Hearables: New Perspectives and Pitfalls of In-Ear Devices for Physiological Monitoring. A Scoping Review

Masè, Michela; Micarelli, Alessandro; Strapazzon, Giacomo

doi:10.3389/fphys.2020.568886

Cited by 27 publications

(21 citation statements)

References 88 publications

(201 reference statements)

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“…Mao et al [12] aimed at the current application status of wearable technology in the field of rehabilitation and discussed the possibility of wearable devices in the field in the future and the main problems. Masè and Micarelli [13], based on the concept of a wearable computer, proposed the concept of a wearable wireless network and built a wearable wireless network model using Bluetooth and Zigbee technology. Zhang et al [14] proposed a wireless sensor network using a wearable computer as an intelligent interface, elaborated the architecture of the wireless sensor network, and proved the feasibility of the wireless sensor network.…”

Section: Introductionmentioning

confidence: 99%

Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

Zhang³

2021

Journal of Sensors

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According to the development needs of wireless sensor networks, this paper uses the combination of embedded system and wireless sensor network technology to design a network node platform. This platform is equipped with a sports training sensor module to measure the physiological indicators of the ward in real time. The network node sends the collected physiological parameters to a remote monitoring center in real time. First, according to the generation mechanism of the physiological index signal and the characteristics of the physiological index signal, the wireless sensor network analysis and processing method are used to denoise the physiological index signal, and the wireless sensor network package is used to extract the characteristics of the physiological index, indicating different types of respiration. The energy characteristics of the sound physiological index signals are different, which verifies the feasibility of the independent component analysis method for separating the physiological index and the physiological index signal of the heart sound. Secondly, the hardware system of physiological index signal acquisition is designed, and the selection principle of the hardware unit is introduced. At the same time, the system structure of the monitor is designed, and then, the wireless sensor network sensor node is researched, the hardware of the wearable monitor system is designed, and the hardware architecture and working mode based on the single-chip MSP430F149 are given. Finally, the wireless hardware platform includes the following main modules: sensor part, preprocessing circuit module, microprocessing module based on MSP430 low power consumption, wireless transceiver module based on RF chip CC2420, and power supply unit used to provide energy.

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Section: Introductionmentioning

confidence: 99%

Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

Zhang³

2021

Journal of Sensors

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“…Today, some headsets use infrared LED constellations, external cameras, and Simultaneous Location And Mapping (SLAM) for translational tracking. Besides, the user's physiological parameters such as heart rate, oxygen saturation, and body temperature can also be tracked by hearables equipped with biosensing hardware such as LEDs, photodetectors, and thermocouples [56]. The physiological data can be used to estimate user's emotional states and subjective preferences, which can, in turn, be harvested to personalize the most preferred AR/MR audio experiences [2].…”

Section: User Activity and State Sensingmentioning

confidence: 99%

Augmented/Mixed Reality Audio for Hearables: Sensing, control, and rendering

Gupta

Ranjan

et al. 2022

IEEE Signal Process. Mag.

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UGMENTED OR MIXED REALITY (AR/MR) is emerging as one of the key technologies in the future of computing. Audio cues are critical for maintaining a high degree of realism, social connection, and spatial awareness for various AR/MR applications such as teaching and training, gaming, remote work, education, and virtual social gatherings. Motivated by a wide variety of AR/MR listening experiences delivered over hearables, this feature article systematically reviews the integration of fundamental and advanced signal processing techniques for AR/MR audio to equip the researchers and engineers in the signal processing community for the next wave of AR/MR. I. Introduction: AR/MR audio experience over hearablesAlternate reality technologies aim to provide a feeling of presence to humans through engaging multi-modal content.We define AR/MR as an alternate reality experience achieved by the seamless fusion of the reproduced virtual content with the real-world stimuli that can be modified as desired. This definition expands on AR's previous usage, where the experiences provided were limited to the overlay of virtual content in the real world. It can also include other alternate reality technologies, such as virtual reality (VR), where users can get transported to a virtual environment.The immersive AR/MR technologies have demonstrated substantial benefits in various applications such as education and training, tourism, and remote working. We have seen significant progress in rendering AR/MR devices' different modalities in the past few decades. In particular, the new lifestyle of reduced physical connection triggered by the COVID-19 pandemic has made such needs even more critical than ever before. This paper focuses on sound (or audio), an inherent part of our everyday lives for communication, social interactions, and situational awareness. Unlike vision, where the field of view is limited, natural listening always spans a 360°range.The pervasive spatial perception of sound can be critical in a wide variety of high-stress situations, such as warnings for approaching vehicles or public announcements in case of emergencies where the visual cues may not be enough.Even in day-to-day cases, such as conversations among a group of people, we rely on audio cues to direct our attention towards a particular speaker, referred to as the cocktail party effect.Audio devices today can be broadly classified into two major categories: speakers and headphones. Speakers are widely used to playback audio content for multimedia applications, where a fixed configuration of speakers can provide the

