Non-destructive measurement technique for water content in organic solvents based on a thermal approach.

The water content of organic solvents is one of the crucial properties that affect the quality of the products and the efficiency of the manufacturing processes. The established water determination methods such as Karl Fischer titration and gas chromatography require skilled operators, specific reagents, and prolonged measurement time. Thus, they are not suitable for both on-line and in-line applications. In this study, we aim to develop a real-time and low-cost device with reliable accuracy. The proposed device based on a newly developed thermal approach could non-destructively detect the water content in multiple organic solvents at low concentrations with high accuracy and without the use of any specific reagent. Experiments were performed for the determination of water content in organic solvents such as methanol, ethanol, and isopropanol. The results show that the proposed device is feasible for the water content determination in methanol, ethanol, and isopropanol at 0-1% w/w. A Bland-Altman plot to illustrate the differences in measurements between the proposed device and coulometric Karl Fischer titration shows that most of the measurements lie within the limits of agreement where 95% of the differences between the two methods are expected to fall in the range of -0.13% and 0.09%.

Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network.

Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 with 12 channels and 2 kHz sampling rate setup, were used for this study. Our approach achieved an overall accuracy of 93.87 ± 1.49 and 91.69 ± 4.68% with a balanced accuracy of 84.00 ± 3.40 and 84.66 ± 4.78% for DB5 and DB7, respectively. We also observed a performance gain when considering only a subset of the movements, namely the six main hand movements based on six prehensile patterns from the Southampton Hand Assessment Procedure (SHAP), a clinically validated hand functional assessment protocol. Classification on only the SHAP movements in DB5 attained an overall accuracy of 98.82 ± 0.58% with a balanced accuracy of 94.48 ± 2.55%. With the same set of movements, our model also achieved an overall accuracy of 99.00% with a balanced accuracy of 91.27% on data from one of the amputee participants in DB7. These results suggest that with more data on the amputee subjects, our proposal could be a promising approach for controlling versatile prosthetic hands with a wide range of predefined hand and wrist movements.

Support vector machine classification of Parkinson's disease and essential tremor subjects based on temporal fluctuation.

Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the realization of treatment depends on specific medication. A novel feature is developed based on a hypothesis that tremor of PD subject has a larger fluctuation during resting than action task. Tremor signal is collected using a triaxial gyroscope sensor attached to subject's finger during kinetic and resting task. The angular velocity signal is analyzed by transforming a one-dimensional to two-dimensional signal using a relation of signal and its delay versions. Tremor fluctuation is defined as the area of 95% confidence ellipse covering the two-dimensional signal. The tremor fluctuation during kinetic and resting task is used as classification features. The support vector machine is used as a classifier and tested with 10-fold cross-validation. This novel feature provides a perfect PD/ET classification with 100% accuracy, sensitivity and specificity.

Temporal fluctuations of tremor signals from inertial sensor: a preliminary study in differentiating Parkinson's disease from essential tremor.

BACKGROUND: Parkinson's disease (PD) and essential tremor (ET) are the two most common movement disorders but the rate of misdiagnosis rate in these disorders is high due to similar characteristics of tremor. The purpose of the study is to present: (a) a solution to identify PD and ET patients by using the novel measurement of tremor signal variations while performing the resting task, (b) the improvement of the differentiation of PD from ET patients can be obtained by using the ratio of the novel measurement while performing two specific tasks. METHODS: 35 PD and 22 ET patients were asked to participate in the study. They were asked to wear a 6-axis inertial sensor on his/her index finger of the tremor dominant hand and perform three tasks including kinetic, postural and resting tasks. Each task required 10 s to complete. The angular rate signal measured during the performance of these tasks was band-pass filtered and transformed into a two-dimensional representation. The ratio of the ellipse area covering 95 % of this two-dimensional representation of different tasks was investigated and the two best tasks were selected for the purpose of differentiation. RESULTS: The ellipse area of two-dimensional representation of the resting task of PD and ET subjects are statistically significantly different (p < 0.05). Furthermore, the fluctuation ratio, defined as a ratio of the ellipse area of two-dimensional representation of resting to kinetic tremor, of PD subjects were statistically significantly higher than ET subjects in all axes (p = 0.0014, 0.0011 and 0.0001 for x, y and z-axis, respectively). The validation shows that the proposed method provides 100 % sensitivity, specificity and accuracy of the discrimination in the 5 subjects in the validation group. While the method would have to be validated with a larger number of subjects, these preliminary results show the feasibility of the approach. CONCLUSIONS: This study provides the novel measurement of tremor variation in time domain termed 'temporal fluctuation'. The temporal fluctuation of the resting task can be used to discriminate PD from ET subjects. The ratio of the temporal fluctuation of the resting task to the kinetic task improves the reliability of the discrimination. While the method is powerful, it is also simple so it could be applied on low resource platforms such as smart phones and watches which are commonly equipped with inertial sensors.

Low-cost, 3-dimension, office-based inertial sensors for automated tremor assessment: technical development and experimental verification.

BACKGROUND: Tremors are common clinical complaints among the elderly and non-specialist physicians frequently are challenged by the need to provide an accurate diagnosis of various tremor syndromes, particularly Parkinson's disease and essential tremor in their busy practices. OBJECTIVE: We sought to develop an easy-to-use, mobile robust, accurate, and cost-effective instrument that can objectively quantify tremors. METHOD: The low-cost, 3-dimension, inertial sensors were developed for automated tremor assessment. The main sensor unit consists of a 3-axis accelerometer and a 3-axis gyroscope for the purpose of measuring the tilting angle relative to the gravity, linear acceleration, and angular velocity of the body segments affected by tremors. The transmitter consists of five main modules, including a microcontroller, power management module, sensor module, external memory interface module, and Bluetooth™ communication interface module, which connects to the sensors by a thin wire. The signal processing utilized fast Fourier transform analysis to include RMS angular rate, RMS angle, RMS rate, RMS velocity, peak frequency, peak frequency magnitude, and dispersion of frequency as variables. RESULT: The prototype was tested with a tremor simulator at programmable angular rates of 2-, 4-, and 8-Hz confirming its accuracy. Twenty subjects (10 PD and 10 age-matched ET patients) participated as part of the experimental verification to perform three tremor tasks, including rest, postural, and kinetic tremor according to the teaching videotape of the motor section of the UPDRS. The mean peak frequency was significantly lower in PD than ET patients at rest on the x- (p < 0.01) and z-axis (p < 0.01). In PD patients, the RMS angular rate, RMS angle, RMS rate, RMS velocity, and peak magnitude were all significantly higher than those values in ET patient at rest while the data was not significantly difference during postural and kinetic actions. ET patients had significantly higher peak frequency during postural action in the y-axis than PD patients (p < 0.05). CONCLUSION: The study provides the technical development of an accurate, inexpensive, and simple method to measure the kinematics of tremor in humans. Further studies are warranted to confirm the validity of each parameter and the diagnostic accuracy in each tremor syndrome.