Feature selection and classification methodology for the detection of knee-joint disorders

Nalband, Saif; Sundar, Aditya; Prince, A. Amalin; Agarwal, Anita

doi:10.1016/j.cmpb.2016.01.020

Cited by 100 publications

(54 citation statements)

References 28 publications

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“…The closer r s is to unity, the more positive and direct is the association between devices. Correlations from 0.3 to 0.5 are considered as low positive (weak) correlation, 0.5 to 0.7 are considerate as a moderate (acceptable) positive correlation, from 0.7 to 0.9 as a high positive (strong) correlation and 0.9 to 1.00 as a very high positive association (very strong) (Mukaka, 2012). The uniformity of data from different Foobot FBT0002100 was also determined by a Spearman's rank-order correlation.…”

Section: Discussionmentioning

confidence: 99%

“…A Bland-Altman analysis was then performed on the validation dataset to examine the correlation and agreement between data generated by the calibration equation and data obtained by the Gray-Wolf instruments. The Bland-Altman method calculates the mean difference between two methods of measurement (the "bias"), and 95 % limits of agreement from the mean difference (1.96 SD) (Myles and Cui, 2007). From this process, a Bland-Altman plot (or difference plot) can be generated as a graphical method of comparing two measurements of the same variable.…”

Section: Discussionmentioning

confidence: 99%

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Field evaluation of a low-cost indoor air quality monitor to quantify exposure to pollutants in residential environments

Moreno-Rangel

Sharpe

Musau

et al. 2018

J. Sens. Sens. Syst.

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Abstract. Measurements of temporal and spatial changes to indoor contaminant concentrations are vital to understanding pollution characteristics. Whilst scientific instruments provide high temporal resolution of indoor pollutants, their cost and complexity make them unfeasible for large-scale projects. Low-cost monitors offer an opportunity to collect high-density temporal and spatial data in a broader range of households. This paper presents a user study to assess the precision, accuracy, and usability of a low-cost indoor air quality monitor in a residential environment to collect data about the indoor pollution. Temperature, relative humidity, total volatile organic compounds (tVOC), carbon dioxide (CO2) equivalents, and fine particulate matter (PM2.5) data were measured with five low-cost (“Foobot”) monitors and were compared with data from other monitors reported to be scientifically validated. The study found a significant agreement between the instruments with regard to temperature, relative humidity, total volatile organic compounds, and fine particulate matter data. Foobot CO2 equivalent was found to provide misleading CO2 levels as indicators of ventilation. Calibration equations were derived for tVOC, CO2, and PM2.5 to improve sensors' accuracy. The data were analysed based on the percentage of time pollutant levels that exceeded WHO thresholds. The performance of low-cost monitors to measure total volatile organic compounds and particulate matter 2.5 µm has not been properly addressed. The findings suggest that Foobot is sufficiently accurate for identifying high pollutant exposures with potential health risks and for providing data at high granularity and good potential for user or scientific applications due to remote data retrieval. It may also be well suited to remote and larger-scale studies in quantifying exposure to pollutants.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Field evaluation of a low-cost indoor air quality monitor to quantify exposure to pollutants in residential environments

Moreno-Rangel

Sharpe

Musau

et al. 2018

J. Sens. Sens. Syst.

View full text Add to dashboard Cite

show abstract

“…After establishing the normality of the data [23], Pearson's correlation coefficient was calculated for each sensor pair to assess the strength of the linear relationship between the two signals [24].…”

Section: Comparing the Similarity Of The Body-worn And Clothing-mountmentioning

confidence: 99%

Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification

Jayasinghe

Harwin

Hwang

2019

Sensors

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Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.

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“…as measured with μCT for all bone specimens regardless of the tibia or fibula harvest location. 39 The numbers of data points for correlation calculation were the numbers of analyzed ROIs and were equal to 108 and 36 for tibial and fibular specimens, respectively. T 2MM did not demonstrate significant correlations with bone porosity or BMD (p > 0.05).…”

Section: Pearson's Correlation Coefficients Between Ute-mri Markers (mentioning

confidence: 99%

Collagen proton fraction from ultrashort echo time magnetization transfer (UTE‐MT) MRI modelling correlates significantly with cortical bone porosity measured with micro‐computed tomography (μCT)

Jerban

Wan

et al. 2018

NMR in Biomedicine

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Intracortical bone porosity is a key microstructural parameter that determines bone mechanical properties. While clinical MRI visualizes the cortical bone with a signal void, ultrashort echo time (UTE) MRI can acquire high signal from cortical bone, thus enabling quantitative assessments. Magnetization transfer (MT) imaging combined with UTE‐MRI can indirectly assess protons in the bone collagenous matrix, which are inversely related to porosity. This study aimed to examine UTE‐MT MRI techniques to evaluate intracortical bone porosity. Eighteen human cortical bone specimens from the tibial and fibular midshafts were scanned using UTE‐MT sequences on a clinical 3 T MRI scanner and on a high‐resolution micro‐computed tomography (μCT) scanner. A series of MT pulse saturation powers (500°, 1000°, 1500°) and frequency offsets (2, 5, 10, 20, 50 kHz) were used to measure the macromolecular fraction (MMF) and macromolecular T2 (T2MM) using a two‐pool MT model. The measurements were made on 136 different regions of interest (ROIs). ROIs were selected at three cortical bone layers (from endosteum to periosteum) and four anatomical sites (anterior, mid‐medial, mid‐lateral, and posterior) to provide a wide range of porosity. MMF showed moderate to strong correlations with intracortical bone porosity (R = −0.67 to −0.73, p < 0.01) and bone mineral density (BMD) (R = +0.46 to +0.70, p < 0.01). Comparing the average MMF between cortical bone layers revealed a significant increase from the endosteum towards the periosteum. Such a pattern was in agreement with porosity reduction and BMD increase towards the periosteum. These results suggest that the two‐pool UTE‐MT technique can potentially serve as a novel and accurate tool to assess intracortical bone porosity.

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Feature selection and classification methodology for the detection of knee-joint disorders

Cited by 100 publications

References 28 publications

Field evaluation of a low-cost indoor air quality monitor to quantify exposure to pollutants in residential environments

Field evaluation of a low-cost indoor air quality monitor to quantify exposure to pollutants in residential environments

Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification

Collagen proton fraction from ultrashort echo time magnetization transfer (UTE‐MT) MRI modelling correlates significantly with cortical bone porosity measured with micro‐computed tomography (μCT)

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