Auritus: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables

Swapnil Sayan Saha, Sandeep Singh Sandha, Siyou Pei, Vivek Jain, Ziqi Wang, Yuchen Li, Ankur Sarker, Mani Srivastava

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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. Firstly, Auritus handles data collection, pre-processing, and labeling tasks for creating customized earable datasets using graphical tools. The system includes an open-source dataset with 2.43 million inertial samples related to head and full-body movements, consisting of 34 head poses and 9 activities from 45 volunteers. Secondly, Auritus provides a tightly-integrated hardware-in-The-loop (HIL) optimizer and TinyML interface to develop lightweight and real-Time machine-learning (ML) models for activity detection and filters for head-pose tracking. To validate the utlity of Auritus, we showcase three sample applications, namely fall detection, spatial audio rendering, and augmented reality (AR) interfacing. Auritus recognizes activities with 91% leave 1-out test accuracy (98% test accuracy) using real-Time models as small as 6-13 kB. Our models are 98-740x smaller and 3-6% more accurate over the state-of-The-Art. We also estimate head pose with absolute errors as low as 5 degrees using 20kB filters, achieving up to 1.6x precision improvement over existing techniques. We make the entire system open-source so that researchers and developers can contribute to any layer of the system or rapidly prototype their applications using our dataset and algorithms.
Original languageEnglish (US)
Pages (from-to)1-34
Number of pages34
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume6
Issue number2
DOIs
StatePublished - Jul 7 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: The research reported in this paper was sponsored in part by: the CONIX Research Center, one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA; by the IoBT REIGN Collaborative Research Alliance funded by the Army Research Laboratory (ARL) under Cooperative Agreement W911NF-17-2-0196; by the NIH mHealth Center for Discovery, Optimization and Translation of Temporally-Precise Interventions (mDOT) under award 1P41EB028242; by the National Science Foundation (NSF) under awards # OAC-1640813 and CNS-1822935; and, by and the King Abdullah University of Science and Technology (KAUST) through its Sensor Innovation research program.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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