From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

Zalewski, P.; Marchegiani, Letizia; Elsts, Atis; Piechocki, Robert J.; Craddock, Ian J; Fafoutis, Xenofon

doi:10.3390/s20061655

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…Other systems were presented and discussed in our previous paper [15] (see Discussion). The sensor appears to fit the wearable sensing paradigm, discussed in [35]. We tested several placements of the sensor, e.g., in different places of the neck, on chest (upper sternum and middle sternum), and based on the signal-to-noise ratio, suprasternal notch appeared the best place to put the device.…”

Section: Discussionmentioning

confidence: 99%

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

2020

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This study aims to evaluate the accuracy of a wireless tracheal audio sensor including the sleep body position measurement, as a possible tool to screen for obstructive sleep apnea (OSA) and also to distinguish among "positional" and "non-positional" sleep apnea patients. 30 adult subjects were asked to sleep naturally for a single night in the sleep laboratory, having simultaneous polysomnography (PSG) as a reference. The results were compared using, e.g., Pearson's and Lin's correlation coefficients, Bland-Altman plots, mean absolute error, intercept of the fitted linear model etc. We found the thresholding approach performed on normalized audio energy data achieved mean absolute error of 5.7, and average difference was −2.0. Pearson's and Lin's R coefficients were 0.92 and 0.70, respectively. The qualitative approach reached 86% accuracy (sensitivity of 96%, and specificity of 76%) when setting a binary border at the level of AHI=15 (combined normal + mild versus combined moderate + severe cases). We also checked the distribution of apneas depending on the body position. Our sensor showed a mean ± SE difference between supine apnea index and non-supine apnea index of 8.3 ± 3.2, while PSG reference of 11.2 ± 3.8. The proposed sensor might be a good complement for home sleep studies, being less disturbing and allowing for longitudinal observations and reliably showing positional OSA. PSG is the gold standard in diagnosing OSA; however, it requires to spend a night in a lab with medical staff, it provides short observation, the cost of the study is very high, and it is less suitable for children. This is why a reliable screening method is needed in sleep medicine. INDEX TERMS Obstructive sleep apnea, apnea-hypopnea index, home sleep study, tracheal sounds, audio processing, supine sleep, non-supine sleep

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Section: Discussionmentioning

confidence: 99%

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

2020

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“…In WBAN, it has been shown that the on-board extraction of features on modern low-power wearables is both feasible and beneficial for system lifetime improvement [127]. Although a resource-constrained sensing system needs to strike the balance between the accuracy of the semantic features output and the cost of analyzing the data for extraction, the benefits from reducing the radio duty cycle which is used for transmission, vastly outweigh the cost of increasing the processor duty cycle which is used for semantic features extraction [72]. As knowledge extraction from the raw data can significantly reduce the information that needs to be transmitted, the SemCom-based method increases the lifetime of the wearable device by one order of magnitude, at the cost of approximately 5% degradation of classification accuracy [72].…”

Section: H Personalized Body Area Networkmentioning

confidence: 99%

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

Yang¹,

Du²,

Liew³

et al. 2022

Preprint

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With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and meaning of information into the data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we outline the major 6G visions and key enabler techniques which lay the foundation of SemCom. Meanwhile, we highlight some benefits of SemComempowered 6G and present a SemCom-native 6G network architecture. Next, we show the evolution of SemCom from its introduction to classical SemCom related theory and modern AIenabled SemCom. Following that, focusing on modern SemCom, we classify SemCom into three categories, i.e., semantic-oriented communication, goal-oriented communication, and semanticaware communication, and introduce three types of semantic metrics. We then discuss the applications, the challenges and technologies related to semantics and communication. Finally, we introduce future research opportunities. In a nutshell, this paper investigates the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues for further direction.

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“…Once it detects these keywords, it sends the remaining speech to a larger model in the cloud to further process the request [32]. While the idea of cascading architectures is usually restricted to model size and alternative systems, research has looked into a cascading use of internal hardware in a system [33].…”

Section: Model Optimisationsmentioning

confidence: 99%

A Primer for tinyML Predictive Maintenance: Input and Model Optimisation

Njor

Madsen

Fafoutis

2022

IFIP Advances in Information and Communication Technology

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In this paper, we investigate techniques used to optimise tinyML based Predictive Maintenance (PdM). We first describe PdM and tinyML and how they can provide an alternative to cloud-based PdM. We present the background behind deploying PdM using tinyML, including commonly used libraries, hardware, datasets and models. Furthermore, we show known techniques for optimising tinyML models. We argue that an optimisation of the entire tinyML pipeline, not just the actual models, is required to deploy tinyML based PdM in an industrial setting. To provide an example, we create a tinyML model and provide early results of optimising the input given to the model.

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From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

Cited by 9 publications

References 34 publications

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

Semantic Communications for 6G Future Internet: Fundamentals, Applications, and Challenges

A Primer for tinyML Predictive Maintenance: Input and Model Optimisation

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