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Found 8 papers, 3 papers with code
Forearm Ultrasound based Gesture Recognition on Edge
To bridge this gap, this paper explores the deployment of deep neural networks for forearm ultrasound-based hand gesture recognition on edge devices.
Sound Tagging in Infant-centric Home Soundscapes
Further, many of these works ignore infants or young children in the environment or have data collected from only a single family where noise from the fixed sound source can be moderate at the infant's position or vice versa.
LLaSA: Large Multimodal Agent for Human Activity Analysis Through Wearable Sensors
Integrating inertial measurement units (IMUs) with large language models (LLMs) advances multimodal AI by enhancing human activity understanding.
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.
Zygarde: Time-Sensitive On-Device Deep Inference and Adaptation on Intermittently-Powered Systems
We propose Zygarde -- which is an energy -- and accuracy-aware soft real-time task scheduling framework for batteryless systems that flexibly execute deep learning tasks1 that are suitable for running on microcontrollers.
Intermittent Learning: On-Device Machine Learning on Intermittently Powered System
This paper introduces intermittent learning - the goal of which is to enable energy harvested computing platforms capable of executing certain classes of machine learning tasks effectively and efficiently.
