This study examines a computational workflow that combines posture recognition and movement computing to bridge the gap in accessing digital archives that capture living knowledge and embodied experiences. By analysing and visualising such archives through bodily features, we aim to enhance archival interaction in the context of digital museology, as demonstrated through two use cases.