The mice employed in pre-clinical research studies lived in dedicated, furnished cages. In recent years, innovative intelligent video monitoring technologies, based on machine learning, among other techniques, have been effectively deployed inside those cages. Unfortunately, little attention has been paid to investigating the correlation between different sensing modalities for movement detection and behavioral analysis in low-cost HCM systems. To fill such a gap, this work introduces an audio-based analysis technique. Audio data have been collected to study the mice’s behavior over time. 24/7 acquisitions have been performed to sample different types of interactions. Furthermore, a capacitive plate has been installed at the bottom of the cage to capture the mice’s motion activity. The aim of this work is to use audio acquisitions and associated analysis to validate and compare them against the insights gained by using the capacitive plate, with the goal of finding correlations between the information provided by the two sensing technologies. The experiments have been conducted on three cages installed in the Francis Crick in London facility and on seven cages hosted at the Consiglio Nazionale delle Ricerche Institute in Rome. The results demonstrated that the capacitive-based analysis measured animal activity in a comparable manner to that measured with the audio analysis system, yielding an average of 83% of linear correlation and 81.33% of monotonicity correlation on the three cages in London. The other seven cages in Rome instead showed a linear correlation of 92.15% and a monotonicity relationship of 92.25% on the 15-min time slot granularity considered for the measurement.