This work was supported by Envisible, Inc. This study was also supported by a grant of the Korean Health Technology R&#38;D Project, Ministry of Health &#38; Welfare, Republic of Korea (HI15C1027). This research was also supported by the National Research Foundation of Korea (NRF-2016R1A1A1A05005348).

NOTE: All the procedures including the use of human subjects were accomplished by a non-invasive manner of simply wearing a pair of glasses. All the data were acquired by measuring the force signals from load cells inserted in the glasses that were not in direct contact with the skin. The data were wirelessly transmitted to the data recording module, which, in this case is a designated smartphone for the study. All the protocols were not related to in vivo/in vitro human studies. No drug and blood samples were used for the experiments. Informed consent was obtained from all subjects of the experiments.
1. Manufacturing of a Sensor-integrated Circuit Module
<ol>
	<li>Purchase electronic components for manufacturing the circuit module.
	<ol>
		<li>Purchase two ball-type load cells, each of which operates in a range between 0 N and 15 N, and produces an output of low differential voltage with maximum 120 mV span in a 3.3 V excitation. 
		NOTE: These load cells are used to measure force signals on both the left and right sides of the glasses.</li>
		<li>Purchase two instrumentation amplifiers and two 15 k&#937; gain-setting resistors. 
		NOTE: The instrumentation amplifier and the gain-setting resistor are used to amplify the force signal of the load cell eight times, up to 960 mV.</li>
		<li>Purchase a micro controller unit (MCU) with wireless capability (e.g., Wi-Fi connectivity), and a 10-bit analog-to-digital converter (ADC). 
		NOTE: The MCU is used to read the force signals and transmit them to a data acquisition module wirelessly. Because one analog input pin is used for two analog force inputs, the use of a multiplexer is introduced in the next step 1.1.4.</li>
		<li>Purchase a two-channel analog multiplexer that handles the two input signals with one ADC pin on the MCU.</li>
		<li>Purchase a lithium-ion polymer (LiPo) battery with 3.7 V nominal voltage, 300 mAh nominal capacity, and 1 C discharge rate. 
		NOTE: The battery capacity was chosen to supply enough current per hour more than 200 mAh and to operate the system reliably for about 1.5 h of an experiment.</li>
		<li>Purchase a 3.3 V voltage regulator for linear down-regulation of the 3.7 V battery voltage to the 3.3 V operating voltage of the system.</li>
		<li>Purchase five 12 k&#937; surface-mounted devices (SMD) type resistors as pull-up resistors of the MCU. The resistor&#39;s footprint is 2.0 mm x 1.2 mm (size 2012).</li>
	</ol>
	</li>
	<li>Fabricate printed circuit boards (PCBs). This step is about drawing the circuit boards, and making the artwork (i.e., the board layout, the .brd file) and the schematic (i.e., the .sch file) for PCB fabrication. A basic understanding of the process of creating artwork and schematic files is required for development.
	<ol>
		<li>Draw a schematic of a left circuit containing the battery using an electronic design application as shown in Figure 1A. Save the result as both artwork (.brd) and schematic (.sch) files.</li>
		<li>Draw a schematic of a right circuit containing the MCU using an electronic design application as shown in Figure 1B. Save the result as both artwork (.brd) and schematic (.sch) files.</li>
		<li>Fabricate the circuit boards by placing an order with a PCB fabrication company.</li>
		<li>Solder every electronic component prepared in step 1.1 to the PCBs as shown in Figure 2 and Figure 3. 
		CAUTION: The instrumentation amplifier is very sensitive to the soldering temperature. Make sure that lead temperature does not exceed 300 &#176;C for 10 s during soldering, otherwise it may cause permanent damage to the component.</li>
	</ol>
	</li>
</ol>
2. 3D Printing of a Frame of the Glasses
<ol>
	<li>Draw the 3D model of the head piece of the glasses using a 3D modeling tool as shown in Figure 4A. Export the result to the .stl file format.</li>
	<li>Draw the 3D model of the left and right temples of the glasses using a 3D modeling tool as shown in Figure 4B and Figure 4C. Export the results to the .stl file format.</li>
	<li>Print the head piece and temple parts using a 3D printer and a carbon fiber filament at 240 &#176;C of a nozzle temperature and 80 &#176;C of a bed temperature. 
