Validation of Polar ElixirTM Pulse Oximeter against Arterial Blood Gases during Stepwise Steady State Inspired Hypoxia.

PURPOSE: The purpose of this study was to evaluate the accuracy of peripheral oxygen saturation (SpO2) measurements from Polar ElixirTM pulse oximetry technology compared to arterial oxygen saturation (SaO2) measurements during acute stepwise steady state inspired hypoxia at rest. A post hoc objective was to determine if SpO2 measurements could be improved by recalibrating the Polar ElixirTM algorithm with SaO2 values from a random subset of participants. METHODS: The International Organization for Standardization (ISO) protocol (ISO 80601-2-61:2017) for evaluating the SpO2 accuracy of pulse oximeter equipment was followed whereby five plateaus of SaO2 between 70-100% were achieved using stepwise reductions in inspired O2 during supine rest. Blood samples drawn through a radial arterial catheter from 25 participants were first used to compare SaO2 to SpO2 measurements from Polar ElixirTM. Then the Polar ElixirTM algorithm was recalibrated using SaO2 data from 13 random participants and SpO2 estimates were recalculated for the other 12 participants. For SaO2 values between 70-100%, root mean square error (RMSE), intraclass correlations (ICC), Pearson correlations, and Bland-Altman plots were used to assess the accuracy, agreement, and strength of relationship between SaO2 values and SpO2 values from Polar ElixirTM. RESULTS: The initial RMSE for Polar ElixirTM was 4.13%. After recalibrating the algorithm, the RMSE was improved to 2.67%. The ICC revealed excellent levels of agreement between SaO2 and Polar ElixirTM SpO2 values both before (ICC(3,1) = 0.837, df = 574, p < 0.001) and after (ICC(3,1) = 0.942, df = 287, p < 0.001) recalibration. CONCLUSIONS: Relative to ISO standards, Polar ElixirTM yielded accurate SpO2 measurements during stepwise inspired hypoxia at rest when compared to SaO2 values, which were improved by recalibrating the algorithm using a subset of the SaO2 data.

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.

Measuring Gait Velocity and Stride Length with an Ultrawide Bandwidth Local Positioning System and an Inertial Measurement Unit.

One possible modality to profile gait speed and stride length includes using wearable technologies. Wearable technology using global positioning system (GPS) receivers may not be a feasible means to measure gait speed. An alternative may include a local positioning system (LPS). Considering that LPS wearables are not good at determining gait events such as heel strikes, applying sensor fusion with an inertial measurement unit (IMU) may be beneficial. Speed and stride length determined from an ultrawide bandwidth LPS equipped with an IMU were compared to video motion capture (i.e., the "gold standard") as the criterion standard. Ninety participants performed trials at three self-selected walk, run and sprint speeds. After processing location, speed and acceleration data from the measurement systems, speed between the last five meters and stride length in the last stride of the trial were analyzed. Small biases and strong positive intraclass correlations (0.9-1.0) between the LPS and "the gold standard" were found. The significance of the study is that the LPS can be a valid method to determine speed and stride length. Variability of speed and stride length can be reduced when exploring data processing methods that can better extract speed and stride length measurements.

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.

Fatigue-Related Changes in Running Gait Patterns Persist in the Days Following a Marathon Race.

CONTEXT: The risk of experiencing an overuse running-related injury can increase with atypical running biomechanics associated with neuromuscular fatigue and/or training errors. While it is important to understand the changes in running biomechanics within a fatigue-inducing run, it may be more clinically relevant to assess gait patterns in the days following a marathon to better evaluate the effects of inadequate recovery on injury. OBJECTIVE: To use center of mass (CoM) acceleration patterns to investigate changes in running patterns prior to (PRE) and at 2 (POST2) and 7 (POST7) days following a marathon race. DESIGN: Pre-post intervention study. SETTING: A 200-m oval track surface. PARTICIPANTS: Seventeen recreational marathon runners (10 females, age = 34.2 [5.67] y; 7 males, age = 47.41 [15.32] y). INTERVENTION: Marathon race. MAIN OUTCOME MEASURES: An inertial measurement unit was placed near the CoM to collect triaxial acceleration data during overground running for PRE, POST2, and POST7 sessions. Twenty-two features were extracted from the acceleration waveforms to characterize different aspects of running gait. Lower-limb musculoskeletal pain was also recorded at each session with a visual analog scale. RESULTS: At POST2, runners reported higher self-reported pain and exhibited elevated peak mediolateral acceleration with an increased mediolateral ratio of acceleration root mean square compared with PRE. At POST7, pain was reduced and more similar to PRE, with runners demonstrating increased stride regularity in the vertical direction and decreased peak resultant acceleration. CONCLUSIONS: The observed changes in CoM motion at POST2 may be associated with atypical running biomechanics that can translate to greater mediolateral impulses, potentially increasing the risk of injury. This study demonstrates the use of an accelerometer as an effective tool to detect atypical CoM motion for runners due to fatigue, recovery, and musculoskeletal pain in real-world environments.

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.

Classification of higher- and lower-mileage runners based on running kinematics.

