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. 2021 Apr:2021:V001T03A004.
doi: 10.1115/dmd2021-1068. Epub 2021 May 11.

MODELING REALITY: REVISITING CALVERT'S FITNESS SIMULATION

Affiliations

MODELING REALITY: REVISITING CALVERT'S FITNESS SIMULATION

Aniya Hartzler et al. Proc Des Med Devices Conf. 2021 Apr.

Abstract

Wearable sensors have gained mainstream acceptance for health and fitness monitoring despite the absence of clinically validated analytic models for clinical decision support. Individual sensors measuring, say, EKG signal and heart rate can provide insight on cardiovascular response, but a more complete picture of health and fitness requires a more complete portfolio of sensors and data. This paper outlines the research underway to revisit and reconfigure the 1976 Calvert systems model of the effect of training on physical performance. Specifically, we use wearable sensor data from clinical trials to supplement a hybrid model created by nesting Perl's Performance-Potential model within Calvert's transfer function approach to system simulation. Contemporary simulation tools combined with wearables clinical trial data is the foundation for a more agile platform for simulation of fitness and exploration of causality between training and physical performance. This platform offers the opportunity to strategically integrate data from various wearable sensors in a fashion enabling improved support for post-injury and return to sport decision-making.

Keywords: Calvert model; Wearables; fitness monitoring; post injury physical therapy; return to sport.

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Figures

FIGURE 1:
FIGURE 1:
MULTICOMPONENT MODEL OF CALVERT TO EXPLAIN EFFECTS OF DIFFERENT FORMS OF TRAINING ON PERFORMANCE (ADAPTED FROM [1]).
FIGURE 2:
FIGURE 2:
HYBRID MODEL OF CALVERT WITH THE PERL MODEL NESTED FOR FITNESS AND FATIGUE AND WEARBLE SENSOR DATA USED TO COMPUTE TRAINING LOAD (Adapted from [1],[5]).
FIGURE 3:
FIGURE 3:
SAMPLE PLOT OF HEART RATE (HR, BPM) AND MUSCLE OXYGENATION FOR SENSOR LOCTED ON THE VASTUS LATERALIS MUSCLE (PERCENT O2)

References

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