	NOTE: The use of any commercial 3D printer and any types of filaments such as acrylonitrile butadiene styrene (ABS) and polylactide (PLA) can be permitted. The nozzle and bed temperatures may be adjusted according to the filament and printing conditions.</li>
	<li>Heat the tips of the temples using a hot air blower of a 180 &#176;C setting and bend them inward about 15 degrees to contact the epidermis of the temporalis muscle like conventional glasses. 
	NOTE: The degree of bending of the glasses temple does not need to be rigorous as the purpose of the curvature is to increase a form factor by helping the glasses fit on a subject&#39;s head when equipped. Be careful, however, as excessive bending will prevent the temples from touching the temporalis muscle, which makes it impossible to collect significant patterns.</li>
	<li>Repeat the steps from step 2.1&#8211;2.4 to print two different sizes of the glasses frame to fit multiple head sizes as shown in Figure 4.</li>
</ol>
3. Assembly of All Parts of the Glasses
<ol>
	<li>Insert the PCBs on both sides of the temples of the glasses using M2 bolts as shown in Figure 5.</li>
	<li>Assemble the head piece and the temples by inserting the M2 bolts into the hinge joints.</li>
	<li>Connect the left and right PCBs using the 3-pin connecting wires as shown in Figure 5.</li>
	<li>Connect the battery to the left circuit and attach it with an adhesive tape to the left temple. The mounting side of the battery is not critical, since it may vary depending on the PCB design.</li>
	<li>Cover the glasses with rubber tapes on the tip and the nose pad to add more friction with the human skin as shown in Figure 5.</li>
</ol>
4. Development of a Data Acquisition System
NOTE: The data acquisition system is composed of a data transmitting module and a data receiving module. The data transmitting module reads the time and the force signals of both sides, and then sends them to the data receiving module, which gathers the received data and writes them to .tsv files.
<ol>
	<li>Upload the data transmitting application to the MCU of the PCB module following the procedures in steps 4.1.1&#8211;4.1.3.
	<ol>
		<li>Run the &#34;GlasSense_Server&#34; project attached to the supplementary files using a computer. 
		NOTE: This project was built with Arduino integrated development environment (IDE). It provides the ability to read the time and force signals with 200 samples/s, and transmit them to the data receiving module.</li>
		<li>Connect the PCB module to the computer via a universal serial bus (USB) connector.</li>
		<li>Press the &#34;Upload&#34; button on the Arduino IDE to flash the programming codes from step 4.1.1 into the MCU.</li>
	</ol>
	</li>
	<li>Upload the data receiving application to a smartphone, which is used to receive the data wirelessly, following the procedures in steps 4.2.1&#8211;4.2.3.
	<ol>
		<li>Run the &#34;GlasSense_Client&#34; project attached to the supplementary files using a computer. 
		NOTE: This project was built with C# programming language. It provides the ability to receive data and save the .tsv files, which contain a subject&#39;s information, such as name, sex, age, and body mass index (BMI).</li>
		<li>Connect the smartphone to the computer via a USB connector to build the data receiving application.</li>
		<li>Press the &#34;File &#62; Build &#38; Run&#34; button on the C# project to build the data receiving application to the smartphone.</li>
	</ol>
	</li>
</ol>
5. Data Collection from a User Study
NOTE: This study collected six featured activity sets: sedentary rest (SR), sedentary chewing (SC), walking (W), chewing while walking (CW), sedentary talking (ST), and sedentary wink (SW).
<ol>
	<li>Select a pair of glasses which have an appropriate size to the user to be tested. Fine-tune the tightness with the support bolts at both the hinges (Figure 5). 
	CAUTION: The force values must not exceed 15 N, since the force sensors used in this study may lose the fine linear characteristic beyond the operation range. The force values can be fine-tuned by loosening or tightening the support bolts.</li>
	<li>Record the activities of all subjects by pressing the &#34;Record&#34; button on the application built in step 4.2.3.
	<ol>
		<li>Record an activity during a 120-s block and generate a recording file of it.
		<ol>
			<li>In the case of SR, sit the subject in a chair and have them use a smartphone or read a book. Allow movement of the head, but avoid movement of the whole body.</li>
