  Oxygen consumption (VO$_2$) provides established clinical and physiological
indicators of cardiorespiratory function and exercise capacity. However, VO$_2$
monitoring is largely limited to specialized laboratory settings, making its
widespread monitoring elusive. Here, we investigate temporal prediction of
VO$_2$ from wearable sensors during cycle ergometer exercise using a temporal
convolutional network (TCN). Cardiorespiratory signals were acquired from a
smart shirt with integrated textile sensors alongside ground-truth VO$_2$ from
a metabolic system on twenty-two young healthy adults. Participants performed
one ramp-incremental and three pseudorandom binary sequence exercise protocols
to assess a range of VO$_2$ dynamics. A TCN model was developed using causal
convolutions across an effective history length to model the time-dependent
nature of VO$_2$. Optimal history length was determined through minimum
validation loss across hyperparameter values. The best performing model encoded
218 s history length (TCN-VO$_2$ A), with 187 s, 97 s, and 76 s yielding less
than 3% deviation from the optimal validation loss. TCN-VO$_2$ A showed strong
prediction accuracy (mean, 95% CI) across all exercise intensities (-22
ml.min$^{-1}$, [-262, 218]), spanning transitions from low-moderate (-23
ml.min$^{-1}$, [-250, 204]), low-high (14 ml.min$^{-1}$, [-252, 280]),
ventilatory threshold-high (-49 ml.min$^{-1}$, [-274, 176]), and maximal (-32
ml.min$^{-1}$, [-261, 197]) exercise. Second-by-second classification of
physical activity across 16090 s of predicted VO$_2$ was able to discern
between vigorous, moderate, and light activity with high accuracy (94.1%). This
system enables quantitative aerobic activity monitoring in non-laboratory
settings across a range of exercise intensities using wearable sensors for
monitoring exercise prescription adherence and personal fitness.
