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Found 4 papers, 2 papers with code
LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone Sensors
In this paper, we introduce LLaSA (Large Language and Sensor Assistant), a multimodal large language model built on LIMU-BERT and Llama, designed to interpret and answer queries related to human activities and motion analysis, leveraging sensor data and contextual reasoning.
Missingness-resilient Video-enhanced Multimodal Disfluency Detection
In experiments across five disfluency-detection tasks, our unified multimodal approach significantly outperforms Audio-only unimodal methods, yielding an average absolute improvement of 10% (i. e., 10 percentage point increase) when both video and audio modalities are always available, and 7% even when video modality is missing in half of the samples.
Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems
Recent works on compression mostly focus on time and memory, but often ignore energy dynamics or significantly reduce the accuracy of pre-trained DNNs.
RecNet: Early Attention Guided Feature Recovery
Uncertainty in sensors results in corrupted input streams and hinders the performance of Deep Neural Networks (DNN), which focus on deducing information from data.