			<li>In the cases of SC and CW, have the subjects eat two types of food texture (toasted bread and chewing jelly) in order to reflect different food properties. Serve the toasted bread in slices of 20 mm x 20 mm, which is a good size for eating.</li>
			<li>In the case of W, have the subjects walk at a speed of 4.5 km/h on a treadmill.</li>
			<li>In the case of ST, sit the subjects down and have them read a book out loud in a normal tone and speed.</li>
			<li>In the case of SW, inform the subjects to wink on the timing of a bell sound of 0.5 s long every 3 s.</li>
		</ol>
		</li>
		<li>Generate a recording file in .tsv format from the data collected in step 5.2.1. 
		NOTE: This file contains a sequence of the time when the data were received, a left force signal, a right force signal, and a label representing the current facial activity. Visualizations of temporal signals of all activities in a block of a user were depicted in Figure 6. The six featured activity sets (SR, SC, W, CW, ST, and SW) were labeled as 1, 2, 3, 4, 5, and 6, respectively. The labels were used to compare the predicted classes in section 8 of the protocol.</li>
		<li>Take a 60-s break after the recording block. Take off the glasses during the break, and re-wear them again when the recording block restarts.</li>
		<li>Repeat the block-and-break set of steps 5.2.1 and 5.2.2 four times for each activity.</li>
		<li>In the case of SW, have the subject wink repeatedly with the left eye during one block, and then wink repeatedly with the right eye during the next block.</li>
	</ol>
	</li>
	<li>Repeat steps 5.1&#8211;5.2 and collect the data from 10 subjects. In this study, we used five males and five females, the average age was 27.9 &#177; 4.3 (standard deviation; s.d.) years, which ranged at 19&#8211;33 years, and the average BMI was 21.6 &#177; 3.2 (s.d.) kg/m2, which ranged at 17.9&#8211;27.4 kg/m2. 
	NOTE: In this study, the subjects who did not have any medical conditions to chew food, wink, and walk were recruited, and this condition was used for inclusion criteria.</li>
</ol>
6. Signal Preprocessing and Segmentation
NOTE: The left and right signals are calculated separately in the following procedures.
<ol>
	<li>Prepare a series of temporal frames of 2 s long.
	<ol>
		<li>Segment the 120 s recorded signals into a set of 2 s frames by hopping them at 1-s intervals using MATLAB as shown in Figure 6. 
		NOTE: The segmented frames of 2 s long were used to extract features in section 7. The 1 s hopping size was determined to divide the signals by the 3 s wink interval already mentioned in step 5.2.1.</li>
		<li>Apply a low-pass filter (LPF) using a 5th order Butterworth filter with a cutoff frequency of 10 Hz for each frame.</li>
		<li>Save the results of step 6.1.2 as the temporal frames for the next steps in step 7.1.</li>
	</ol>
	</li>
	<li>Prepare a series of spectral frames.
	<ol>
		<li>Subtract the median from the original signals of each frame to remove the preload when wearing the glasses. 
		NOTE: The preload value is not required for the following frequency analysis, since it does not include any information about chewing, walking, wink, etc. It could, however, contain significant information, which can vary from subject to subject, from every setting of the glasses, and even from the moment a subject wears the glasses.</li>
		<li>Apply a Hanning window to each frame to reduce a spectral leakage on frequency analysis.</li>
		<li>Produce and save a single-sided spectrum by applying a fast Fourier transform (FFT) to each frame.</li>
	</ol>
	</li>
	<li>Define a combination of a temporal and a spectral frame of the same time as a frame block (or simply a frame).</li>
</ol>
7. Generation of Feature Vectors
NOTE: A feature vector is generated per frame produced in section 6 of the protocol. The left and right frames are calculated separately and combined into a feature vector in the following procedures. All the procedures were implemented in MATLAB.
<ol>
	<li>Extract statistical features from a temporal frame in step 6.1 of the protocol. A list of the total number of 54 features is given in Table 1.</li>
	<li>Extract statistical features from a spectral frame in step 6.2 of the protocol. A list of the total number of 30 features is given in Table 2.</li>
	<li>Generate an 84-dimensional feature vector by combining the temporal and spectral features above.</li>
	<li>Label the generated feature vectors from the recordings in step 5.2 of the protocol.</li>
