Name: design and evaluation of smart glasses for food intake and physical activity classification
Uploaded: 2018-02-14T13:00:00
Description: Watch this Scientific Journal Video about Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification at JoVE.com

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 sampl...

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	<li>Sharma, A. M., Padwal, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Obesity+is+a+sign-over-eating+is+a+symptom:+an+aetiological+framework+for+the+assessment+and+management+of+obesity.">Obesity is a sign-over-eating is a symptom: an aetiological framework for the assessment and management of obesity.</a> Obes Rev. 11 (5), 362-370 (2010).</li><li>Pi-Sunyer, F. X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clinical+guidelines+on+the+identification,+evaluation,+and+treatment+of+overweight+and+obesity+in+adults.">Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults.</a> Am J Clin Nutr. 68 (4), 899-917 (1998).</li><li>McCrory, P., Strauss, B., Wahlqvist, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Energy+balance,+food+intake+and+obesity.">Energy balance, food intake and obesity.</a> Exer Obes. , (1994).</li><li>Albinali, F., Intille, S., Haskell, W., Rosenberger, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Proceedings of the 12th ACM international conference on Ubiquitous computing. , 311-320 (2010).</li><li>Bonomi, A., Westerterp, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+physical+activity+monitoring+and+lifestyle+interventions+in+obesity:+a+review.">Advances in physical activity monitoring and lifestyle interventions in obesity: a review.</a> Int J Obes. 36 (2), 167-177 (2012).</li><li>Jung, S., Lee, J., Hyeon, T., Lee, M., Kim, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fabric-Based+Integrated+Energy+Devices+for+Wearable+Activity+Monitors.">Fabric-Based Integrated Energy Devices for Wearable Activity Monitors.</a> Adv Mater. 26 (36), 6329-6334 (2014).</li><li>Fulk, G. D., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+sensors+to+measure+activity+in+people+with+stroke.">Using sensors to measure activity in people with stroke.</a> Top Stroke Rehabil. 18 (6), 746-757 (2011).</li><li>Makeyev, O., Lopez-Meyer, P., Schuckers, S., Besio, W., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+food+intake+detection+based+on+swallowing+sounds.">Automatic food intake detection based on swallowing sounds.</a> Biomed Signal Process Control. 7 (6), 649-656 (2012).</li><li>P&#228;&#223;ler, S., Fischer, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Food+intake+activity+detection+using+an+artificial+neural+network.">Food intake activity detection using an artificial neural network.</a> Biomed Tech (Berl). , (2012).</li><li>Passler, S., Fischer, W. -. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Food+intake+monitoring:+Automated+chew+event+detection+in+chewing+sounds.">Food intake monitoring: Automated chew event detection in chewing sounds.</a> IEEE J Biomed Health Inform. 18 (1), 278-289 (2014).</li><li>Kadomura, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> CHI'13 Extended Abstracts on Human Factors in Computing Systems. , 1551-1556 (2013).</li><li>Fontana, J. M., Farooq, M., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+ingestion+monitor:+A+novel+wearable+device+for+monitoring+of+ingestive+behavior.">Automatic ingestion monitor: A novel wearable device for monitoring of ingestive behavior.</a> IEEE Trans Biomed Eng. 61 (6), 1772-1779 (2014).</li><li>Shen, Y., Salley, J., Muth, E., Hoover, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+Accuracy+of+a+Wrist+Motion+Tracking+Method+for+Counting+Bites+across+Demographic+and+Food+Variables.">Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites across Demographic and Food Variables.</a> IEEE J Biomed Health Inform. , (2016).</li><li>Farooq, M., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> International Conference on Bioinformatics and Biomedical Engineering. , 464-472 (2017).</li><li>Grigoriadis, A., Johansson, R. S., Trulsson, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+profile+and+amplitude+of+human+masseter+muscle+activity+is+adapted+to+food+properties+during+individual+chewing+cycles.">Temporal profile and amplitude of human masseter muscle activity is adapted to food properties during individual chewing cycles.</a> J Oral Rehab. 41 (5), 367-373 (2014).</li><li>Strini, P. J. S. A., Strini, P. J. S. A., de Souza Barbosa, T., Gavi&#227;o, M. B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessment+of+thickness+and+function+of+masticatory+and+cervical+muscles+in+adults+with+and+without+temporomandibular+disorders.">Assessment of thickness and function of masticatory and cervical muscles in adults with and without temporomandibular disorders.</a> Arch Oral Biol. 58 (9), 1100-1108 (2013).</li><li>Standring, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Gray's anatomy: the anatomical basis of clinical practice. , (2015).</li><li>Farooq, M., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+wearable+device+for+food+intake+and+physical+activity+recognition.">A novel wearable device for food intake and physical activity recognition.</a> Sensors. 16 (7), 1067 (2016).</li><li>Zhang, R., Amft, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Proceedings of the 2016 ACM International Symposium on Wearable Computers. , 50-52 (2016).</li><li>Farooq, M., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentation+and+Characterization+of+Chewing+Bouts+by+Monitoring+Temporalis+Muscle+Using+Smart+Glasses+with+Piezoelectric+Sensor.">Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses with Piezoelectric Sensor.</a> IEEE J Biomed Health Inform. , (2016).</li><li>Huang, Q., Wang, W., Zhang, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Your+Glasses+Know+Your+Diet:+Dietary+Monitoring+using+Electromyography+Sensors.">Your Glasses Know Your Diet: Dietary Monitoring using Electromyography Sensors.</a> IEEE Internet of Things Journal. , (2017).</li><li>Chung, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+glasses-type+wearable+device+for+monitoring+the+patterns+of+food+intake+and+facial+activity.">A glasses-type wearable device for monitoring the patterns of food intake and facial activity.</a> Scientific Reports. 7, 41690 (2017).</li><li>Cristianini, N., Shawe-Taylor, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> An introduction to support Vector Machines: and other kernel-based learning methods. , (2000).</li><li>Giannakopoulos, T., Pikrakis, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Introduction to Audio Analysis: A MATLAB Approach. , (2014).</li><li>Chang, C. -. C., Lin, C. -. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LIBSVM:+a+library+for+support+vector+machines.">LIBSVM: a library for support vector machines.</a> ACM Trans Intell Syst Technol. 2 (3), 27 (2011).</li><li>Po, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Time-frequency+analysis+of+chewing+activity+in+the+natural+environment.">Time-frequency analysis of chewing activity in the natural environment.</a> J Dent Res. 90 (10), 1206-1210 (2011).</li><li>Ji, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frequency+and+velocity+of+people+walking.">Frequency and velocity of people walking.</a> Struct Eng. 84 (3), 36-40 (2005).</li><li>Knoblauch, R., Pietrucha, M., Nitzburg, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Field+studies+of+pedestrian+walking+speed+and+start-up+time.">Field studies of pedestrian walking speed and start-up time.</a> Transp Res Red. 1538, 27-38 (1996).</li></ol>

