Dataset Information

High accuracy thermal conductivity measurement of aqueous cryoprotective agents and semi-rigid biological tissues using a microfabricated thermal sensor.

ABSTRACT: An improved thermal-needle approach for accurate and fast measurement of thermal conductivity of aqueous and soft biomaterials was developed using microfabricated thermal conductivity sensors. This microscopic measuring device was comprehensively characterized at temperatures from 0 °C to 40 °C. Despite the previous belief, system calibration constant was observed to be highly temperature-dependent. Dynamic thermal conductivity response during cooling (40 °C to -40 °C) was observed using the miniaturized single tip sensor for various concentrations of CPAs, i.e., glycerol, ethylene glycol and dimethyl sulfoxide. Chicken breast, chicken skin, porcine limb, and bovine liver were assayed to investigate the effect of anatomical heterogeneity on thermal conductivity using the arrayed multi-tip sensor at 20 °C. Experimental results revealed distinctive differences in localized thermal conductivity, which suggests the use of approximated or constant property values is expected to bring about results with largely inflated uncertainties when investigating bio-heat transfer mechanisms and/or performing sophisticated thermal modeling with complex biological tissues. Overall, the presented micro thermal sensor with automated data analysis algorithm is a promising approach for direct thermal conductivity measurement of aqueous solutions and soft biomaterials and is of great value to cryopreservation of tissues, hyperthermia or cryogenic, and other thermal-based clinical diagnostics and treatments.

SUBMITTER: Liang XM

PROVIDER: S-EPMC4438607 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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High accuracy thermal conductivity measurement of aqueous cryoprotective agents and semi-rigid biological tissues using a microfabricated thermal sensor.

Liang Xin M XM Sekar Praveen K PK Zhao Gang G Zhou Xiaoming X Shu Zhiquan Z Huang Zhongping Z Ding Weiping W Zhang Qingchuan Q Gao Dayong D

Scientific reports 20150520

An improved thermal-needle approach for accurate and fast measurement of thermal conductivity of aqueous and soft biomaterials was developed using microfabricated thermal conductivity sensors. This microscopic measuring device was comprehensively characterized at temperatures from 0 °C to 40 °C. Despite the previous belief, system calibration constant was observed to be highly temperature-dependent. Dynamic thermal conductivity response during cooling (40 °C to -40 °C) was observed using the min ...[more]

PMID: 25993037

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Project description:BackgroundInertial Measurement Unit (IMU)-based wearable sensors have found common use to track arm activity in daily life. However, classifying a high number of arm motions with single IMU-based systems still remains a challenging task. This paper explores the possibility to increase the classification accuracy of these systems by incorporating a thermal sensor. Increasing the number of arm motions that can be classified is relevant to increasing applicability of single-device wearable systems for a variety of applications, including activity monitoring for athletes, gesture control for video games, and motion classification for physical rehabilitation patients. This study explores whether a thermal sensor can increase the classification accuracy of a single-device motion classification system when evaluated with healthy participants. The motions performed are reproductions of exercises described in established rehabilitation protocols.MethodsA single wrist-mounted device was built with an inertial sensor and a thermal sensor. This device was worn on the wrist, was battery powered, and transmitted data over Bluetooth to computer during recording. A LabVIEW Graphical User Interface (GUI) instructed the user to complete 24 different arm motions in a pre-randomized order. The received data were pre-processed, and secondary features were calculated on these data. These features were processed with Principal Component Analysis (PCA) for dimensionality reduction and then several machine learning models were applied to select the optimal model based on speed and accuracy. To test the effectiveness of the scheme, 11 healthy subjects participated in the trials.ResultsAverage personalized classification model accuracies of 93.55% were obtained for 11 healthy participants. Generalized model accuracies of 82.5% indicated that the device can classify arm motions on a user without prior training. The addition of a thermal sensor significantly increased classification accuracy of a single wrist-mounted inertial device, from 75 to 93.55%, (F(1,20)?=?90.53, p?=?7.25e-09).ConclusionThis study found that the addition of the thermal sensor improved the classification accuracy of 24 arm motions from 75 to 93.55% for a single-device system. Our results provide evidence that a single device can be used to classify a relatively large number of arm motions from arm rehabilitation protocols. While this study provides a conceptual proof-of-concept with a healthy population, additional investigation is required to evaluate the performance of this system for specific applications, such as activity classification for physically affected stroke survivors undergoing home-based rehabilitation.

Dataset Information

High accuracy thermal conductivity measurement of aqueous cryoprotective agents and semi-rigid biological tissues using a microfabricated thermal sensor.

Publications

High accuracy thermal conductivity measurement of aqueous cryoprotective agents and semi-rigid biological tissues using a microfabricated thermal sensor.

Similar Datasets

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