PilotEar: Enabling In-ear Inertial Navigation

Ahuja, Ashwin; Ferlini, Andrea; Mascolo, Cecilia

doi:10.1145/3460418.3479326

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…Given the widely usage of earphones, earables have gained tremendous attention for human sensing, such as blood flow [29], blood pressure [9], hear rate [11], dietary monitoring based on swallow [4], navigation [2], facial gestures detection [3], and authentication [18,20,52]…”

Section: In-ear Sensingmentioning

confidence: 99%

EarHealth

Jin

Gao²,

Guo³

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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With the aging of the population and the long-time wearing of earphones, hearing health has gradually emerged as a worldwide health issue. Early detection of hearing health conditions would greatly reduce potential risks with timely medical intervention. This study proposes an earphone-based ear condition monitoring system, named EarHealth, which is low-cost, non-invasive, and easily usable in daily life. It can detect three major hearing health conditions: ruptured eardrum, earwax buildup and blockage, and otitis media. By analyzing the recorded echoes evoked by a chirp sound stimulus, EarHealth recognizes the distinguishable characteristics from ear canal structure and eardrum mobility. EarHealth achieves an accuracy of 82.6% in 92 human subjects, including 27 normal subjects, 22 patients with ruptured eardrum, 25 patients with otitis media, and 18 patients with earwax blockage. EarHealth is the first earphone-based system capable of monitoring hearing health conditions by utilizing the ear canal geometry and eardrum mobility. It is anticipated that EarHealth would provide pervasive and proactive protection for hearing health. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing.

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Section: In-ear Sensingmentioning

confidence: 99%

EarHealth

Jin

Gao²,

Guo³

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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“…This earworn sensing platform uses Arduino Nano 33 BLE Sense as microcontroller (MCU) and collects motion from the 9-axis IMU for earable prototype and iphone. The PilotEar achieved the drift of 0.11𝑚 when fusing both earables (7).…”

Section: Related Studiesmentioning

confidence: 99%

Sensor Based Wearable Devices for Road Navigation

Tyagi¹,

Singh

2022

ECS Trans.

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Internet of Things (IoT)-based applications play a significant role and are highly influenced by the global positioning system (GPS). GPS has a wide range of applications and benefits in tracking and route finding. A GPS tracker can help to explore numerous possibilities in various domains such as the health field, safety (to protect various assets), pattern recognition, logistics, in business models by using navigation systems embedded with sensors, and many more. Smart IoT wearable devices are the next step of evolution in the field of navigation using visual, auditory, ultrasonic, and other technologies. This paper introduces the various IoT sensors used in navigation systems and some prominent existing applications. This paper also provides a survey of existing wearable devices and concludes that the correct choice of sensors can easily overcome the challenges of navigation.

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“…Despite being widely used in the literature (Ahuja et al, 2021;A ´rvai and Homolya, 2020;Hanafi et al, 2019;Wang et al, 2022), this conventional method will be discussed in this paper to highlight its limitations, particularly for large tilt angles and when the IMU is pointing toward the east and west directions.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network for tilt compensation in yaw estimation

Halitim,

Bouhedda,

Tchoketch-Kebir

et al. 2023

Transactions of the Institute of Measurement and Control

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Low-cost inertial measurement units (IMUs) are commonly used to determine the orientation of objects, such as unmanned aerial vehicles (UAVs) and smartphones. They calculate yaw by measuring Earth’s magnetic field’s horizontal components. However, in the presence of tilt (pitch or roll), a tilt-compensation operation is necessary. This is usually done by projecting measurements onto a horizontal plane. This method has limitations, particularly for large tilt angles and when the IMU is pointing toward the east or west directions. In this paper, we expose the shortcomings of this conventional approach and propose a novel machine learning–based solution employing an artificial neural network (ANN). This method eliminates the need to determine tilt angles and uses accelerometer and magnetometer measurements as its inputs. The dataset for training and testing the ANN was collected based on a 3D nonmagnetic scaled platform, using a low-cost IMU and a Raspberry Pi platform. On one hand, our method outperforms the conventional tilt-compensation technique and other complementary filters (Madgwick and Mahony) in terms of accuracy, as evidenced by the root mean square error (RMSE = 1.95°). However, this superiority comes at the expense of a more complex system that consumes more processing time.

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PilotEar: Enabling In-ear Inertial Navigation

Cited by 11 publications

References 31 publications

EarHealth

EarHealth

Sensor Based Wearable Devices for Road Navigation

Artificial neural network for tilt compensation in yaw estimation

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