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.

Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions.

Running-related overuse injuries can result from a combination of various intrinsic (e.g., gait biomechanics) and extrinsic (e.

Use of baseline pelvic acceleration during running for classifying response to muscle strengthening treatment in patellofemoral pain: A preliminary study.

Background: Objectively identifying patients at baseline who may not respond well to a generic muscle strengthening intervention could improve clinical practice by optimizing treatment strategies. The purpose of this study was to determine whether pelvic acceleration measures during running, and clinical and demographic variables could classify patellofemoral pain patients according to their response to a 6-week hip/core and knee exercise-based rehabilitation protocol.

Methods: Forty-one individuals with patellofemoral pain participated in a 6-week exercise intervention program and were sub-grouped into treatment Responders (n = 28) and Non-responders (n = 13) based on self-reported pain and function measures.

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.

Runners with patellofemoral pain demonstrate sub-groups of pelvic acceleration profiles using hierarchical cluster analysis: an exploratory cross-sectional study.

Background: Previous studies have suggested that distinct and homogenous sub-groups of gait patterns exist among runners with patellofemoral pain (PFP), based on gait analysis. However, acquisition of 3D kinematic data using optical systems is time consuming and prone to marker placement errors. In contrast, axial segment acceleration data can represent an overall running pattern, being easy to acquire and not influenced by marker placement error.

Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions.

The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle "big data". Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V's definition of big data: volume, velocity, variety, veracity, and value.

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.

Gait Kinematics in Individuals with Acute and Chronic Patellofemoral Pain.

Purpose: This study aimed to identify the discriminating kinematic gait characteristics between individuals with acute and chronic patellofemoral pain (PFP) and healthy controls.

Methods: Ninety-eight runners with PFP (39 male, 59 female) and 98 healthy control runners (38 male, 60 female) ran on a treadmill at a self-selected speed while three-dimensional lower limb kinematic data were collected. Runners with PFP were split into acute (n = 25) and chronic (n = 73) subgroups on the basis of whether they had been experiencing pain for less or greater than 3 months, respectively.

The use of real-time feedback to improve kinematic marker placement consistency among novice examiners.

Marker placement deviation has been shown to be the largest source of error in gait kinematic data, limiting the ability of clinicians and researchers to conduct between-day or between-center investigations. Prior marker-placement standardization methods are either impractical for a clinical setting or rely on expert marker placement. However, a recently developed, real-time feedback tool has been developed and shown to improve marker placement and downstream kinematic calculations.

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.

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.

Reliability of gait analysis using wearable sensors in patients with knee osteoarthritis.

The aim of this study was to determine the test-retest reliability of linear acceleration waveforms collected at the low back, thigh, shank, and foot during walking, in a cohort of knee osteoarthritis patients, by applying two separate sensor attitude correction methods (static attitude correction and dynamic attitude correction). Linear acceleration data were collected on the subjects׳ most affected limb during treadmill walking on two separate days. Results reveal all attitude corrected acceleration waveforms displayed high repeatability, with coefficient of multiple determination values ranging from 0.

Gait biomechanics in the era of data science.

Data science has transformed fields such as computer vision and economics. The ability of modern data science methods to extract insights from large, complex, heterogeneous, and noisy datasets is beginning to provide a powerful complement to the traditional approaches of experimental motion capture and biomechanical modeling. The purpose of this article is to provide a perspective on how data science methods can be incorporated into our field to advance our understanding of gait biomechanics and improve treatment planning procedures.

Determination of patellofemoral pain sub-groups and development of a method for predicting treatment outcome using running gait kinematics.

Background: Not all patients with patellofemoral pain exhibit successful outcomes following exercise therapy. Thus, the ability to identify patellofemoral pain subgroups related to treatment response is important for the development of optimal therapeutic strategies to improve rehabilitation outcomes. The purpose of this study was to use baseline running gait kinematic and clinical outcome variables to classify patellofemoral pain patients on treatment response retrospectively.

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%).

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.

Kinematic gait patterns and their relationship to pain in mild-to-moderate hip osteoarthritis.

Background: Mild-to-moderate hip osteoarthritis is often managed clinically in a non-surgical manner. Effective non-surgical management of this population requires characterizing the specific impairments within this group. To date, a complete description of all lower extremity kinematics in mild-to-moderate hip osteoarthritis patients has not been presented.

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.

Validation of a Torso-Mounted Accelerometer for Measures of Vertical Oscillation and Ground Contact Time During Treadmill Running.

The purpose of this study was to validate measures of vertical oscillation (VO) and ground contact time (GCT) derived from a commercially-available, torso-mounted accelerometer compared with single marker kinematics and kinetic ground reaction force (GRF) data. Twenty-two semi-elite runners ran on an instrumented treadmill while GRF data (1000 Hz) and three-dimensional kinematics (200 Hz) were collected for 60 s across 5 different running speeds ranging from 2.7 to 3.

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.

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.

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.