ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2022
Auritus: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- LocoMote: AI-driven Sensor Tags for Fine-Grained Undersea Localization and Sensing.IEEE sensors journal · 2024Article
- TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning.ACM transactions on embedded computing systems : TECS · 2024Article
- Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges.IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium · 2023Article
- Machine Learning for Microcontroller-Class Hardware: A Review.IEEE sensors journal · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Smart ear-worn devices (called earables) are being equipped with various onboard sensors and algorithms, transforming earphones from simple audio transducers to multi-modal interfaces making rich inferences about human motion and vital signals. However, developing sensory applications using earables is currently quite cumbersome with several barriers in the way. First, time-series data from earable sensors incorporate information about physical phenomena in complex settings, requiring machine-learning (ML) models learned from large-scale labeled data. This is challenging in the context of earables because large-scale open-source datasets are missing. Secondly, the small size and compute constraints of earable devices make on-device integration of many existing algorithms for tasks such as human activity and head-pose estimation difficult. To address these challenges, we introduce Auritus an extendable and open-source optimization toolkit designed to enhance and replicate earable applications. Auritus serves two primary functions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.