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

Benson, Lauren C.; Clermont, Christian A.; Osis, Sean T.; Kobsar, Dylan; Ferber, Reed

doi:10.1016/j.jbiomech.2018.01.034

Cited by 42 publications

(47 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the use of 10fold cross-validation of the training dataset to attempt to improve generalizability of classification, the model slightly overfit to the training dataset as there was lower classification accuracy for the independent testing dataset compared to the 10-fold crossvalidation of the training dataset. Regarding real-world usability, previous studies that have classified IMU-generated running and walking patterns have consistently reported classification accuracy greater than 80% (Kobsar et al, 2014(Kobsar et al, , 2015Phinyomark et al, 2014;Ahamed et al, 2018Ahamed et al, , 2019Benson et al, 2018b;Clermont et al, 2018). Thus, the reported 93.17% accuracy for the training dataset and 83.81% accuracy for the independent testing dataset in the current study suggests that this classification mechanism has practical use.…”

Section: Discussionsupporting

confidence: 57%

“…However, the current study did not calculate coordinative variability in a manner similar to the methods proposed by Hamill et al (2012), so future prospective studies should consider a link between the increased center of mass variability observed during sidewalk running and running-related injuries. Due to the influence of speed on the magnitude of center of mass accelerations (Kobsar et al, 2014;Benson et al, 2018b), and the tendency to preferentially select a slightly slower speed during treadmill compared to overground running (Kong et al, 2012), speed was included as a potential feature in the classification model. However, speed was not one of the selected features used in the model.…”

Section: Discussionmentioning

confidence: 99%

“…Inertial measurement units (IMUs) are portable devices that can be used to quantify running biomechanical patterns in a runner's natural environment (Norris et al, 2014;Reenalda et al, 2016), yet, these investigations are still rare (Benson et al, 2018a). Running biomechanical analysis using IMUs is commonly conducted by recording 3D center of mass accelerations and extracting features related to the magnitude, consistency and variability of the signal (Henriksen et al, 2004;Moe-Nilssen and Helbostad, 2004;Kobsar et al, 2014;Benson et al, 2018b;Clermont et al, 2018). There remains an absence of an association between joint-level mechanics commonly investigated using laboratory-based motion capture systems and features generated from center of mass accelerations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Benson

Clermont

Ferber

2020

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

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 crossvalidation 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.

show abstract

Section: Discussionsupporting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Benson

Clermont

Ferber

2020

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, this study had a relatively small sample size compared to the number of predictors. The number of participants in the present study was, however, comparable to other similar researches in clinical biomechanics (n = 41 in [46], n = 44 in [47]). Results from the study will enable future researchers to fit the presently reported model's learning curve to inverse power law models [48] and to estimate the sample size needed to achieve a desired classification performance.…”

Section: Discussionsupporting

confidence: 87%

Interpretable machine learning models for classifying low back pain status using functional physiological variables

et al. 2020

View full text Add to dashboard Cite

Purpose To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. Methods Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. Results Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak = 0.047) in model 1, the deltoid muscle (peak = 0.052) in model 2, and the iliocostalis muscle (peak = 0.16) in model 3. Conclusion The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk.

show abstract

“…Another potential limitation is the amount of data collected and utilized for the analysis (i.e., continuous waveform data from three sensors). While on-board preprocessing would likely provide the most efficient management of data, based on our previous research the limiting factor in this may be the event detection itself [ 48 ]. Nevertheless, this topic remains outside the scope of the current proof-of-concept study and future work may look to examine the most efficient ways preprocess and package the data for analysis.…”

Section: Discussionmentioning

confidence: 99%

Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach

Kobsar

Ferber

2018

Sensors

Self Cite

View full text Add to dashboard Cite

Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual’s typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual’s movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers observed post-intervention exhibited a large association (ρ = 0.78) with improvements in self-reported clinical outcomes. These findings demonstrate a proof-of-concept and a novel methodological approach for integrating machine learning and wearable sensor data. This approach provides an objective and evidence-informed way to understand clinically important changes in human movement patterns in response to exercise therapy.

show abstract

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

Cited by 42 publications

References 31 publications

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

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

Interpretable machine learning models for classifying low back pain status using functional physiological variables

Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach

Contact Info

Product

Resources

About