, Powerstep and no orthotic. RESULTS: All over-the-counter devices exhibited significant decreases in plantar fascia strain compared to no orthotic and only Powerstep exhibited significant decreases in peak rearfoot eversion. Medial longitudinal arch deformation was not reduced for any over-the-counter device. CONCLUSION: Different over-the-counter devices exhibited specific alterations in rearfoot kinematics and all reduced plantar fascia strain by varying amounts. These over-the-counter-specific kinematic changes should be taken into consideration when recommending these devices as a treatment option. CLINICAL RELEVANCE: Over-the-counter orthoses are a cost-effective alternative to custom-made devices. We demonstrated that three commonly used over-the-counter devices influence foot kinematics and plantar fascia strain differently. Clinicians can use these results to provide more tailored treatment options for their patients.

Kinematic gait patterns in healthy runners: A hierarchical cluster analysis.

Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (P<0.001), independent of age, height, weight, and running speed. When these two groups were compared to PFP runners, one cluster exhibited greater while the other exhibited reduced peak knee abduction angles (P<0.05). The variability observed in running patterns across this sample could be the result of different gait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners.

Gait Biomechanics and Patient-Reported Function as Predictors of Response to a Hip Strengthening Exercise Intervention in Patients with Knee Osteoarthritis.

OBJECTIVE: Muscle strengthening exercises have been shown to improve pain and function in adults with mild-to-moderate knee osteoarthritis, but individual response rates can vary greatly. Predicting individuals who respond and those who do not is important in developing a more efficient and effective model of care for knee osteoarthritis (OA). Therefore, the purpose of this study was to use pre-intervention gait kinematics and patient-reported outcome measures to predict post-intervention response to a 6-week hip strengthening exercise intervention in patients with mild-to-moderate knee OA. METHODS: Thirty-nine patients with mild-to-moderate knee osteoarthritis completed a 6-week hip-strengthening program and were subgrouped as Non-Responders, Low-Responders, or High-Responders following the intervention based on their change in Knee injury Osteoarthritis Outcome Score (KOOS). Predictors of responder subgroups were retrospectively determined from baseline patient-reported outcome measures and kinematic gait parameters in a discriminant analysis of principal components. A 3-4 year follow-up on 16 of the patients with knee OA was also done to examine long-term changes in these parameters. RESULTS: A unique combination of patient-reported outcome measures and kinematic factors was able to successfully subgroup patients with knee osteoarthritis with a cross-validated classification accuracy of 85.4%. Lower patient-reported function in daily living (ADL) scores and hip frontal plane kinematics during the loading response were most important in classifying High-Responders from other sub-groups, while a combination of hip, knee, ankle kinematics were used to classify Non-Responders from Low-Responders. CONCLUSION: Patient-reported outcome measures and objective biomechanical gait data can be an effective method of predicting individual treatment success to an exercise intervention. Measuring gait kinematics, along with patient-reported outcome measures in a clinical setting can be useful in helping make evidence-based decisions regarding optimal treatment for patients with knee OA.

Do intermediate- and higher-order principal components contain useful information to detect subtle changes in lower extremity biomechanics during running?

Recently, a principal component analysis (PCA) approach has been used to provide insight into running pathomechanics. However, researchers often account for nearly all of the variance from the original data using only the first few, or lower-order principal components (PCs), which are often associated with the most dominant movement patterns. In contrast, intermediate- and higher-order PCs are generally associated with subtle movement patterns and may contain valuable information about between-group variation and specific test conditions. Few investigations have evaluated the utility of intermediate- and higher-order PCs based on observational cross-sectional analyses of different cohorts, and no prior studies have evaluated longitudinal changes in an intervention study. This study was designed to test the utility of intermediate- and higher-order PCs in identifying differences in running patterns between different groups based on three-dimensional bilateral lower-limb kinematics. The results reveal that differences between sex- and age-groups of 128 runners were observed in the lower- and intermediate-order PCs scores (p<0.05) while differences between baseline and following a 6-week muscle strengthening program for 24 runners with patellofemoral pain were observed in the higher-order PCs scores (p<0.05), which exhibited a moderate correlation with self-reported pain scores (r=-0.43; p<0.05).

A novel method to evaluate error in anatomical marker placement using a modified generalized Procrustes analysis.

As biomechanical research evolves, a continuing challenge is the standardization of data collection and analysis techniques. In gait analysis, placement of markers to construct an anatomical model has been identified as the single greatest source of error; however, there is currently no standardized approach to quantifying these errors. The current study applies morphometric methods, including a generalized Procrustes analysis (GPA) and a nearest neighbour comparison to quantify discrepancies in marker placement, with the goal of improving reliability in gait analysis. An extensive data-set collected by an Expert (n = 340) was used to evaluate marker placements performed by a Novice (n = 55). Variances identified through principal component analysis were used to create a modified GPA to transform anatomical data, and scaled coordinates from the Novice data-set were then scored against the Expert subset. The results showed quantitative differences in marker placement, suggesting that, although training improved consistency, systematic biases remained.

Gender and age-related differences in bilateral lower extremity mechanics during treadmill running.

