Differences in Situational Patterns During Change of Direction Movements Greater than 90° in Youth Male and Female Soccer Players.

Change of direction (COD) maneuvers in soccer create tactical advantages, but also expose the player to an increased risk of injury. COD ability is commonly tested with pre-planned drills including cuts greater than 90°. These tests do not take into consideration positional differences players encounter during games. This case-series study used principal component analysis (PCA) to examine situational differences during COD movements between playing positions in youth soccer games. For each of the four teams included (26 females, 27 males), one game was analyzed using video-analysis. Two independent reviewers identified situational patterns and a PCA was used to examine differences between playing positions. Three principal components explained 89% of the variation in the data and were categorized as the total quantity of CODs, attacking/goal-scoring and defensive reacting types of CODs. One-way ANOVA on the individual principal component (PC) scores showed significant differences (p < 0.05) between centre midfielders, goalkeepers, and centrebacks in the quantity of CODs (PC1), and between wingers and fullbacks and centre backs in attacking/goal-scoring CODs (PC2), whereas PC3 was not different between playing positions. Differences between playing positions suggest that training and testing protocols in soccer could be enhanced to better match the individual and playing position-based needs.

Evaluating a Wearable Solution for Measuring Lower Extremity Asymmetry During Landing.

Purpose: Force plates can be used to monitor landing asymmetries during rehabilitation, but they are not widely available. Accelerometer-based wearable technology may be a more feasible solution. The purpose of this article was to determine the agreement between impact accelerations measured with force plates and accelerometer-derived measures of (1) centre of mass (COM) acceleration and (2) tibial acceleration asymmetries during bilateral landings. Method: Participants completed three countermovement jumps (CMJ) and three squat jumps (SJ) on dual force plates with triaxial accelerometers attached to each tibia and lower back, near the COM. Bland and Altman 95% limits of agreement (95% LOA) were calculated. Results: 19 adults (n = 11; 58% women, n = 8; 42% men) participated in the study. The mean differences between impact and COM accelerations were 0.24 g (95% LOA: -1.34 g to 1.82 g) and 0.38 g (95% LOA: -1.15 to 1.91 g) for the CMJ and SJ, respectively. The mean differences between the impact and tibial acceleration-based lower limb asymmetries in the CMJ and SJ were -6% (95% LOA: -32% to 19%) and 0% (95% LOA: -45% to 45%), respectively. Conclusions: Our findings show acceptable agreement between impact acceleration and accelerometer-based COM acceleration and lack of agreement between impact accelerations and accelerometer-based tibial acceleration asymmetries. COM acceleration could be used to quantify landing impacts during rehabilitation, but we do not consider the accelerometer-based asymmetry measures to be a suitable alternative for force plate-based measures. Future work should focus on determining normative values for lower extremity asymmetries during landing tasks.

Objectif: les plateformes de force peuvent être utilisées pour surveiller les asymétries à l'atterrissage pendant la réadaptation, mais elles ne sont pas largement accessibles. Le recours à la technologie portable sous forme d'accéléromètres serait peut-être plus réalisable. Le présent article visait à déterminer la concordance entre les mesures d'accélération à l'impact à l'aide des plateformes de force et à l'aide des accéléromètres pour connaître 1) l'accélération au centre de gravité (CdG) et 2) les asymétries d'accélération tibiale lors des atterrissages bilatéraux. Méthodologie: les participants ont effectué trois sauts avec contre-mouvement (SCM) et trois sauts groupés (SG) sur des plateformes de double force conjuguées à l'installation d'accéléromètres triaxiaux à chaque tibia et à la colonne lombaire, près du CdG. Les chercheurs ont calculé les limites de concordance à 95 % de Bland et Altman (LdC à 95 %). Résultats: au total, 19 adultes (n = 11; 58 % de femmes, n = 8; 42 % d'hommes) ont participé à l'étude. Les chercheurs ont établi que les principales différences entre l'accélération à l'impact et l'accélération au CdG étaient de 0,24 g (LdC à 95 %; −1,34 g à 1,82 g) et de 0,38 g (LdC à 95 %; −1,15 g à 1,91 g) lors des SCM et des SG, respectivement. Les différences moyennes entre l'impact et les asymétries des membres inférieurs à l'accélération tibiale lors des SCM et des SG correspondaient à −6 % (LdC à 95 %; −32 % à 19 %) et à 0 % (LdC à 95 %; −45 % à 45 %), respectivement. Conclusions: selon les observations des chercheurs, la concordance est acceptable entre l'accélération à l'impact et l'accélération au CdG à l'aide des accéléromètres, mais pas entre les accélérations à l'impact et les asymétries d'accélération tibiale à l'aide des accéléromètres. L'accélération au CdG pourrait être utilisée pour quantifier les impacts à l'atterrissage pendant la réadaptation, mais les chercheurs ne considèrent pas que les mesures d'asymétrie à l'aide des accéléromètres peuvent remplacer convenablement les mesures obtenues à l'aide des plateformes de force. Les futurs travaux devraient viser à déterminer les valeurs normatives des asymétries des membres inférieurs pendant des tâches d'atterrissage.