	<li>Repeat the steps from steps 7.1&#8211;7.4 for all frame blocks and generate a series of feature vectors.</li>
</ol>
8. Classification of the Activities into Classes
NOTE: This step is to select the classifier model of a support vector machine (SVM)23 by determining parameters that show the best accuracy from the given problem (i.e., feature vectors). The SVM is a well-known supervised machine learning technique, which shows excellent performance in generalization and robustness using a maximum margin between the classes and a kernel function. We used a grid-search and a cross-validation method to define a penalty parameter C and a kernel parameter &#947; of the radial basis function (RBF) kernel. A minimum understanding of machine learning techniques and the SVM is required to perform the following procedures. Some referential materials23,24,25 are recommended for better understanding of machine learning techniques and the SVM algorithm. All the procedures in this section were implemented using LibSVM25 software package.
<ol>
	<li>Define a grid of pairs of (C, &#947;) for the grid-search. Use exponentially growing sequences of C (2-10, 2-5, &#8230;, 230) and &#947; (2-30, 2-25, &#8230;, 210). 
	NOTE: These sequences were determined heuristically.</li>
	<li>Define a pair of (C, &#947;) (e.g., (2-10, 2-30)).</li>
	<li>For the defined grid in step 8.2, perform the 10-fold cross-validation scheme. 
	NOTE: This scheme divides the entire feature vectors into 10-part subsets, then test one subset from the classifier model trained by the other subsets, and repeat it over all the subsets, one by one. Therefore, every feature vectors can be tested sequentially.
	<ol>
		<li>Divide the entire feature vectors into 10-part subsets.</li>
		<li>Define a testing set from a subset, and a training set from the remaining 9 subsets.</li>
		<li>Define a scale vector that scales all elements of the feature vectors to the range of [0, 1] for the training set. 
		NOTE: The scale vector has same dimension with the feature vector. It consists of a set of multipliers that scales the same row (or column) of all feature vectors to the range of [0, 1]. For example, the first feature of a feature vector is linearly scaled to the range of [0, 1] for the all first features of the training feature vectors. Note that the scale vector is defined from the training set, because the testing set should be assumed to be unknown. This step increases the accuracy of the classification by making the features the equal range and avoiding numerical errors during the calculation.</li>
		<li>Scale each feature of the training set to the range of [0, 1] using the scale vector obtained in step 8.2.3.</li>
		<li>Scale each feature of the testing set to the range of [0, 1] using the scale vector obtained in step 8.2.3.</li>
		<li>Train the training set through the SVM with the defined pair of (C, &#947;) in step 8.2, and then build a classifier model.</li>
		<li>Test the testing set through the SVM with the defined pair of (C, &#947;) in step 8.2, and the classifier model obtained from the training procedure.</li>
		<li>Calculate a classification accuracy on the testing set. The accuracy was calculated from the percentage of feature vectors which are correctly classified.</li>
		<li>Repeat the steps 8.2.2&#8211;8.2.8 for all the subsets, and calculate the average accuracy of all subsets.</li>
	</ol>
	</li>
	<li>Repeat the steps 8.2&#8211;8.3.9 for all grid points of a pair of (C, &#947;).</li>
	<li>Find the local maximum of the highest accuracy of the grid. All the procedures of section 8 are illustrated in Figure 7.</li>
	<li>(Optional) If the step of the grid is considered coarse, repeat the steps 8.1&#8211;8.5 in a finer grid near the local maximum found in step 8.5, and find a new local maximum of the fine grid.</li>
	<li>Compute the precision, recall, and F1 score of each class of activities from the following equations: 
	<img alt="Equation 1" src="/files/ftp_upload/56633/56633eq1.jpg" />&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;Equation 1 
	<img alt="Equation 2" src="/files/ftp_upload/56633/56633eq2.jpg" />&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;Equation 2 
	<img alt="Equation 3" src="/files/ftp_upload/56633/56633eq3.jpg" />&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;Equation 3 
	where TP, FP, and FN represent true positives, false positives, and false negatives for each activity, respectively. The confusion matrix of all the activities is given in Table 3.</li>
</ol>

