A low-power portable scanner for body-worn Wireless Resistive Analog Passive (WRAP) sensors for mHealth applications

Zaman, Sabbir; Morshed, Bashir I.

doi:10.1016/j.measurement.2021.109214

Cited by 7 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the presented system and the corresponding results have been compared to several works in the literature [16,[19][20][21][22][23][24]35,46]. Table 5 summarizes a qualitative comparison between some recent works and the current proposal.…”

Section: Comparison To Other Alternativesmentioning

confidence: 99%

See 1 more Smart Citation

A Versatile Wearable Based on Reconfigurable Hardware for Biomedical Measurements

Toral

Romero²,

Castillo

et al. 2022

SSRN Journal

View full text Add to dashboard Cite

Section: Comparison To Other Alternativesmentioning

confidence: 99%

“…For example, ink-jet can be used on textiles to create e-textiles [31], or can be used on flexible substrates to create electrodes for biosignals [32]. These techniques are not limited to electrodes, as they can be also used for temperature or chemical sensors [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A Versatile Wearable Based on Reconfigurable Hardware for Biomedical Measurements

Toral

Romero²,

Castillo

et al. 2022

SSRN Journal

View full text Add to dashboard Cite

“…reader for the passive sensors), commercial wearables, a custom smartphone app (SCC-Health app), and a custom web server (SCC-Health server). Details of the design and functionality of the sensors and scanner can be found elsewhere [13]. For physiological data collection, we utilized novel inkjet-printed (IJP) sensors in addition to commercial wearables such as a smart wristband (Mi Band 2, Xiaomi) and a fingertip pulse oximeter (CMS50E, Crucial Medical Systems) [14].…”

Section: Smart Health (Shealth) Frameworkmentioning

confidence: 99%

A Smart Health (sHealth) Centric Method Toward Estimation of Sleep Deficiency Severity from Wearable Sensor Data Fusion

Rahman¹,

Morshed²,

Preza³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Sleep deficiency impacts the quality of life and may have serious health consequences in the long run. Questionnaire-based subjective assessment of sleep deficiency has many limitations. On the other hand, objective assessment of sleep deficiency is challenging. In this study, we propose a polysomnography-based mathematical model for computing baseline sleep deficiency severity score and then investigated the estimation of sleep deficiency severity using features available only from wearable sensor data including heart rate variability and single-channel electroen-cephalography for a dataset of 500 subjects. We used Monte-Carlo Feature Selection (MCFS) and inter-dependency discovery for selecting the best features and removing multi-collinearity. For developing the Regression model we investigated both the frequentist and the Bayesian ap-proaches. An Artificial Neural Network achieved the best performance of RMSE = 5.47 and an R-squared value of 0.67 for sleep deficiency severity estimation. The developed method is com-parable to conventional methods of Functional Outcome of Sleep Questionnaire and Epworth Sleepiness Scale for assessing the impact of sleep apnea on sleep deficiency. Moreover, the results pave the way for reliable and interpretable sleep deficiency severity estimation using a wearable device in Smart Health.

show abstract

“…The main components in the framework are various sensors, such as battery-less body-worn passive sensors with a scanner (i.e., reader for the passive sensors), commercial wearables, a custom smartphone app (SCC-Health app), and a custom web server (SCC-Health server). Details of the design and functionality of the sensors and scanner can be found elsewhere [17]. For physiological data collection, we utilized novel inkjet-printed (IJP) sensors in addition to commercial wearables such as a smart wristband (Mi Band 2, Xiaomi, Beijing, China) and a fingertip pulse oximeter (CMS50E, Crucial Medical Systems, Atlanta, GA, USA) [18].…”

Section: Smart Health (Shealth) Frameworkmentioning

confidence: 99%

A Smart Health (sHealth)-Centric Method toward Estimation of Sleep Deficiency Severity from Wearable Sensor Data Fusion

2021

Self Cite

View full text Add to dashboard Cite

Sleep deficiency impacts the quality of life and may have serious health consequences in the long run. Questionnaire-based subjective assessment of sleep deficiency has many limitations. On the other hand, objective assessment of sleep deficiency is challenging. In this study, we propose a polysomnography-based mathematical model for computing baseline sleep deficiency severity score and then investigated the estimation of sleep deficiency severity using features available only from wearable sensor data including heart rate variability and single-channel electroencephalography for a dataset of 500 subjects. We used Monte-Carlo feature selection (MCFS) and inter-dependency discovery for selecting the best features and removing multi-collinearity. For developing the Regression model we investigated both the frequentist and the Bayesian approaches. An artificial neural network achieved the best performance of RMSE = 5.47 and an R-squared value of 0.67 for sleep deficiency severity estimation. The developed method is comparable to conventional methods of Functional Outcome of Sleep Questionnaire and Epworth Sleepiness Scale for assessing the impact of sleep apnea on sleep deficiency. Moreover, the results pave the way for reliable and interpretable sleep deficiency severity estimation using single-channel EEG.

show abstract

A low-power portable scanner for body-worn Wireless Resistive Analog Passive (WRAP) sensors for mHealth applications

Cited by 7 publications

References 38 publications

A Versatile Wearable Based on Reconfigurable Hardware for Biomedical Measurements

A Versatile Wearable Based on Reconfigurable Hardware for Biomedical Measurements

A Smart Health (sHealth) Centric Method Toward Estimation of Sleep Deficiency Severity from Wearable Sensor Data Fusion

A Smart Health (sHealth)-Centric Method toward Estimation of Sleep Deficiency Severity from Wearable Sensor Data Fusion

Contact Info

Product

Resources

About