Identification of motion outliers in wrist pulse signal

Garg, Nidhi; Bisht, Amandeep; Ryait, Hardeep Singh; Kumar, Amod

doi:10.1016/j.compeleceng.2018.03.001

Cited by 11 publications

(3 citation statements)

References 5 publications

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“…The hardware encompasses Electrodes [8,9], ProComp Infiniti system [13,24], BIOPAC MP150 [15,22], Kendal media Trace 500 [19] etc were used in prior works. The data collected is fed to software for processing in Matlab, Labview [19,33] signals [36,37]. The rationale behind these artifacts is the baseline wander (respiration), low-frequency noise (subject movement) wrongly placed electrode, high-frequency noise from devices, noise due to 50 Hz power line interference, white Gaussian noise.…”

Section: B Data Acquisitionmentioning

confidence: 99%

An Emotion Recognition Based on Physiological Signals

2019

IJITEE

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Emotion recognition is alluring considerable interest among the researchers. Emotions are discovered by facial, speech, gesture, posture and physiological signals. Physiological signals are a plausible mechanism to recognize emotion using human-computer interaction. The objective of this paper is to put forth the recognition of emotions using physiological signals. Various emotion elicitation protocols, feature extraction techniques, classification methods that aim at recognizing emotions from physiological signals are discussed here. Wrist Pulse Signal is also discussed to fill the lacunae of the other physiological signal for emotion detection. Working on basic as well as non-basic human emotion and human-computer interface will make the system robust.

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Section: B Data Acquisitionmentioning

confidence: 99%

An Emotion Recognition Based on Physiological Signals

2019

IJITEE

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“…They will perceive the pulse morphology to determine the states of different organs by applying vary pressure simultaneously or sequentially on the three regions and record the change of the required pulse wave. The trained practitioners can gather elaborate physiological and pathological information on the cardiovascular system, organ functions, patients'constitution, behavioral patterns, and previous illness, as well as body's homeostatic balance by diagnosing the condition of pulse [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The Correlation Study of Cun, Guan and Chi Position Based on Wrist Pulse Characteristics

et al. 2021

IEEE Access

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To analyze the correlation of pulse characteristics at different acquisition positions, the pulse parameters that can identify different pulse and physiological information are given. In this paper, the relevance and difference of wrist pulse characteristics at Cun, Guan and Chi position were studied by pulse diagnosis instrument and time-frequency, frequency-domain and time-frequency domain analysis methods etc. Firstly, the pulse information of Cun, Guan and Chi position is collected from 206 healthy adults. The 66 pulse characteristics of each sampled location are extracted. In addition, the 66 feature indexes of each position are used to calculate the Spearman correlation coefficients among Cun, Guan, Chi position as well as the both hands in detail. Finally, the relevance and difference of wrist pulse at Cun, Guan and Chi position were studied and analyzed by significance test. The pulse characteristics with high correlation at Cun-Guan-Chi position of left and right hands are mainly power spectrum characteristics and time-frequency characteristics, while the features with high correlation between both hands mostly concentrate in the time domain characteristics. This work has studied the relationship between the position of pulse acquisition from quantitative and qualitative aspects, and laid a foundation for the objective diagnosis of pulse condition and the comprehensive disclosure of pulse information.

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“…In the literature, WPS can be utilized for various applications, for instance, pre-meal and post-meal classification [4], physical exercise [5], diabetes classification [6], hypertension association [7,8], lung cancer recognition [9], and inflammation classification [10,11]. Various signal processing techniques on WPS can be found in [12][13][14], for instance, dynamic time warping, wavelet analysis, periodic decomposition, principal component analysis, and linear discriminant analysis.…”

Section: Introductionmentioning

confidence: 99%

A Novel MOGA-SVM Multinomial Classification for Organ Inflammation Detection

Chui

Lytras

2019

Applied Sciences

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Wrist pulse signal (WPS) contains crucial information of humans’ health condition. It can serve as an alternative method for diagnosing of organ inflammation instead of traditional clinical measurement. In this paper, a novel multi-objective genetic algorithm based support vector machine (MOGA-SVM) has been proposed for the multinomial classification of the inflammations of appendix, pancreas, and duodenum. A customized similarity kernel (KCS) has been optimally designed. The performance of multinomial classification using KCS is compared with five types of kernels, linear, radial basis function (RBF), polynomial and sigmoid kernel, as well as mixtures of polynomial and RBF, to verify the effectiveness of KCS. The sensitivity, specificity and accuracy (Acc) of the proposed method are 92%, 91.2%, and 91.6% respectively. The results have demonstrated that KCS improves the accuracy of classification from 8.9% to 59.6%. When compared to related work, the proposed method increases the performance by more than 10%. It is believed that WPS can serve as alternative measures to diagnose organ inflammations.

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Identification of motion outliers in wrist pulse signal

Cited by 11 publications

References 5 publications

An Emotion Recognition Based on Physiological Signals

An Emotion Recognition Based on Physiological Signals

The Correlation Study of Cun, Guan and Chi Position Based on Wrist Pulse Characteristics

A Novel MOGA-SVM Multinomial Classification for Organ Inflammation Detection

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