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The authors have nothing to disclose.

In this study, we first proposed the design and manufacturing process of glasses that sense the patterns of food intake and physical activities. As this study mainly focused on the data analysis to distinguish the food intake from the other physical activities (such as walking and winking), the sensor and data acquisition system required the implementation of mobility recording. Thus, the system included the sensors, the MCU with wireless communication capability, and the battery. The proposed protocol provided a novel a...

Continuous and objective monitoring of food intake is essential for maintaining energy balance in the human body, as excessive energy accumulation may cause overweightness and obesity1, which could result in various medical complications2. The main factors in the energy imbalance are known to be both excessive food intake and insufficient physical activity3. Various studies on the monitoring of daily energy expenditure have been introduced with automatic and objective measurement of physical activity patterns through wearable devices4,5,6, even at the end-consumer level and medical stage7. Research on the monitoring of food intake, however, is still in the laboratory setting, since it is difficult to detect the food intake activity in a direct and objective manner. Here, we aim to present a device design and its evaluation for monitoring the food intake and physical activity patterns at a practical level in daily life.
There have been various indirect approaches to monitor the food intake through chewing and swallowing sounds8,9,10, movement of the wrist11,12,13, image analysis14, and electromyogram (EMG)15. However, these approaches were difficult to apply to daily life applications, because of their inherent limitations: the methods using sound were vulnerable to be influenced by environmental sound; the methods using the movement of the wrist were difficult to distinguish from other physical activities when not consuming food; and the methods using the images and EMG signals are restricted by the boundary of movement and environment. These studies showed the capability of automated detection of the food intake using sensors, but still had a limitation of practical applicability to everyday life beyond laboratory settings.
In this study, we used the patterns of temporalis muscle activity as the automatic and objective monitoring of the food intake. In general, the temporalis muscle repeats the contraction and relaxation as a part of masticatory muscle during the food intake16,17; thus, the food intake activity can be monitored by detecting the periodic patterns of temporalis muscle activity. Recently, there have been several studies utilizing the temporalis muscle activity18,19,20,21, which used the EMG or piezoelectric strain sensor and attaching them directly onto human skin. These approaches, however, were sensitive to the skin location of the EMG electrodes or strain sensors, and were easily detached from the skin due to the physical movement or perspiration. Therefore, we proposed a new and effective method using a pair of glasses that sense the temporalis muscle activity through two load cells inserted in both the hinges in our previous study22. This method showed great potential of detecting the food intake activity with a high accuracy without touching the skin. It was also un-obtrusive and non-intrusive, since we used a common glasses-type device.
In this study, we present a series of detailed protocols of how to implement the glasses-type device and how to use the patterns of temporalis muscle activity for monitoring the food intake and physical activity. The protocols include the process of hardware design and fabrication that consists of a 3D-printed frame of the glasses, a circuit module, and a data acquisition module, and include the software algorithms for data processing and analysis. We furthermore examined the classification among several featured activities (e.g., chewing, walking, and winking) to demonstrate the potential as a practical system that can tell a minute difference between the food intake and other physical activity patterns.

<table>
	<tbody>
		<tr>
			<td>FSS1500NSB</td>
			<td>Honeywell, USA</td>
			<td></td>
			<td>Load cell</td>
		</tr>
		<tr>
			<td>INA125U</td>
			<td>Texas Instruments, USA</td>
			<td></td>
			<td>Amplifier</td>
		</tr>
		<tr>
			<td>ESP-07</td>
			<td>Shenzhen Anxinke Technology, China</td>
			<td></td>
			<td>MCU with Wi-Fi module</td>
		</tr>
		<tr>
			<td>74LVC1G3157</td>
			<td>Nexperia, The Netherlands</td>
			<td></td>
			<td>Multiplexer</td>
		</tr>
		<tr>
			<td>MP701435P</td>
			<td>Maxpower, China</td>
			<td></td>
			<td>LiPo battery</td>
		</tr>
		<tr>
			<td>U1V10F3</td>
			<td>Pololu, USA</td>
			<td></td>
			<td>Voltage regulator</td>
		</tr>
		<tr>
			<td>Ultimaker 2</td>
			<td>Ultimaker, The Netherlands</td>
			<td></td>
			<td>3D printer</td>
		</tr>
		<tr>
			<td>ColorFabb XT-CF20</td>
			<td>ColorFabb, The Netherlands</td>
			<td></td>
			<td>Carbon fiber filament</td>
		</tr>
		<tr>
			<td>iPhone 6s Plus</td>
			<td>Apple, USA</td>
			<td></td>
			<td>Data acquisition device</td>
		</tr>
		<tr>
			<td>Jelly Belly</td>
			<td>Jelly Belly Candy Company, USA</td>
			<td></td>
			<td>Food texture for user study</td>
		</tr>
	</tbody>
</table>

design and evaluation of smart glasses for food intake and physical activity classification

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle activities during food intake and other physical activities. We fabricated a 3D-printed frame of the glasses and a load cell-integrated printed circuit board (PCB) module inserted in both hinges of the frame. The module was used to acquire the force signals, and transmit them wirelessly. These procedures provide the system with higher mobility, which can be evaluated in practical wearing conditions such as walking and waggling. A performance of the classification is also evaluated by distinguishing the patterns of food intake from those physical activities. A series of algorithms were used to preprocess the signals, generate feature vectors, and recognize the patterns of several featured activities (chewing and winking), and other physical activities (sedentary rest, talking, and walking). The results showed that the average F1 score of the classification among the featured activities was 91.4%. We believe this approach can be potentially useful for automatic and objective monitoring of ingestive behaviors with higher accuracy as practical means to treat ingestive problems.

Through the procedures outlined in the protocol, we prepared two versions of the 3D printed frame by differentiating the length of the head piece, LH (133 and 138 mm), and the temples, LT (110 and 125 mm), as shown in Figure 4. Therefore, we can cover several wearing conditions, which can be varied from the subjects&#39; head size, shape, etc. The subjects chose one of the frames to fit to their head for the user study. The vert...

