Thoracoabdominal Asynchrony (TAA) is a key metric in respiration monitoring, which characterizes the non-parallel periodical motion of human's rib cage (RC) and abdomen (AB) during each breath. Long-term measurement of TAA plays a significant role in respiration health tracking. Existing TAA measurement methods including Respiratory Inductive Plethysmography (RIP) and Optoelectronic Plethysmography (OEP) all intrusive to subjects and have certain requirements on operation conditions, which limit their usage to hospital scenario. To address this gap, we propose mmTAA, the first mmWave-based, non-intrusive TAA measurement system ready for ubiquitous usage in daily-life. In mmTAA, we design a Two-stage RC-AB centroid finding module, aiming to identify the most probable location of RC-AB centroid, which can best represent RC and AB in mmWave sensing scenario. Subsequently, we design TAANet, a novel Convolutional Neural Network (CNN)-based architecture with residual modules, tailored for TAA measurement. Meanwhile, in order to address the imbalance of continuous data, we add imbalance information equalizer including feature and label equalizer during network training. We implement mmTAA on a commonly used multi-antenna mmWave radar. We prototype, deploy and evaluate mmTAA on 25 subjects and 25.7h data in total. mmTAA achieves 4.01$^{\circ }$ MAE and 1.56$^{\circ }$ average error, close to OEP method.