Respiratory rate is one of important vital signs, i.e. an indication of patient's health state and its normal rates for adult person at rest may range from 12 to 20 breaths per minute (BPM). The rates may increase due to fever, illness, and other medical conditions. We report an effort to develop and calibrating a non-contact and low-cost respiratory rate monitoring system, based on digital image correlation technique using a used Microsoft Kinect camera. Steps accomplished in this reported work was designed as a hands-on training for last year student, where they can learn and grasp on how to develop a clinical instrument and to assure its measuring performance. Calibration steps were accomplished to ensure the accuracy of the monitoring results. Average measurement errors in distance determination was below 1%, meanwhile overall error in determining measured cycles were the range of 2.4% - 4.5 % (i.e. translational motion with repetition cycle of 12 - 18 cycles per minute, which is directly proportional to beat-per-minute (BPM)). The proposed system was then tested to 10 volunteers from students, to determine the volunteer's respiratory rate, i.e. either the chest and belly respiratory rates.
We have developed a simple and low cost viscometer prototype by using laser. The prototype consists a laser with 405 nm as a light source, an LDR optical sensor as a detector, Arduino Uno as a signal processor and serial monitor as a display. We use frying oil as object. The frying oil was heated to obtain a certain viscosity because there are strong correlations between viscosity and temperature. The results show that when the temperature increase, the viscosity is decreased, and the output intensity detected by LDR will be increased. In this design the average error value is 36.89 centipoise compared to standard measurement using Ostwald viscometer. The main caused is the experiment was not done in the dark room as it should.
It is necessary to design an instrument using optical fiber as a soil moisture sensor, which can be implemented to detect potential landslides. The paper describes the sensor design, the experimental setup and an example of sensor prototype. This study uses multimode plastic optical fiber, light source from IF E91A IR LEDs that connected with Arduino, and OPM software for data retrieval. The POF’s removed cladding is 6 cm long. Using the U-bent method with diameter variation of 35 mm, 30 mm, and 25 mm, the optical power decreases along with the increase of water content in the soil. The sensitivity obtained for 35 mm, 30 mm, and 25 mm diameter are 0.245 μWatt / %, 0.154 μWatt / %, and 0.437 μWatt / % respectively. The use of U-bent method with 25 mm diameter has a better performance because of its larger sensitivity than the other diameter variation.
Home door security systems using facial recognition based on image processing have been widely developed. Face detection in this system uses the Haar-Cascade Method. The system was tested using 4 types of tests, namely the accuracy test, the distance test, the facial expression test, and the lighting conditions test. The results show that this door design system has a total average accuracy level of 97.2%, with an optimal distance of 1.5 meters, and the condition of the lights must be on. Meanwhile, for the facial expression variation test, the system can distinguish well except when the face is tilted left or right.
Fruit consumption rate of Indonesian population is still far below the level of sufficiency of fruits consumption recommended by the Food Agriculture Organization / World Health Organization (FAO / WHO). Maintaining the quality of fruit is expected to maintain stability in the fulfillment of national fruit needs. Early detection to determine the growth rate can be seen from the level of greenery or chlorophyll content during the growth period. So we need a method to measure the concentration of chlorophyll in the leaves by using Diffuse Reflectance Spectroscopy which is non destructive. This technique does not require complicated sample preparation as in the determination of chlorophyll content through Absorption Spectroscopy. Tissue model of leaf (phantom) is made of gelatin with known chlorophyll content variation, used as a preliminary stage for testing Diffuse Reflectance Spectroscopy technique. Determination of chlorophyll content by Absorption Spectroscopy technique will be used as a comparison. To determine the value of optical parameters (absorption coefficient μa and reduced scattering coefficient μs'), obtained from fitting between the measured reflectance spectra of phantom and the reflectance model of leaves. Chlorophyll content determined from correlation equation y = 0,944 x + 5,0069 with a coefficient of determination (R2) of 0,9422 for mango leaves, y = 0,5759 x + 5,6772 with coefficient of determination (R2) of 0.9945 for starfruit leaves, y = 0,1168 x + 3,7704 with a coefficient of determination (R2) of 0.9789 for guava leaves.
The multispectral imaging (MSI) technique has been used for skin analysis, especially for distant mapping of invivo skin chromophores. We have successfully developed an MSI system with a new approach. Our MSI system captures 11 mono-spectral images of human skin which is too little for providing an accurate diagnostic information. We need something to reconstruct the 11 monoband data sets to the wider range hyperspectral data sets. In this paper, we proposed a method to build a hyperspectral reflectance cube based on artificial neural network (ANN) algorithm. ANN is trained using the 32 natural color from X-Rite Color Checker Passport. The learning procedure the involves acquisition, by a spectrometer. This neural network is then used to retrieve a hyperspectral reflectance cube between 380 and 880 nm with a 5 nm resolution. To evaluate the performance of reconstruction, we used the Goodness of Fit Coefficient (GFC) and Root Mean Squared Error (RMSE). The reconstruction results are very good. The average GFC was 0,9988 and the average RMSE was 0.023. We also tested the quality of reconstruction with healthy skin data sets and the results are good enough. For skin data sets, the average GFC was 0.9855 and the average RMSE was 0.0608.
Chlorophyll is a main biochemistry component for photosynthesis and a health indicator for plant. Chlorophyll concentration can be measured by non-destructive and non-contact method using Diffuse Reflectance Spectroscopy. Phantom consist of intralipid, aquades, gelatin and chlorophyll. Chlorophyll with certain concentration that known by absorption technique is used to be a primary stage for DRS. Non linear least square data fitting using diffuse reflectance mathematical model is used to determine parameters from phantom. The value of parameters is used for chlorophyll concentration measurement and will be compared with the chlorophyll concentration from absorption technique. When the system have been quantified well, the system will be used for chlorophyll concentration measurement for leaves without a complicated sample preparation such as absorption technique. Phantom reflectance spectroscopy has been tested and has a similar spectrum shape with leave's reflectance. Chlorophyll concentration measurement has been tested from three species of vegetables with three variation concentration and got error value for mustard, spinach and kale respectively 3%-10% ; 2.5%-10% and 16%-31%.
The quantitative evaluations were carried out in NIR spectroscopy that was implemented for monitoring and predicting the concentration of glucose samples. The collected absorbance data was preprocessed in developing PLS model before calibration using Savitzky-Golay filter. The spectrum was corrected by subtracting the offset of the regression to the absorption value and dividing this difference by the slope using Leave One Out Cross Validation (LOOCV) of the training set to determine the optimum number of PLS components. The Samples of glucose solution consist of 21 different molarity from 3000 to 5000 mg/dl with the interval of 100 mg/dl in step. Results obtained shown the linear dependency of the reference and predicted glucose concentration, with RMSECV and R2CV value are 104.92 mg/dl and 0.9728, respectively. The RMSECV shown the lowest error present and R2CV were close to one, indicates that the PLS model suited to accurately predict the variability glucose concentration.
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