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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

This study presents a protocol of designing and manufacturing a glasses-type wearable device that detects the patterns of food intake and other featured physical activities using load cells inserted in both hinges of the glasses.

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle ...

design-evaluation-smart-glasses-for-food-intake-physical-activity

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Engineering

11.1K Views.  Seoul National University. This study presents a protocol of designing and manufacturing a glasses-type wearable device that detects the patterns of food intake and other featured physical activities using load cells inserted in both hinges of the glasses.

Video: Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Experiments on Ultrasonic Lubrication Using a Piezoelectrically-assisted Tribometer and Optical Profilometer

Friction and wear are detrimental to engineered systems. Ultrasonic lubrication is achieved when the interface between two sliding surfaces is vibrated at a frequency above the acoustic range (20 kHz). As a solid-state technology, ultrasonic lubrication can be used where conventional lubricants are unfeasible or undesirable. Further, ultrasonic lubrication allows for electrical modulation of the effective friction coefficient between two sliding surfaces. This property enables adaptive systems that modify their frictional state and associated dynamic response as the operating conditions change. Surface wear can also be reduced through ultrasonic lubrication. We developed a protocol to investigate the dependence of friction force reduction and wear reduction on the linear sliding velocity between ultrasonically lubricated surfaces. A pin-on-disc tribometer was built which differs from commercial units in that a piezoelectric stack is used to vibrate the pin at 22 kHz normal to the rotating disc surface. Friction and wear metrics including effective friction force, volume loss, and surface roughness are measured without and with ultrasonic vibrations at a constant pressure of 1 to 4 MPa and three different sliding velocities: 20.3, 40.6, and 87 mm/sec. An optical profilometer is utilized to characterize the wear surfaces. The effective friction force is reduced by 62% at 20.3 mm/sec. Consistently with existing theories for ultrasonic lubrication, the percent reduction in friction force diminishes with increasing speed, down to 29% friction force reduction at 87 mm/sec. Wear reduction remains essentially constant (49%) at the three speeds considered.

We present a protocol for using a piezoelectrically-assisted tribometer and optical profilometer to investigate the dependence of ultrasonic wear and friction reduction on linear velocity, contact pressure, and surface properties.

Friction and wear are detrimental to engineered systems. Ultrasonic lubrication is achieved when the interface between two sliding surfaces is vibrated at ...

Scanning Light Scattering Profiler (SLPS) Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses

The scanning light scattering profiler (SLSP) methodology has been developed for the full-angle quantitative evaluation of forward and backward light scattering from intraocular lenses (IOLs) using goniophotometer principles. This protocol describes the SLSP platform and how it employs a 360&#176; rotational photodetector sensor that is scanned around an IOL sample while recording the intensity and location of scattered light as it passes through the IOL medium. The SLSP platform can be used to predict, non-clinically, the propensity for current and novel IOL designs and materials to induce light scatter. Non-clinical evaluation of light scattering properties of IOLs can significantly reduce the number of patient complaints related to unwanted glare, glistening, optical defects, poor image quality, and other phenomena associated with the unintended light scattering. Future studies should be conducted to correlate SLSP data with clinical results to help identify which measured light scatter is most problematic for patients that have undergone cataract surgery subsequent to IOL implantation.

Scanning Light Scattering Profiler SLPS Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses

This protocol describes the scanning light scattering profiler (SLSP) that enables the full-angle quantitative evaluation of forward and backward scattering of light from intraocular lenses (IOLs) using goniophotometer principles.

The scanning light scattering profiler (SLSP) methodology has been developed for the full-angle quantitative evaluation of forward and backward light ...

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Here, we demonstrate the advantages of the laser Doppler vibrometer (LDV) over conventional techniques (the network analyzer), as well as the techniques to create an application-based microelectromechanical systems (MEMS) filter and how to use it efficiently (i.e., tuning the tuning-capability and avoiding both failure and stiction). LDV enables crucial measurements that are impossible with the network analyzer, such as higher mode detection (highly sensitive biosensor application) and resonance measurement for very small devices (fast prototyping). Accordingly, LDV was used to characterize the frequency tuning range and resonance frequency at different modes of the MEMS filters built for this study. This wide range frequency tuning mechanism is based simply on Joule heating from the embedded heaters and relatively high thermal stress with respect to the temperature of a fixed-fixed beam. However, we demonstrate that another limitation of this method is the resulting high thermal stress, which can burn the devices. Further improvement was achieved and shown for the first time in this study, such that the tuning capability was increased by 32% through an increase in the applied DC bias voltage (25 V to 35 V) between the two adjacent beams. This important finding eliminates the need for the extra Joule heating at the wider frequency tuning range. Another possible failure is through stiction and requirement of structure optimization: We propose a simple and easy technique of low frequency square wave signal application that can successfully separate the beams and eliminates the need for the more sophisticated and complicated methods given in the literature. The above findings necessitate a design methodology, and so we also provide an application-based design.