Female runners have a two-fold risk of sustaining certain running-related injuries as compared to their male counterparts. Thus, a comprehensive understanding of the sex-related differences in running kinematics is necessary. However, previous studies have either used discrete time point variables and inferential statistics and/or relatively small subject numbers. Therefore, the first purpose of this study was to use a principal component analysis (PCA) method along with a support vector machine (SVM) classifier to examine the differences in running gait kinematics between female and male runners across a large sample of the running population as well as between two age-specific sub-groups. Bilateral 3-dimensional lower extremity gait kinematic data were collected during treadmill running. Data were analysed on the complete sample (n = 483: female 263, male 220), a younger subject group (n = 56), and an older subject group (n = 51). The PC scores were first sorted by the percentage of variance explained and we also employed a novel approach wherein PCs were sorted based on between-gender statistical effect sizes. An SVM was used to determine if the sex and age conditions were separable and classifiable based on the PCA. Forty PCs explained 84.74% of the variance in the data and an SVM classification accuracy of 86.34% was found between female and male runners. Classification accuracies between genders for younger subjects were higher than a subgroup of older runners. The observed interactions between age and gender suggest these factors must be considered together when trying to create homogenous sub-groups for research purposes.

Predicting timing of foot strike during running, independent of striking technique, using principal component analysis of joint angles.

As 3-dimensional (3D) motion-capture for clinical gait analysis continues to evolve, new methods must be developed to improve the detection of gait cycle events based on kinematic data. Recently, the application of principal component analysis (PCA) to gait data has shown promise in detecting important biomechanical features. Therefore, the purpose of this study was to define a new foot strike detection method for a continuum of striking techniques, by applying PCA to joint angle waveforms. In accordance with Newtonian mechanics, it was hypothesized that transient features in the sagittal-plane accelerations of the lower extremity would be linked with the impulsive application of force to the foot at foot strike. Kinematic and kinetic data from treadmill running were selected for 154 subjects, from a database of gait biomechanics. Ankle, knee and hip sagittal plane angular acceleration kinematic curves were chained together to form a row input to a PCA matrix. A linear polynomial was calculated based on PCA scores, and a 10-fold cross-validation was performed to evaluate prediction accuracy against gold-standard foot strike as determined by a 10 N rise in the vertical ground reaction force. Results show 89-94% of all predicted foot strikes were within 4 frames (20 ms) of the gold standard with the largest error being 28 ms. It is concluded that this new foot strike detection is an improvement on existing methods and can be applied regardless of whether the runner exhibits a rearfoot, midfoot, or forefoot strike pattern.

Classification accuracy of a single tri-axial accelerometer for training background and experience level in runners.

Accelerometers are increasingly used tools for gait analysis, but there remains a lack of research on their application to running and their ability to classify running patterns. The purpose of this study was to conduct an exploratory examination into the capability of a tri-axial accelerometer to classify runners of different training backgrounds and experience levels, according to their 3-dimensional (3D) accelerometer data patterns. Training background was examined with 14 competitive soccer players and 12 experienced marathon runners, and experience level was examined with 16 first-time and the same 12 experienced marathon runners. Discrete variables were extracted from 3D accelerations during a short run using root mean square, wavelet transformation, and autocorrelation procedures. A principal component analysis (PCA) was conducted on all variables, including gait speed to account for covariance. Eight PCs were retained, explaining 88% of the variance in the data. A stepwise discriminant analysis of PCs was used to determine the binary classification accuracy for training background and experience level, with and without the PC of Speed. With Speed, the accelerometer correctly classified 96% of runners for both training background and experience level. Without Speed, the accelerometer correctly classified 85% of runners based on training background, but only 68% based on experience level. These findings suggest that the accelerometer is effective in classifying athletes of different training backgrounds, but is less effective for classifying runners of different experience levels where gait speed is the primary discriminator.

Between-limb kinematic asymmetry during gait in unilateral and bilateral mild to moderate knee osteoarthritis.

OBJECTIVE: To compare lower-limb kinematic asymmetries during gait in individuals with unilateral and bilateral symptomatic osteoarthritis and controls. DESIGN: Cross-sectional. SETTING: Laboratory. PARTICIPANTS: Participants (N=54) had symptomatic unilateral (n=18) or bilateral (n=18) knee osteoarthritis. Healthy controls were sex- and age-matched and similar in height and weight to osteoarthritis groups (n=18). INTERVENTION: Three-dimensional motion analysis was conducted while participants walked on a treadmill at 1.1m/s. MAIN OUTCOME MEASURES: Maximum joint angles and velocities of the knee and hip during stance, knee flexion, knee adduction, and hip adduction at initial contact, pelvic drop, stride length, and average toe out. RESULTS: There was a significant limb effect for knee flexion at initial contact (P=.01). The bilateral osteoarthritis group demonstrated the largest between-limb asymmetry (2.83°; 95% confidence interval, .88-4.78; effect size [ES]=.67). The bilateral osteoarthritis group also displayed tendencies toward between-limb asymmetry in hip adduction at initial contact and peak knee adduction during stance; ESs were small (ES=.33 and .48). Lower-limb kinematics was symmetrical in the control and unilateral knee osteoarthritis groups. CONCLUSIONS: Between-limb asymmetries are present even at mild to moderate stages of knee osteoarthritis. During this stage, between-limb asymmetry appears to be more prevalent in patients with bilateral symptomatic disease, suggesting that patients with unilateral disease maintain kinematic symmetry for longer in the knee osteoarthritis process. Further, early treatment strategies should target the restoration of gait symmetry and involve kinematics changes in both lower limbs. Future research is needed to determine the efficacy of such strategies with respect to kinematic asymmetry, pain, and disease progression.