In this study we have shown that VO2max can be predicted from submaximal running using data fusion of heart rate and accelerometer measurements. We extracted various features from these measurements and used a data-driven approach based on greedy forward selection to select a small subset of these features. The best model found in this paper has an explained variance of 0.784 and uses four features: two descriptive features (gender and body weight), one heart rate feature (HR-10) and one accelerometer feature (SD-1t,total,0). This model can predict an individual’s maximal oxygen uptake from
objective variables calculated from running on a treadmill at only 8 or 9 km · hr−1for four minutes.
While the method presented in this study is accessible for recreational runners, it would be more convenient if VO2max can be predicted during outdoor running instead.
Future work can explore whether the feature extraction and selection methods used in this paper can be applied for outdoor running as well. It will be important then to find features that are independent of running speed.
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Bijlage B
Poster
maximal rate at which an individual can consume oxygen (ml/kg/min) VO2max ≡
Depending on age, gender, genetic markers, training status, …
Upper limit of endurance performance
Measured using a
maximal incremental exercise test Practical limitations
Expensive lab set-up required Physical limitations
Risks for heart disease patients, physically demanding, hard to incorporate in strict training regimen
Predict from
submaximal exercise Based on data fusion of physiological and
• 1 min. rest between stages
• Increasing running speed
• Until volitional exhaustion Data collected during the test:
• Heart rate
• Acceleration from tibia, lower back and upper back
• Oxygen consumption (VO2)
25 subjects, of which From warm-up and first 3
stages (submaximal effort)
Highly intercorrelated features
Not all features useful for predicting VO2max
• Descriptive features (D): gender and body weight
• Heart rate (HR): (reciprocal of) mean heart rate for each stage [8 features]
• Biomechanical features (B): (reciprocal of) mean, stddev, RMS, energy, phase plot height, width and ratio (=width/height) for each direction (x, y, z, total) at each
accelerometer, for each stage [672 features] Best result for S4 R2= 0.784 S1(D)
S2(D+HR) S3(D+B) S4(D+HR+B)
S1= {gender, body weight}
S3= S1 ∪{phase plot ratio of total lower back acc. in stage 2}
S2= S1 ∪{reciprocal of mean heart rate in warm-up stage}
S4= S1 ∪S2 ∪{reciprocal of stddev of total tibia acc. in warm-up stage}
Best result:
• Least squares linear regression + greedy forward selection
+ physiological and biomechanical features
• Selected features are from the warm-up stage only:
minimal physical effort required
Prof. Jesse Davis, Wannes Meert, Prof. Benedicte Vanwanseele
• Improved results of model by Weyand et al.
• No questionnaires or self-chosen running speed Select best feature
Add f* toS if
Select features Susing greedy forward selection:
a
Evaluated by R2with leave-one-subject-out cross-validation
S4(D+HR+B) S2(D+HR) S1(D)