Who Does Not Respond to Injury Prevention Warm-up Programs? A Secondary Analysis of Trial Data From Neuromuscular Training Programs in Youth Basketball, Soccer, and Physical Education.

OBJECTIVES: To identify factors associated with nonresponse to neuromuscular training (NMT) warm-up programs among youth exposed to NMT warm-ups. METHODS: This is a secondary analysis of youth (aged 11-18 years) in the intervention groups of 4 randomized controlled trials in high school basketball, youth community soccer, and junior high school physical education. Youth who were exposed to NMT and who sustained an injury during the study were considered nonresponders. Odds ratios (ORs) were based on generalized estimating equations logistic regression controlling for clustering by team/class and adjusted for age, weight, height, balance performance, injury history, sex, and sport (soccer/basketball/physical education). RESULTS: A total of 1793 youth were included. Youth with a history of injury in the previous year had higher odds (OR = 1.64; 95% CI: 1.14, 2.37) of injury during the study, and females were more likely (OR = 1.67; 95% CI: 1.21, 2.31) to sustain an injury than males who were participating in NMT. Age was not associated with the odds of sustaining an injury (OR = 1.10; 95% CI: 0.93, 1.30). Soccer players benefited most from greater adherence, with 81% lower odds of injury (OR = 0.19; 95% CI: 0.06, 0.57) when completing 3 NMT sessions a week compared with 1 session per week. CONCLUSION: Factors associated with nonresponse to an NMT warm-up program were female sex, history of injury during the previous 12 months, and lower weekly NMT session adherence in some sports (soccer). J Orthop Sports Phys Ther 2023;53(2):94-102. Epub: 9 December 2022. doi:10.2519/jospt.2022.11526.

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis.

Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.

Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload.

Missing data can influence calculations of accumulated athlete workload. The objectives were to identify the best single imputation methods and examine workload trends using multiple imputation. External (jumps per hour) and internal (rating of perceived exertion; RPE) workload were recorded for 93 (45 females, 48 males) high school basketball players throughout a season. Recorded data were simulated as missing and imputed using ten imputation methods based on the context of the individual, team and session. Both single imputation and machine learning methods were used to impute the simulated missing data. The difference between the imputed data and the actual workload values was computed as root mean squared error (RMSE). A generalized estimating equation determined the effect of imputation method on RMSE. Multiple imputation of the original dataset, with all known and actual missing workload data, was used to examine trends in longitudinal workload data. Following multiple imputation, a Pearson correlation evaluated the longitudinal association between jump count and sRPE over the season. A single imputation method based on the specific context of the session for which data are missing (team mean) was only outperformed by methods that combine information about the session and the individual (machine learning models). There was a significant and strong association between jump count and sRPE in the original data and imputed datasets using multiple imputation. The amount and nature of the missing data should be considered when choosing a method for single imputation of workload data in youth basketball. Multiple imputation using several predictor variables in a regression model can be used for analyses where workload is accumulated across an entire season.

Magnitude, Frequency, and Accumulation: Workload Among Injured and Uninjured Youth Basketball Players.

