  Commercial Wi-Fi devices can be used for integrated sensing and
communications (ISAC) to jointly exchange data and monitor indoor environment.
In this paper, we investigate a proof-of-concept approach using automated
quantum machine learning (AutoQML) framework called AutoAnsatz to recognize
human gesture. We address how to efficiently design quantum circuits to
configure quantum neural networks (QNN). The effectiveness of AutoQML is
validated by an in-house experiment for human pose recognition, achieving
state-of-the-art performance greater than 80% accuracy for a limited data size
with a significantly small number of trainable parameters.
