Using an in-Ear Wearable to Annotate Activity Data across Multiple Inertial Sensors

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

doi:10.1145/3345615.3361136

Cited by 18 publications

(15 citation statements)

References 9 publications

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“…Our between-subject re-evaluation of known algorithms on a broader depth and rate range dataset allowed us to find ideal tuning parameters and identify the Bandpass algorithm as the most reliable approach. Our initial results regarding the popular earable platform are particularly encouraging, given their broad availability, and high user acceptance [9]. Though not mandatory, fusing sensors reduced the error further and outperformed existing rescuer-centered principles.…”

Section: Discussionmentioning

confidence: 83%

Cardiopulmonary Resuscitation Support: Comparison of Wrist-, Chest-, and Ear-Worn Devices and Estimation Algorithms

Hermann

Breitling

Röddiger

et al. 2021

2021 International Symposium on Wearable Computers

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Chest compressions (CC) are the most important means of treating cardiac arrest but are challenging to perform. Real-time feedback of depth and rate can improve CC quality. For the first time, we compared three wearable positions and six depth, and rate estimation algorithms under the same conditions. We share their optimal tuning parameters. Our evaluation of earables results in a new prime candidate for high-quality cardiopulmonary resuscitation (CPR) feedback. For depth estimation on chest, wrist, and ear the median absolute deviation (MAD) was 3.4 mm, 4.5 mm, and 5.9 mm, respectively (target depth range: 50-60 mm). Though not necessary for effective CC, fusing sensor locations reduces the depth MAD further to 3.2 mm. CC rate was estimated at less than 1.6 compressions per minute (cpm) MAD in all configurations. Hence, all wearables and algorithms give precise input for live-saving CPR. CCS CONCEPTS• Human-centered computing → Empirical studies in ubiquitous and mobile computing; Ubiquitous and mobile devices.

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Section: Discussionmentioning

confidence: 83%

Cardiopulmonary Resuscitation Support: Comparison of Wrist-, Chest-, and Ear-Worn Devices and Estimation Algorithms

Hermann

Breitling

Röddiger

et al. 2021

2021 International Symposium on Wearable Computers

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“…Earable devices were used to monitor multiple diseases and health conditions in 16% (8/51) of the studies, namely, brain, cardiac, and respiratory functions [ 65 ]; cardiovascular status, sweating, and motion [ 66 ]; HR and breathing rate [ 67 ]; respiration and posture [ 21 ]; metabolic functions in relation to fever, insomnia, fatigue, and depression [ 22 ]; gait classification [ 68 ]; cardiovascular, metabolic, and mental disorders, including stress and pain response [ 69 ]; and chronic stress, cognitive dysfunctions, depression, and CVD [ 70 ]. In another 12% (6/51) of the studies, earable devices were used to monitor various aspects of health status, namely, thermoregulation [ 71 ], fertility [ 72 ], heat stress [ 73 ], tongue movements [ 18 ], facial expressions [ 74 ], and physical activity [ 75 ].…”

Section: Resultsmentioning

confidence: 99%

Health-Related Indicators Measured Using Earable Devices: Systematic Review

Choi¹,

Jeon²,

Kim³

et al. 2022

JMIR Mhealth Uhealth

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Background Earable devices are novel, wearable Internet of Things devices that are user-friendly and have potential applications in mobile health care. The position of the ear is advantageous for assessing vital status and detecting diseases through reliable and comfortable sensing devices. Objective Our study aimed to review the utility of health-related indicators derived from earable devices and propose an improved definition of disease prevention. We also proposed future directions for research on the health care applications of earable devices. Methods A systematic review was conducted of the PubMed, Embase, and Web of Science databases. Keywords were used to identify studies on earable devices published between 2015 and 2020. The earable devices were described in terms of target health outcomes, biomarkers, sensor types and positions, and their utility for disease prevention. Results A total of 51 articles met the inclusion criteria and were reviewed, and the frequency of 5 health-related characteristics of earable devices was described. The most frequent target health outcomes were diet-related outcomes (9/51, 18%), brain status (7/51, 14%), and cardiovascular disease (CVD) and central nervous system disease (5/51, 10% each). The most frequent biomarkers were electroencephalography (11/51, 22%), body movements (6/51, 12%), and body temperature (5/51, 10%). As for sensor types and sensor positions, electrical sensors (19/51, 37%) and the ear canal (26/51, 51%) were the most common, respectively. Moreover, the most frequent prevention stages were secondary prevention (35/51, 69%), primary prevention (12/51, 24%), and tertiary prevention (4/51, 8%). Combinations of ≥2 target health outcomes were the most frequent in secondary prevention (8/35, 23%) followed by brain status and CVD (5/35, 14% each) and by central nervous system disease and head injury (4/35, 11% each). Conclusions Earable devices can provide biomarkers for various health outcomes. Brain status, healthy diet status, and CVDs were the most frequently targeted outcomes among the studies. Earable devices were mostly used for secondary prevention via monitoring of health or disease status. The potential utility of earable devices for primary and tertiary prevention needs to be investigated further. Earable devices connected to smartphones or tablets through cloud servers will guarantee user access to personal health information and facilitate comfortable wearing.

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“…Instead of explicit synchronization actions, signal couplings, or context markers, Hölzemann et al [9] utilize the uniqueness of variations present in accelerometer signals from inertial measurement units (IMU) to align the independently recorded time series. The alignment, with an accuracy in the order of seconds, requires sufficient periods of resting with low variations as well as similarities and accordance in the simultaneous measurements at diverse locations such as head and wrist.…”

Section: A Present Synchronization Methodsmentioning

confidence: 99%

PulSync: The Heart Rate Variability as a Unique Fingerprint for the Alignment of Sensor Data Across Multiple Wearable Devices

Wolling

Laerhoven

Siirtola

et al. 2021

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

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Most off-the-shelf wearable devices do not provide reliable synchronization interfaces, causing multi-device sensing and machine learning approaches, e.g. for activity recognition, still to suffer from inaccurate clock sources and unmatched time. Instead of using active online synchronization techniques, such as those based on bidirectional wireless communication, we propose in this work to use the human heartbeat as a reference signal that is continuously and ubiquitously available throughout the entire body surface. We introduce PulSync, a novel approach that enables the alignment of sensor data across multiple devices utilizing the unique fingerprint-like character of the heart rate variability interval function. In an evaluation on a dataset from 25 subjects, we demonstrate the reliable alignment of independent ECG recordings with a mean accuracy of −0.71 ± 3.44 samples, respectively −2.86 ± 11.43 ms at 250 Hz sampling rate.

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Using an in-Ear Wearable to Annotate Activity Data across Multiple Inertial Sensors

Cited by 18 publications

References 9 publications

Cardiopulmonary Resuscitation Support: Comparison of Wrist-, Chest-, and Ear-Worn Devices and Estimation Algorithms

Cardiopulmonary Resuscitation Support: Comparison of Wrist-, Chest-, and Ear-Worn Devices and Estimation Algorithms

Health-Related Indicators Measured Using Earable Devices: Systematic Review

PulSync: The Heart Rate Variability as a Unique Fingerprint for the Alignment of Sensor Data Across Multiple Wearable Devices

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