A protocol for a fixed-fixed beam design using a laser Doppler vibrometer (LDV), including the measurement of frequency tuning, modification of tuning capability, and avoidance of device failure and stiction, is presented. The superiority of the LDV method over the network analyzer is demonstrated due to its higher mode capability.

Here, we demonstrate the advantages of the laser Doppler vibrometer (LDV) over conventional techniques (the network analyzer), as well as the techniques ...

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

We demonstrate the use of two nuclear-based analytical methods that can follow the modifications of microstructural arrangement of iron-based metallic glasses (MGs). Despite their amorphous nature, the identification of hyperfine interactions unveils faint structural modifications. For this purpose, we have employed two techniques that utilize nuclear resonance among nuclear levels of a stable 57Fe isotope, namely M&#246;ssbauer spectrometry and nuclear forward scattering (NFS) of synchrotron radiation. The effects of heat treatment upon (Fe2.85Co1)77Mo8Cu1B14 MG are discussed using the results of ex situ and in situ experiments, respectively. As both methods are sensitive to hyperfine interactions, information on structural arrangement as well as on magnetic microstructure is readily available. M&#246;ssbauer spectrometry performed ex situ describes how the structural arrangement and magnetic microstructure appears at room temperature after the annealing under certain conditions (temperature, time), and thus this technique inspects steady states. On the other hand, NFS data are recorded in situ during dynamically changing temperature and NFS examines transient states. The use of both techniques provides complementary information. In general, they can be applied to any suitable system in which it is important to know its steady state but also transient states.

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Here, we present a protocol to describe ex situ and in situ investigations of structural transformations in metallic glasses. We employed nuclear-based analytical methods which inspect hyperfine interactions. We demonstrate the applicability of M&#246;ssbauer spectrometry and nuclear forward scattering of synchrotron radiation during temperature-driven experiments.

We demonstrate the use of two nuclear-based analytical methods that can follow the modifications of microstructural arrangement of iron-based metallic ...

Hybrid Printing for the Fabrication of Smart Sensors

A method to combine additively manufactured substrates or foils and multilayer inkjet printing for the fabrication of sensor devices is presented. First, three substrates (acrylate, ceramics, and copper) are prepared. To determine the resulting material properties of these substrates, profilometer, contact angle, scanning electron microscope (SEM), and focused ion beam (FIB) measurements are done. The achievable printing resolution and suitable drop volume for each substrate are, then, found through the drop size tests. Then, layers of insulating and conductive ink are inkjet printed alternately to fabricate the target sensor structures. After each printing step, the respective layers are individually treated by photonic curing. The parameters used for the curing of each layer are adapted depending on the printed ink, as well as on the surface properties of the respective substrate. To confirm the resulting conductivity and to determine the quality of the printed surface, four-point probe and profilometer measurements are done. Finally, a measurement set-up and results achieved by such an all-printed sensor system are shown to demonstrate the achievable quality.

Here we present a protocol for the fabrication of inkjet-printed multilayer sensor structures on additively manufactured substrates and foil.

A method to combine additively manufactured substrates or foils and multilayer inkjet printing for the fabrication of sensor devices is presented. First, ...

Design and Implementation of a Bespoke Robotic Manipulator for Extra-corporeal Ultrasound

With the potential for high precision, dexterity, and repeatability, a self-tracked robotic system can be employed to assist the acquisition of real-time ultrasound. However, limited numbers of robots designed for extra-corporeal ultrasound have been successfully translated into clinical use. In this study, we aim to build a bespoke robotic manipulator for extra-corporeal ultrasound examination, which is lightweight and has a small footprint. The robot is formed by five specially shaped links and custom-made joint mechanisms for probe manipulation, to cover the necessary range of motion with redundant degrees of freedom to ensure the patient&#39;s safety. The mechanical safety is emphasized with a clutch mechanism, to limit the force applied to patients. As a result of the design, the total weight of the manipulator is less than 2 kg and the length of the manipulator is about 25 cm. The design has been implemented, and simulation, phantom, and volunteer studies have been performed, to validate the range of motion, the ability to make fine adjustments, mechanical reliability, and the safe operation of the clutch. This paper details the design and implementation of the bespoke robotic ultrasound manipulator, with the design and assembly methods illustrated. Testing results to demonstrate the design features and clinical experience of using the system are presented. It is concluded that the current proposed robotic manipulator meets the requirements as a bespoke system for extra-corporeal ultrasound examination and has great potential to be translated into clinical use.

