Exploring Physiological Parameters in Dynamic WBAN Channels

Munoz, Max; Foster, Robert; Hao, Yang

doi:10.1109/tap.2014.2342751

“…The EE associated with the posture changes described above may show small variations in the already mentioned link lengths as the abdominal displacements occur due to cardiovascular and respiratory cycles. The maximum displacement caused by the mechanical processes is estimated to be 0.2–0.5 mm and 4–12 mm, respectively [ 34 ]. The results are shown for on-body NLOS channel, as its distances fall in mid-range and easily elaborated.…”

Section: Hybrid Cooperative Scheme For Energy Efficient Wbanmentioning

confidence: 99%

Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation

Waheed

¹

,

Ahmad

²

,

Ahmed

³

et al. 2018

Sensors

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The fabrication of lightweight, ultra-thin, low power and intelligent body-borne sensors leads to novel advances in wireless body area networks (WBANs). Depending on the placement of the nodes, it is characterized as in/on body WBAN; thus, the channel is largely affected by body posture, clothing, muscle movement, body temperature and climatic conditions. The energy resources are limited and it is not feasible to replace the sensor’s battery frequently. In order to keep the sensor in working condition, the channel resources should be reserved. The lifetime of the sensor is very crucial and it highly depends on transmission among sensor nodes and energy consumption. The reliability and energy efficiency in WBAN applications play a vital role. In this paper, the analytical expressions for energy efficiency (EE) and packet error rate (PER) are formulated for two-way relay cooperative communication. The results depict better reliability and efficiency compared to direct and one-way relay communication. The effective performance range of direct vs. cooperative communication is separated by a threshold distance. Based on EE calculations, an optimal packet size is observed that provides maximum efficiency over a certain link length. A smart and energy efficient system is articulated that utilizes all three communication modes, namely direct, one-way relay and two-way relay, as the direct link performs better for a certain range, but the cooperative communication gives better results for increased distance in terms of EE. The efficacy of the proposed hybrid scheme is also demonstrated over a practical quasi-static channel. Furthermore, link length extension and diversity is achieved by joint network-channel (JNC) coding the cooperative link.

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“…With the advancements in body-centric wireless systems, physiological monitoring has been revolutionized for improving healthcare and wellbeing of people [1][2][3][4][5][6]. These systems predominantly rely on wireless intelligent sensors that are capable of retrieving clinical information from physiological signals to interpret the progression of various ailments.…”

Section: Introductionmentioning

confidence: 99%

Deep learning framework for subject-independent emotion detection using wireless signals

Khan

¹

,

Ihalage

²

,

Ma

³

et al. 2021

Self Cite

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Emotion states recognition using wireless signals is an emerging area of research that has an impact on neuroscientific studies of human behaviour and well-being monitoring. Currently, standoff emotion detection is mostly reliant on the analysis of facial expressions and/or eye movements acquired from optical or video cameras. Meanwhile, although they have been widely accepted for recognizing human emotions from the multimodal data, machine learning approaches have been mostly restricted to subject dependent analyses which lack of generality. In this paper, we report an experimental study which collects heartbeat and breathing signals of 15 participants from radio frequency (RF) reflections off the body followed by novel noise filtering techniques. We propose a novel deep neural network (DNN) architecture based on the fusion of raw RF data and the processed RF signal for classifying and visualising various emotion states. The proposed model achieves high classification accuracy of 71.67% for independent subjects with 0.71, 0.72 and 0.71 precision, recall and F1-score values respectively. We have compared our results with those obtained from five different classical ML algorithms and it is established that deep learning offers a superior performance even with limited amount of raw RF and post processed time-sequence data. The deep learning model has also been validated by comparing our results with those from ECG signals. Our results indicate that using wireless signals for stand-by emotion state detection is a better alternative to other technologies with high accuracy and have much wider applications in future studies of behavioural sciences.

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“…With the advancements in body-centric wireless systems, physiological monitoring has been revolutionized for improving healthcare and wellbeing of people [1][2][3][4][5][6] . These systems predominantly rely on wireless intelligent sensors that are capable of retrieving clinical information from physiological signals to interpret the progression of various ailments.…”

Section: Introductionmentioning

confidence: 99%

Deep learning framework for subject-independent emotion detection using wireless signals

Khan,

Ihalage,

Ma

et al. 2020

Preprint

Self Cite

0

View full text Add to dashboard Cite

Emotion states recognition using wireless signals is an emerging area of research that has an impact on neuroscientific studies of human behaviour and well-being monitoring. Currently, standoff emotion detection is mostly reliant on the analysis of facial expressions and/or eye movements acquired from optical or video cameras. Meanwhile, although they have been widely accepted for recognizing human emotions from the multimodal data, machine learning approaches have been mostly restricted to subject dependent analyses which lack of generality. In this paper, we report an experimental study which collects heartbeat and breathing signals of 15 participants from radio frequency (RF) reflections off the body followed by novel noise filtering techniques. We propose a novel deep neural network (DNN) architecture based on the fusion of raw RF data and the processed RF signal for classifying and visualising various emotion states. The proposed model achieves high classification accuracy of 71.67% for independent subjects with 0.71, 0.72 and 0.71 precision, recall and F1-score values respectively. We have compared our results with those obtained from five different classical ML algorithms and it is established that deep learning offers a superior performance even with limited amount of raw RF and post processed time-sequence data. The deep learning model has also been validated by comparing our results with those from ECG signals. Our results indicate that using wireless signals for stand-by emotion state detection is a better alternative to other technologies with high accuracy and have much wider applications in future studies of behavioural sciences.

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Exploring Physiological Parameters in Dynamic WBAN Channels

Cited by 23 publications

References 32 publications

Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation

Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation

Deep learning framework for subject-independent emotion detection using wireless signals

Deep learning framework for subject-independent emotion detection using wireless signals

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