Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare

Bai, Jinwei; Shen, Li; Sun, Haichun; Shen, Bairong

doi:10.1007/978-981-10-6041-0_2

Cited by 20 publications

(9 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(49) In addition, a number of individuals have monitored their health intensely for the express purpose of identifying signs of a health status changes, some of which might be attributable to genetic susceptibilities. (55) Table 1 lists examples of published studies exploring the utility of genetic assays in generating a diagnosis for individuals with idiopathic conditions (or what have been referred to as ‘diagnostic odysseys’) as well as published studies exploring the utility of near continuous monitoring to identify evidence for a health status change in an individual. Such diagnoses and monitoring are highly personalized by definition.…”

Section: Emerging and Next-generation Personalized Medicine Strategiesmentioning

confidence: 99%

Personalized medicine: motivation, challenges, and progress

Goetz

Schork

2018

Fertility and Sterility

432

209

View full text Add to dashboard Cite

There is a great deal of hype surrounding the concept of personalized medicine. Personalized medicine is rooted in the belief that since individuals possess nuanced and unique characteristics at the molecular, physiological, environmental exposure, and behavioral levels, they may need to have interventions provided to them for diseases they possess that are tailored to these nuanced and unique characteristics. This belief has been verified to some degree through the application of emerging technologies such as DNA sequencing, proteomics, imaging protocols, and wireless health monitoring devices, which have revealed great inter-individual variation in disease processes. In this review, we consider the motivation for personalized medicine, its historical precedents, the emerging technologies that are enabling it, some recent experiences including successes and setbacks, ways of vetting and deploying personalized medicines, and future directions, including potential ways of treating individuals with fertility and sterility issues. We also consider current limitations of personalized medicine. We ultimately argue that since aspects of personalized medicine are rooted in biological realities, personalized medicine practices in certain contexts are likely to be inevitable, especially as relevant assays and deployment strategies become more efficient and cost-effective.

show abstract

Section: Emerging and Next-generation Personalized Medicine Strategiesmentioning

confidence: 99%

Personalized medicine: motivation, challenges, and progress

Goetz

Schork

2018

Fertility and Sterility

432

209

View full text Add to dashboard Cite

show abstract

“…Furthermore, the 10-fold cross-validation methodology made our developed technique more robust and reliable. The proposed technique is completely automated, economical, and non-invasive, it can be used in the real-time monitoring of heart conditions with the development of cloud computing, smart phones, and wearable sensors [32], and can increase the survival rate of patients at risk of SCD.…”

Section: Discussionmentioning

confidence: 99%

Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal

Shi

Wang

2022

Symmetry

View full text Add to dashboard Cite

Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples’ lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a noninvasive process for acquiring the electrical activity of the heart, has both asymmetric and non-stationary characteristics; it is frequently employed to diagnose and evaluate the heart’s condition. In this work, we have detected SCD 14 min (separately for each one-minute interval) prior to its occurrence by analyzing ECG signals using discrete wavelet transform (DWT) and locality preserving projection (LPP). In the experiment, we have performed DWT on ECG signals to obtain coefficients, then LPP as a reduction methodology was used to cut down these obtained coefficients. Then, the acquired LPP features were ranked using various methods, including the T-test, Bhattacharyya, Wilcoxon, and entropy. At last, the highly ranked LPP features were subjected to decision tree, k-nearest neighbor (KNN), and support vector machine classifiers for distinguishing normal from SCD ECG signals. Our proposed technique has achieved a highest accuracy of 97.6% for the detection of SCD 14 min prior using the KNN classifier, compared to the existing works. Our proposed method is capable of predicting the people at risk of developing SCD 14 min before its onset, and, hence, clinicians would have enough time to provide treatment in intensive care units (ICU) for a subject at risk of SCD. Thus, this proposed technique as a useful tool can increase the survival rate of many cardiac patients.

show abstract

“…However, too many subjective factors probably lead to inaccurate results. The objective evaluation methods are divided into three categories, namely, detection based on driver physiological parameters [4][5][6][7][8], detection based on vehicle behavior [9,10], and detection based on computer vision [11][12][13][14][15][16][17][18][19][20]. The detection based on driver physiological parameters, including electroencephalogram (EEG), electrocardiogram (ECG), electromyography (EMG), electrooculogram (EOG), and other parameters, can reflect the driver's physiological state.…”

Section: Introductionmentioning

confidence: 99%

Driver Fatigue Tracking and Detection Method Based on OpenMV

Zhou¹,

Gong²,

Qie³

et al. 2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Aiming at the problem of fatigue driving, this paper proposed a driver fatigue tracking and detection method combined with OpenMV. OpenMV is used for image acquisition, and the Dlib feature point model is used to locate the detected driver's face. The aspect ratio of eyes is calculated to judge the opening and closing of eyes, and then fatigue detection is performed by PERCLOS (Percentage of Eyelid Closure over the Pupil). The cradle head system is mounted for driver fatigue tracking and detection. The results show that the dynamic tracking system can improve the accuracy to 92.10% for relaxation and 85.20% for fatigue of driver fatigue detection. This system can be applied to the monitoring of fatigue driving very effectively.

show abstract

Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare

Cited by 20 publications

References 66 publications

Personalized medicine: motivation, challenges, and progress

Personalized medicine: motivation, challenges, and progress

Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal

Driver Fatigue Tracking and Detection Method Based on OpenMV

Contact Info

Product

Resources

About