Overuse injuries are common in basketball. Wearable technology enables the workload to be monitored in sport settings. However, workload-injury models lack a biological basis both in the metrics recorded and how workload is accumulated. We introduce a new metric for monitoring workload: weighted jump height, where each jump height is weighted to represent the expected effect of the jump magnitude on damage to the tendon. The objectives of this study were to use principal components analysis to identify distinct modes of variation in all workload metrics accumulated over 1, 2, 3, and 4 weeks and to examine differences among the modes of variation in workload metrics between participants before the injury and uninjured participants. Forty-nine youth basketball players participated in their typical basketball practices and games, and lower extremity injuries were classified as patellar or Achilles tendinopathy, other overuse, or acute. An inertial measurement unit recorded the number and height of all jumps, and session rating of perceived exertion was recorded. The previous 1-, 2-, 3-, and 4-week workloads of jump count, jump height, weighted jump height, and session rating of perceived exertion were summed for each participant-week. Principal components analysis explained the variance in the accumulated workload variables. Using the retained principal components, the difference between the workload of injured participants in the week before the injury and the mean workload of uninjured participants was described for patellar or Achilles tendinopathy, overuse lower extremity injury, and any lower extremity injury. Participants with patellar or Achilles tendinopathy and overuse lower extremity injuries had a low workload magnitude for all variables in the 1, 2, 3, and 4 weeks before injury compared with the weeks before no injury. Participants with overuse lower extremity injuries and any lower extremity injury had a high previous 1-week workload for all variables along with a low previous 3- and 4-week jump count, jump height, and weighted jump height before injury compared with the weeks before no injury. Weighted jump height represents the cumulative damage experienced by tissues due to repetitive loads. Injured youth basketball athletes had a low previous 3- and 4-week workloads coupled with a high previous 1-week workload.

Workload a-WEAR-ness: Monitoring Workload in Team Sports With Wearable Technology. A Scoping Review.

OBJECTIVES: To (1) identify the wearable devices and associated metrics used to monitor workload and assess injury risk, (2) describe the situations in which workload was monitored using wearable technology (including sports, purpose of the analysis, location and duration of monitoring, and athlete characteristics), and (3) evaluate the quality of evidence that workload monitoring can inform injury prevention. DESIGN: Scoping review. LITERATURE SEARCH: We searched the CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Embase, HealthSTAR, MEDLINE, PsycINFO, SPORTDiscus, and Web of Science databases. STUDY SELECTION CRITERIA: We included all studies that used wearable devices (eg, heart rate monitor, inertial measurement units, global positioning system) to monitor athlete workload in a team sport setting. DATA SYNTHESIS: We provided visualizations that represented the workload metrics reported, sensors used, sports investigated, athlete characteristics, and the duration of monitoring. RESULTS: The 407 included studies focused on team ball sports (67% soccer, rugby, or Australian football), male athletes (81% of studies), elite or professional level of competition (74% of studies), and young adults (69% of studies included athletes aged between 20 and 28 years). Thirty-six studies of 7 sports investigated the association between workload measured with wearable devices and injury. CONCLUSION: Distance-based metrics derived from global positioning system units were common for monitoring workload and are frequently used to assess injury risk. Workload monitoring studies have focused on specific populations (eg, elite male soccer players in Europe and elite male rugby and Australian football players in Oceania). Different injury definitions and reported workload metrics and poor study quality impeded conclusions regarding the relationship between workload and injury. J Orthop Sports Phys Ther 2020;50(10):549-563. doi:10.2519/jospt.2020.9753.

New Considerations for Collecting Biomechanical Data Using Wearable Sensors: The Effect of Different Running Environments.

Traditionally, running biomechanics analyses have been conducted using 3D motion capture during treadmill or indoor overground running. However, most runners complete their runs outdoors. Since changes in running terrain have been shown to influence running gait mechanics, the purpose of this study was to use a machine learning approach to objectively determine relevant accelerometer-based features to discriminate between running patterns in different environments and determine the generalizability of observed differences in running patterns. Center of mass accelerations were recorded for recreational runners in treadmill-only (n = 28) and sidewalk-only (n = 25) environments, and an independent group (n = 16) ran in both treadmill and sidewalk environments. A feature selection algorithm was used to develop a training dataset from treadmill-only and sidewalk-only running. A binary support vector machine model was trained to classify treadmill and sidewalk running. Classification accuracy was determined using 10-fold cross-validation of the training dataset and an independent testing dataset from the runners that ran in both environments. Nine features related to the consistency and variability of center of mass accelerations were selected. Specifically, there was greater ratio of vertical acceleration during treadmill running and a greater ratio of anterior-posterior acceleration during sidewalk running in both the training and testing dataset. Step and stride regularity were significantly greater in the treadmill condition for the vertical axis in both the training and testing dataset, and in the medial-lateral axis for the testing dataset. During sidewalk running, there was significantly greater variability in the magnitude of the vertical and anterior-posterior accelerations for both datasets. The classification accuracy based on 10-fold cross-validation of the training dataset (M = 93.17%, SD = 2.43%) was greater than the classification accuracy of the independent testing dataset (M = 83.81%, SD = 3.39%). This approach could be utilized in future analyses to identify relevant differences in running patterns using wearable technology.