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“…As a significant limitation, these sites display some degree of invasivity, confining their use to specific clinical situations. To address these problem, other places, such as the ear canal and the skin surface, have been proposed, which may allow non-invasive, indirect estimation of CBT (Brinnel and Cabanac, 1989;Gallimore, 2004;Gunga et al, 2008;Kimberger et al, 2013;Strapazzon et al, 2014;Asadian et al, 2016;Masè et al, 2020). Methods for indirect CBT determination from the skin surface have gained attention thanks to non-invasivity, easy probe positioning, and small size.…”

Section: Introductionmentioning

confidence: 99%

Low Ambient Temperature Exposition Impairs the Accuracy of a Non-invasive Heat-Flux Thermometer

et al. 2022

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BackgroundIndirect core body temperature (CBT) monitoring from skin sensors is gaining attention for in-field applications thanks to non-invasivity, portability, and easy probe positioning. Among skin sensors, heat-flux devices, such as the so-called Double Sensor (DS), have demonstrated reliability under various experimental and clinical conditions. Still, their accuracy at low ambient temperatures is unknown. In this randomized cross-over trial, we tested the effects of cold temperature exposition on DS performance in tracking CBT.MethodsTwenty-one participants were exposed to a warm (23.2 ± 0.4°C) and cold (−18.7 ± 1.0°C) room condition for 10 min, following a randomized cross-over design. The accuracy of the DS to estimate CBT in both settings was assessed by quantitative comparison with esophageal (reference) and tympanic (comparator) thermometers, using Bland–Altman and correlation analyses (Pearson’s correlation coefficient, r, and Lin’s concordance correlation coefficient, CCC).ResultsIn the warm room setting, the DS showed a moderate agreement with the esophageal sensor [bias = 0.09 (−1.51; 1.69) °C, r = 0.40 (p = 0.069), CCC = 0.22 (−0.006; 0.43)] and tympanic sensor [bias = 2.74 (1.13; 4.35) °C, r = 0.54 (p < 0.05), CCC = 0.09 (0.008; 0.16)]. DS accuracy significantly deteriorated in the cold room setting, where DS temperature overestimated esophageal temperature [bias = 2.16 (−0.89; 5.22) °C, r = 0.02 (0.94), CCC = 0.002 (−0.05; 0.06)]. Previous exposition to the cold influenced temperature values measured by the DS in the warm room setting, where significant differences (p < 0.00001) in DS temperature were observed between randomization groups.ConclusionDS accuracy is influenced by environmental conditions and previous exposure to cold settings. These results suggest the present inadequacy of the DS device for in-field applications in low-temperature environments and advocate further technological advancements and proper sensor insulation to improve performance in these conditions.

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Hearables: New Perspectives and Pitfalls of In-Ear Devices for Physiological Monitoring. A Scoping Review

Cited by 27 publications

References 88 publications

Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

Augmented/Mixed Reality Audio for Hearables: Sensing, control, and rendering

Low Ambient Temperature Exposition Impairs the Accuracy of a Non-invasive Heat-Flux Thermometer

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