  We describe a novel metric-based learning approach that introduces a
multimodal framework and uses deep audio and geophone encoders in siamese
configuration to design an adaptable and lightweight supervised model. This
framework eliminates the need for expensive data labeling procedures and learns
general-purpose representations from low multisensory data obtained from
omnipresent sensing systems. These sensing systems provide numerous
applications and various use cases in activity recognition tasks. Here, we
intend to explore the human footstep movements from indoor environments and
analyze representations from a small self-collected dataset of acoustic and
vibration-based sensors. The core idea is to learn plausible similarities
between two sensory traits and combining representations from audio and
geophone signals. We present a generalized framework to learn embeddings from
temporal and spatial features extracted from audio and geophone signals. We
then extract the representations in a shared space to maximize the learning of
a compatibility function between acoustic and geophone features. This, in turn,
can be used effectively to carry out a classification task from the learned
model, as demonstrated by assigning high similarity to the pairs with a human
footstep movement and lower similarity to pairs containing no footstep
movement. Performance analyses show that our proposed multimodal framework
achieves a 19.99\% accuracy increase (in absolute terms) and avoided
overfitting on the evaluation set when the training samples were increased from
200 pairs to just 500 pairs while satisfactorily learning the audio and
geophone representations. Our results employ a metric-based contrastive
learning approach for multi-sensor data to mitigate the impact of data scarcity
and perform human movement identification with limited data size.