Validation of a commercially available inertial measurement unit for recording jump load in youth basketball players.

A high incidence of overuse knee injuries among youth basketball players may be attributed to number of jumps. Wearable technology may be an effective tool for measuring jump load compared to traditional counting methods. The purpose of this study was to validate a commercially available jump counter (VERT® Classic) in youth basketball practices and games, and to identify the characteristics (i.e., height, direction, takeoff) of jumps recorded by the VERT® Classic. 46 (19F, 27M) youth basketball players wore a VERT® Classic and were recorded on video during games and practices. The number of jumps recorded by the VERT® Classic and evaluated by video raters were compared for each jump characteristic using intraclass correlation coefficient (ICC(3,k)), mean offset, and limits of agreement. The number and percent of VERT® Classic jumps and corresponding video jumps according to timestamp were reported. VERT® Classic jumps had excellent reliability with video-counted jumps over 15 cm (ICC(3,k) = 0.958), with a mean offset of -2.4 jumps (fewer VERT® Classic) and limits of agreement -12.6 to 7.8 jumps. Pairs of corresponding jumps represented 68.0% of total video jumps and 92.0% of VERT® Classic jumps. The VERT® Classic can provide an estimate of jump load in youth basketball.

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.

New Considerations for Collecting Biomechanical Data Using Wearable Sensors: How Does Inclination Influence the Number of Runs Needed to Determine a Stable Running Gait Pattern?

As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a 'typical' or 'stable' gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2-4 runs were needed to define a stable running pattern for univariate, and 4-5 days were necessary for multivariate analysis across all inclination conditions. Pearson's correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63-0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual's stable gait pattern.

Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions.

The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions.

Effects of Caffeine on Exertion, Skill Performance, and Physicality in Ice Hockey.

PURPOSE: To determine the effects of low-dose caffeine supplementation (3 mg/kg body mass) consumed 1 h before the experiment on rating of perceived exertion (RPE), skills performance (SP), and physicality in male college ice hockey players. METHODS: Using a double-blind, placebo-controlled, randomized crossover experimental design, 15 college ice hockey players participated in SP trials and 14 participated in scrimmage (SC) trials on a total of 4 d, with prescribed ice hockey tasks occurring after a 1-h high-intensity practice. In the SP trials, time to complete and error rate for each drill of the validated Western Hockey League Combines Testing Standard were recorded. Peak head accelerations, trunk contacts, and offensive performance were quantified during the SC trials using accelerometery and video analysis. RPE was assessed in both the SP and SC trials. RESULTS: RPE was significantly greater in the caffeine (11.3 [2.0]) than placebo (9.9 [1.9]) condition postpractice (P = .002), with a trend toward greater RPE in caffeine (16.9 [1.8]) than placebo (15.7 [2.8]) post-SC (P = .05). There was a greater number of peak head accelerations in the caffeine (4.35 [0.24]) than placebo (4.14 [0.24]) condition (P = .028). Performance times, error rate, and RPE were not different between intervention conditions during the SP trials (P > .05). CONCLUSIONS: A low dose of caffeine has limited impact on sport-specific skill performance and RPE but may enhance physicality during ice hockey SCs.

Subject-specific and group-based running pattern classification using a single wearable sensor.

The objective of this study was to determine whether subject-specific or group-based models provided better classification accuracy to identify changes in biomechanical running gait patterns across different inclination conditions. The classification process was based on measurements from a single wearable sensor using a total of 41,780 strides from eleven recreational runners while running in real-world and uncontrolled environment. Biomechanical variables included pelvic drop, ground contact time, braking, vertical oscillation of pelvis, pelvic rotation, and cadence were recorded during running on three inclination grades: downhill, -2° to -7°; level, -0.2° to +0.2°; and uphill, +2° to +7°. An ensemble and non-linear machine learning algorithm, random forest (RF), was used to classify inclination condition and determine the importance of each of the biomechanical variables. Classification accuracy was determined for subject-specific and group-based RF models. The mean classification accuracy of all subject-specific RF models was 86.29%, while group-based classification accuracy was 76.17%. Braking was identified as the most important variable for all the runners using the group-based model and for most of the runners based on a subject-specific models. In addition, individual runners used different strategies across different inclination conditions and the ranked order of variable importance was unique for each runner. These results demonstrate that subject-specific models can better characterize changes in gait biomechanical patterns compared to a more traditional group-based approach.

