{"doi":"10.1145/3534586","title":"Auritus","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.","journal":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","year":2022,"id":269303,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":16,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.944,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":842092,"name":"Sandeep Singh Sandha","orcid":"0000-0003-1421-1880","position":1,"is_corresponding":false},{"id":932067,"name":"Siyou Pei","orcid":"0000-0003-3802-8298","position":2,"is_corresponding":false},{"id":932068,"name":"Vivek Jain","orcid":"0000-0002-8666-0784","position":3,"is_corresponding":false},{"id":570142,"name":"Ziqi Wang","orcid":"0000-0002-0232-125X","position":4,"is_corresponding":false},{"id":932069,"name":"Yuchen Li","orcid":"0009-0008-4520-9867","position":5,"is_corresponding":false},{"id":932070,"name":"Ankur Sarker","orcid":"0000-0003-4232-3345","position":6,"is_corresponding":false},{"id":563489,"name":"Mani Srivastava","orcid":"0000-0002-3782-9192","position":7,"is_corresponding":false},{"id":842091,"name":"Swapnil Sayan Saha","orcid":"0000-0001-5357-2254","position":0,"is_corresponding":true}],"reference_count":79,"raw_metadata":null,"created_at":"2026-07-19T00:27:18.142851Z","pmid":"38515794","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}