BACKGROUND: Running-related overuse injuries can result from the combination of extrinsic (e.g., running mileage) and intrinsic risk factors (e.g., biomechanics and gender), but the relationship between these factors is not fully understood. Therefore, the first purpose of this study was to determine whether we could classify higher- and lower-mileage runners according to differences in lower extremity kinematics during the stance and swing phases of running gait. The second purpose was to subgroup the runners by gender and determine whether we could classify higher- and lower-mileage runners in male and female subgroups. METHODS: Participants were allocated to the "higher-mileage" group (≥32 km/week; n = 41 (30 females)) or to the "lower-mileage" group (≤25 km; n = 40 (29 females)). Three-dimensional kinematic data were collected during 60 s of treadmill running at a self-selected speed (2.61 ± 0.23 m/s). A support vector machine classifier identified kinematic differences between higher- and lower-mileage groups based on principal component scores. RESULTS: Higher- and lower-mileage runners (both genders) could be separated with 92.59% classification accuracy. When subgrouping by gender, higher- and lower-mileage female runners could be separated with 89.83% classification accuracy, and higher- and lower-mileage male runners could be separated with 100% classification accuracy. CONCLUSION: These results demonstrate there are distinct kinematic differences between subgroups related to both mileage and gender, and that these factors need to be considered in future research.

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.

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.

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.

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.

Kinematic Gait Patterns in Competitive and Recreational Runners.

Certain homogeneous running subgroups demonstrate distinct kinematic patterns in running; however, the running mechanics of competitive and recreational runners are not well understood. Therefore, the purpose of this study was to determine whether we could separate and classify competitive and recreational runners according to gait kinematics using multivariate analyses and a machine learning approach. Participants were allocated to the 'competitive' (n = 20) or 'recreational' group (n = 15) based on age, sex, and recent race performance. Three-dimensional (3D) kinematic data were collected during treadmill running at 2.7 m/s. A support vector machine (SVM) was used to determine if the groups were separable and classifiable based on kinematic time point variables as well as principal component (PC) scores. A cross-fold classification accuracy of 80% was found between groups using the top 5 ranked time point variables, and the groups could be separated with 100% cross-fold classification accuracy using the top 14 ranked PCs explaining 60.29% of the variance in the data. The features were primarily related to pelvic tilt, as well as knee flexion and ankle eversion in late stance. These results suggest that competitive and recreational runners have distinct, 'typical' running patterns that may help explain differences in injury mechanisms.

Accelerometer-Based Step Regularity Is Lower in Older Adults with Bilateral Knee Osteoarthritis.

Purpose: To compare the regularity and symmetry of gait between a cohort of older adults with bilateral knee osteoarthritis (OA) and an age and sex-matched control group of older adults with healthy knees. Methods: Fifteen (8 females) older adults with knee OA (64.7 ± 6.7 years) and fifteen (8 females) pain-free controls (66.1 ± 10.0 years) completed a 9-min. walk at a self-selected, comfortable speed while wearing a single waist-mounted tri-axial accelerometer. The following gait parameters were compared between the two groups according to sex: mean step time, mean stride time, stride and step regularity (defined as the consistency of the stride-to-stride or step-to-step pattern) and the symmetry of gait (defined as the difference between step and stride regularity) as determined by an unbiased autocorrelation procedure that analyzed the pattern of acceleration in the vertical, mediolateral and anteroposterior directions. Results: Older adults with knee OA displayed significantly less step regularity in the vertical (p < 0.05) and anteroposterior (p < 0.05) directions than controls. Females with knee OA were also found to have significantly less mediolateral step regularity than female controls (p < 0.05), whereas no difference was found between males. Conclusion: The results showed that the regularity of the step pattern in individuals with bilateral knee OA was less consistent compared to similarly-aged older adults with healthy knees. The findings suggest that future studies should investigate the relationship between step regularity, sex and movement direction as well as the application of these methods to the clinical assessment of knee OA.

Accelerometer-based determination of gait variability in older adults with knee osteoarthritis.

Knee osteoarthritis (KOA) can affect the spatiotemporal (ST) aspects of gait as well as the variability of select ST parameters based on standard linear measures of variability (e.g., standard deviation (SD) and coefficient of variation). Non-linear measures (e.g., fractal scaling index (FSI) and sample entropy) can be more sensitive to changes in gait variability, and have been used to quantify differences in the stride patterns of patients with Parkinson's disease and the motion of ACL-deficient knees. However, the effect of KOA on the dynamic complexity of the stride pattern has not been investigated. Therefore, the purpose of this study was to investigate the effect of KOA on gait variability (linear and non-linear measures) in a group of older adults, and to compare these results to a healthy control group. Participants walked for 10min with a tri-axial accelerometer placed at the lower back. Mean and SDs of stride time and step time as well as the FSI for the entire series of stride times were calculated for each participant. Participants with KOA had significantly greater mean stride time (p=0.031) and step time (p=0.024) than control group participants. While stride and step time variability (SD) were greater in the KOA group, the differences were not significant, nor was the difference in the FSI. Low statistical power (ß=0.40 and 0.30 for stride and step time SD, respectively) combined with the confounding effects of walking speed and heterogeneous KOA severity likely prevented significant differences from being found.