New considerations for collecting biomechanical data using wearable sensors: Number of level runs to define a stable running pattern with a single IMU.

Wearable technology can be used to quantify running biomechanical patterns in a runner's natural environment, however, changes in external factors during outdoor running may influence a runner's typical gait pattern. Therefore, the purpose of this study was to determine how many runs are needed to define a stable or typical running pattern. Six biomechanical variables were recorded using a single wearable sensor placed on the lower back during ten outdoor runs for twelve runners. Univariate and multivariate distributions were created and based on the probability density function, the percent of similar data points (within 95%) from each unique run for the same runner were determined. Stability was defined when the addition of data from a new run resulted in less than a 5% change in the probability density function. To cross-validate, the percent of similar data points at stability was compared between the same and different runners using a repeated-measures MANOVA (Bonferroni-corrected α = 0.007). The maximum number of runs needed to reach stability for univariate and multivariate analyses was four and five, respectively. There was a significant overall effect on similar data points between the same and different runners (p = 0.001), with a greater percent of similar data points for the same runner compared to other runners (p < 0.007). Based on biomechanical data collected using a single wearable sensor placed on the lower back, this is the first study to show that four (univariate) to five (multivariate) runs are needed to establish a stable running pattern in real-world settings.

Running patterns for male and female competitive and recreational runners based on accelerometer data.

The purpose of this study was to classify runners in sex-specific groups as either competitive or recreational based on center of mass (CoM) accelerations. Forty-one runners participated in the study (25 male and 16 female), and were labeled as competitive or recreational based on age, sex, and race performance. Three-dimensional acceleration data were collected during a 5-minute treadmill run, and 24 features were extracted. Support vector machine classification models were used to examine the utility of the features in discriminating between competitive and recreational runners within each sex-specific subgroup. Competitive and recreational runners could be classified with 82.63 % and 80.4 % in the male and female models, respectively. Dominant features in both models were related to regularity and variability, with competitive runners exhibiting more consistent running gait patterns, but the specific features were slightly different in each sex-specific model. Therefore, it is important to separate runners into sex-specific competitive and recreational subgroups for future running biomechanical studies. In conclusion, we have demonstrated the ability to analyze running biomechanics in competitive and recreational runners using only CoM acceleration patterns. A runner, clinician, or coach may use this information to monitor how running patterns change as a result of training.

A Principal Components Analysis Approach to Quantifying Foot Clearance and Foot Clearance Variability.

Low foot clearance and high variability may be related to falls risk. Foot clearance is often defined as the local minimum in toe height during swing; however, not all strides have this local minimum. The primary purpose of this study was to identify a nondiscrete measure of foot clearance during all strides, and compare discrete and nondiscrete measures in ability to rank individuals on foot clearance and variability. Thirty-five participants (young adults [n = 10], older fallers [n = 10], older nonfallers [n = 10], and stroke survivors [n = 5]) walked overground while lower extremity 3D kinematics were recorded. Principal components analysis (PCA) of the toe height waveform yielded representation of toe height when it was closest to the ground. Spearman's rank order correlation assessed the association of foot clearance and variability between PCA and discrete variables, including the local minimum. PCA had significant (P < .05) moderate or strong associations with discrete measures of foot clearance and variability. An approximation of the discrete local minimum had a weak association with PCA and other discrete measures of foot clearance. A PCA approach to quantifying foot clearance can be used to identify the behavioral components of toe height when it is closest to the ground, even for strides without a local minimum.

Identifying trippers and non-trippers based on knee kinematics during obstacle-free walking.

