Using the eSense Wearable Earbud as a Light-Weight Robot Arm Controller

Odoemelem, Henry; Hölzemann, Alexander; Laerhoven, Kristof Van

doi:10.1145/3345615.3361138

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…Work on fundamental principles creates the foundation of the research space. This includes e.g., characterization of wearing variability [27] and understanding earables as input modality, e.g., to control a robot arm [31]. Community initiatives include approaches for secure earable data sharing platforms [34], and also sensor extensions such as a magnetometer [13].…”

Section: Design Space Of Earable Prototypingmentioning

confidence: 99%

Design space and usability of earable prototyping

Röddiger

Beigl

Exler

2020

Proceedings of the 2020 International Symposium on Wearable Computers

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Earable computing gains growing attention within research and becomes ubiquitous in society. However, there is an emerging need for prototyping devices as critical drivers of innovation. In our work, we reviewed the features of existing earable platforms. Based on 24 publications, we characterized the design space of earable prototyping. We used the open eSense platform (6-axis IMU, auditory I/O) to evaluate the problem-based learning usability of non-experts. We collected data from 79 undergraduate students who developed 39 projects. Our questionnaire-based results suggest that the platform creates interest in the subject matter and supports self-directed learning. The projects align with the research space, indicating ease of use, but lack contributions for more challenging topics. Additionally, many projects included games not present in current research. The average SUS score of the platform was 67.0. The majority of problems are technical issues (e.g., connecting, playing music). CCS CONCEPTS• Human-centered computing → Empirical studies in ubiquitous and mobile computing.

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Section: Design Space Of Earable Prototypingmentioning

confidence: 99%

Design space and usability of earable prototyping

Röddiger

Beigl

Exler

2020

Proceedings of the 2020 International Symposium on Wearable Computers

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Exploring Human Activities Using eSense Earable Device

Islam

Hossain

Ahad

et al. 2020

Smart Innovation, Systems and Technologies

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Auritus

Saha

Sandha

Pei

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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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.

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Using the eSense Wearable Earbud as a Light-Weight Robot Arm Controller

Cited by 5 publications

References 2 publications

Design space and usability of earable prototyping

Design space and usability of earable prototyping

Exploring Human Activities Using eSense Earable Device

Auritus

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