The Challenge of Measuring Exercise: Advancing Metrological Barriers in Wearable Sensing

TL;DR

Current wearable devices struggle to accurately measure individual exercise benefits due to sensor limitations and lack of personalization.

Contribution

The paper highlights the need for advanced sensing techniques to achieve individualized and quality-based exercise measurement.

Findings

Consumer wearables often fail to capture individualized exercise effects due to sensor limitations and nonstandardized validation.

Newer sensing applications may provide novel biometric insights and bridge gaps in physiological metrology.

Improved individualized measurement could enhance understanding of exercise efficacy and clinical outcomes.

Abstract

Regular physical activity offers extensive health benefits, yet current consumer wearables struggle to accurately quantify these effects at an individualized level. Sensor performance often falls short due to susceptibility to interferences, nonstandardized validation, and reliance on indirect estimations. Further, sensors often cannot capture or account for disparities in measurement types, populations, and physiological or anatomical characteristics, nor can they account for how different exercise modalities affect results on a personalized scale. There is a drive for developers to refine the impact of how we measure the benefits of exercise, improving the usefulness of data through advanced optical modeling and spectroscopic applications. This review critically examines the shortcomings of prevailing noninvasive measurements and techniques used in common, commercially available fitness trackers and describes why it is difficult to quantify the effects of exercise as an individualized, quality-based metric. Next, we discuss newer sensing applications that attempt to curtail known limitations, some of which may unveil novel biometric insights through differentiated approaches, bridging gaps not only in technological advancement but also in physiological metrology. In conclusion, we believe that new sensing techniques should explore solutions beyond population-based statistics and aim to provide an individualized understanding of a person’s response to exercise, while also reducing disparities in personalized health monitoring. The results could lead to a more effective understanding of exercise efficacy and its impact on performance management and clinical outcomes.

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