Trips are a major cause of falls. Sagittal-plane kinematics affect clearance between the foot and obstacles, however, it is unclear which kinematic measures during obstacle-free walking are associated with avoiding a trip when encountering an obstacle. The purpose of this study was to determine kinematic factors during obstacle-free walking that are related to obstacle avoidance ability. It was expected that successful obstacle avoidance would be associated with greater peak flexion/dorsiflexion and range of motion (ROM), and differences in timing of peak flexion/dorsiflexion during swing of obstacle-free walking for the hip, knee and ankle. Three-dimensional kinematics were recorded as 35 participants (young adults age 18-45 (N = 10), older adults age 65+ without a history of falls (N = 10), older adults age 65+ who had fallen in the last six months (N = 10), and individuals who had experienced a stroke more than six months earlier (N = 5)) walked on a treadmill, under obstacle-free walking conditions with kinematic features calculated for each stride. A separate obstacle avoidance task identified trippers (multiple obstacle contact) and non-trippers. Linear discriminant analysis with sequential feature selection classified trippers and non-trippers based on kinematics during obstacle-free walking. Differences in classification performance and selected features (knee ROM and timing of peak knee flexion during swing) were evaluated between trippers and non-trippers. Non-trippers had greater knee ROM (P = .001). There was no significant difference in classification performance (P = .193). Individuals with reduced knee ROM during obstacle-free walking may have greater difficulty avoiding obstacles.

The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review.

BACKGROUND: Quantitative gait analysis is essential for evaluating walking and running patterns for markers of pathology, injury, or other gait characteristics. It is expected that the portability, affordability, and applicability of wearable devices to many different populations will have contributed advancements in understanding the real-world gait patterns of walkers and runners. Therefore, the purpose of this systematic review was to identify how wearable devices are being used for gait analysis in out-of-lab settings. METHODS: A systematic search was conducted in the following scientific databases: PubMed, Medline, CINAHL, EMBASE, and SportDiscus. Each of the included articles was assessed using a custom quality assessment. Information was extracted from each included article regarding the participants, protocol, sensor(s), and analysis. RESULTS: A total of 61 articles were reviewed: 47 involved gait analysis during walking, 13 involved gait analysis during running, and one involved both walking and running. Most studies performed adequately on measures of reporting, and external and internal validity, but did not provide a sufficient description of power. Small, unobtrusive wearable devices have been used in retrospective studies, producing unique measures of gait quality. Walking, but not running, studies have begun to use wearable devices for gait analysis among large numbers of participants in their natural environment. CONCLUSIONS: Despite the advantages provided by the portability and accessibility of wearable devices, more studies monitoring gait over long periods of time, among large numbers of participants, and in natural walking and running environments are needed to analyze real-world gait patterns, and would facilitate prospective, subject-specific, and subgroup investigations. The development of wearables-specific metrics for gait analysis provide insights regarding the quality of gait that cannot be determined using traditional components of in-lab gait analyses. However, guidelines for the usability of wearable devices and the validity of wearables-based measurements of gait quality need to be established.

Classifying running speed conditions using a single wearable sensor: Optimal segmentation and feature extraction methods.

Accelerometers have been used to classify running patterns, but classification accuracy and computational load depends on signal segmentation and feature extraction. Stride-based segmentation relies on identifying gait events, a step avoided by using window-based segmentation. For each segment, discrete points can be extracted from the accelerometer signal, or advanced features can be computed. Therefore, the purpose of this study was to examine how different segmentation and feature extraction methods influence the accuracy and computational load of classifying running conditions. Forty-four runners ran at their preferred speed and 25% faster than preferred while an accelerometer at the lower back recorded 3D accelerations. Computational load was determined as the accelerometer signal was segmented into single and five strides, and corresponding small and large windows, with discrete points extracted from the single stride segments and advanced features computed from all four segment types. Each feature set was used to classify speed conditions and classification accuracy was recorded. Computational load and classification accuracy were compared across all feature sets using a repeated-measures MANOVA, with follow-up t-tests to compare feature type (discrete vs. advanced), segmentation method (stride- vs. window-based), and segment size (small vs. large), using a Bonferroni-adjusted α = 0.003. The five-stride (97.49 (±4.57)%) and large-window advanced (97.23 (±5.51)%) feature sets produced the greatest classification accuracy, but the large-window advanced feature set had a lower computational load (0.0041 (±0.0002)s) than the stride-based feature sets. Therefore, using a few advanced features and large overlapping window sizes yields the best performance of both classification accuracy and computational load.