This paper introduces the design and implementation of a bespoke robotic manipulator for extra-corporeal ultrasound examination. The system has five degrees of freedom with lightweight joints made by 3D printing and a mechanical clutch for safety management.

With the potential for high precision, dexterity, and repeatability, a self-tracked robotic system can be employed to assist the acquisition of real-time ...

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It is intended as a practical and reproducible procedure to identify macroscopic cracks at an early stage and monitor crack propagation during fatigue tests. It consists of strain field measurements at the weld using DIC. Images are taken at fixed load cycle intervals. Cracks become visible in the computed strain field as elevated strains. This way, the whole width of a small-scale specimen can be monitored to detect where and when a crack initiates. Subsequently, it is possible to monitor the development of the crack length. Because the resulting images are saved, the results are verifiable and comparable. The procedure is limited to cracks initiating at the surface and is intended for fatigue tests under laboratory conditions. By visualizing the crack, the presented procedure allows direct observation of macrocracks from their formation until rupture of the specimen.

Digital image correlation is used in fatigue tests on a resonance testing machine to detect macroscopic cracks and monitor crack propagation in welded specimens. Cracks on the specimen surface become visible as increased strains.

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It ...

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Traditional U-turn designs can improve operational features obviously, while U-turn diversions and merge segments still cause traffic congestion, conflicts, and delays. An exclusive spur dike U-turn lane design (ESUL) is proposed here to solve the disadvantages of traditional U-turn designs. To evaluate the operation performance of ESUL, a traffic simulation protocol is needed. The whole simulation process includes five steps: data collection, data analysis, simulation model build, simulation calibration, and sensitive analysis. Data collection and simulation model build are two critical steps and are described later in greater detail. Three indexes (travel time, delay, and number of stops) are commonly used in the evaluation, and other parameters can be measured from the simulation according to experimental needs. The results show that the ESUL significantly diminishes the disadvantages of traditional U-turn designs. The simulation can be applied to solve microscopic traffic problems, such as in single or several adjacent intersections or short segments. This method is not suitable for larger scale road networks or evaluations without data collection.

This protocol describes the process of solving a microscopic traffic problem with simulation. The whole process contains a detailed description of data collection, data analysis, simulation model build, simulation calibration, and sensitive analysis. Modifications and troubleshooting of the method are also discussed.

Traditional U-turn designs can improve operational features obviously, while U-turn diversions and merge segments still cause traffic congestion, ...

Fabrication of Compressed Hosiery and Measurement of its Pressure Characteristic Exerted on the Lower Limbs

This article reports the pressure characteristic measurement of compressed hosiery via direct and indirect methods. In the direct method, an interface sensor is used to measure the pressure value exerted on the lower limbs. In the indirect method, the necessary parameters mentioned by the cone and cylinder model are tested to calculate the pressure value. The necessary parameters involve course density, wales density, circumference, length, thickness, tension, and deformation of the compressed hosiery. Compared with the results of the direct method, the cone model in the indirect method is more suitable for calculating the pressure value because the cone model considers the change in radius of the lower limb from the knee to the ankle. Based on this measurement, the relationship among fabrication, structure, and pressure is further investigated in this study. We find that graduation is the main influence that can change the wales density. On the other hand, elastic motors directly affect the course density and the circumference of the stockings. Our reported work provides the fabrication-structure-pressure relationship and a design guide for gradually compressed hosiery.

This article reports fabrication, structure and pressure measurement of compressed hosiery by employing direct and indirect methods.

This article reports the pressure characteristic measurement of compressed hosiery via direct and indirect methods. In the direct method, an interface ...

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with slopes of up to 84&#176;. The manufacturing method is valid for the fast pneunet bending actuators in general and might, therefore, be interesting for newcomers to the field of actuator manufacturing. The control of the robot is achieved by means of a pneumatic control box that can provide arbitrary pressures and can be built by only using purchased components, a laser cutter, and a soldering iron. For the walking performance of the robot, the pressure-angle calibration plays a crucial role. Therefore, a semi-automated method for the pressure-angle calibration is presented. At high inclines (&#62; 70&#176;), the robot can no longer reliably fix itself to the walking plane. Therefore, the gait pattern is modified to ensure that the feet can be fixed on the walking plane.

This protocol provides a detailed list of steps to be performed for the manufacturing, control and evaluation of the climbing performance of a gecko-inspired soft robot.

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with ...