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...

Watch this Scientific Journal Video about Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification at JoVE.com

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 ...

The overall goal of this procedure is to design and manufacture a glasses type wearable device that detects the patterns of temporalis muscle activities during food intake and other physical activities. This method can help answer key questions in the field of monitoring of ingestible behaviors such food intake detection during a meal. The main advantage of this technique is that the patterns of temporalis muscle activity during the food intake can be detected automatically and objectively using a pair of glasses.The manufacturing procedure will be demonstrated by Wonjoon Oh, a mechanical engineer at our company. The data collection procedure will be done by Hak Yung Gu, who is also at our company. Key to the experiment are the circuit modules which must be manufactured.There are two. One for the left and right temples in a pair of glasses. The left module contains a load cell, connector, instrumentation amplifier, voltage regulator, and battery connector.The right module has a micro-controller with WiFi, UART connector, multiplexer, amplifier, connector, and load cell. In addition to the circuits, glasses frames have to be fabricated. The frames must be able to support the printed circuit board modules.To create the frames, use a 3-D modeling tool to draw a model of the headpiece of the glasses and it's two temple pieces. Export the drawing to CAD's stereo lithography files for 3-D printing. When printing the frame parts use a carbon filament with a 250 degree celsius nozzle temperature and an 80 degree celsius bed temperature.After printing, work to bend the temples inward. Use a hot air blower set at 180 degrees celsius to heat a piece. Once heated, bend the temple inward about 15 degrees.Bend each temple piece inward before continuing. Gather all the pieces to assemble the glasses. These include left and right printed circuit boards, left and right temples, and a headpiece.Begin with the right temple components. Insert the right circuit board in the right temple. Secure it in place with a bolt.Secure the left circuit board and left temple in the same way. Next, attach the headpiece and one of the temples at the appropriate hinge joint and secure it with a bolt. Continue by attaching the other temple to the headpiece.When done, connect the left and right circuits using 3 pin connecting wires. For this design, connect a battery to the left circuit. Secure it with adhesive tape.Complete the glasses by applying rubber tape at the tip and on the nose pads to increase friction for the wearer. At this point, prepare for data acquisition. At a computer, run the GlasSense_Server project and connect the frame circuit module via USB.Flash the GlasSense_Server software into the micro-controller. This project uses a smartphone to receive the data. Run the GlasSense_Client software on the computer and connect the smartphone via a USB connector.Build the data receiving application on the smartphone. For the study, identify a subject and select suitably sized glasses. Have the subject try on the glasses.If necessary, use the support bolts on the frame to fine tune the fit. The next step is to have the subject collect data for different activities. For the sedentary rest activity, have the subject sit in a chair to read with the glasses on.Record the activity using the smartphone application for 120 seconds. Allow head movement but avoid movement of the entire body. After 120 seconds, stop the recording and take off the glasses for a 60 second break.Repeat recording data and taking a break four times. Follow a similar procedure for the walking activity by having the subject stand on treadmill and wear the glasses. Start the treadmill at 4.5 km per hour and have the subject record for 120 seconds.When done take a 60 second break with the glasses off. There are also sedentary chewing and chewing while walking activities. For these prepare toasted bread in 20x20 mm squares and have chewing jellies ready.Arrange for the subject to sit or be on the treadmill, as appropriate, with the food nearby. While wearing the glasses, start the activity and data recording. Have the subject eat bread while chewing normally.After 120 seconds, take a 60 second break with the glasses off. Next, perform a sedentary talking activity. For this, collect data as the subject sits and reads a book out loud in a normal tone and speed.In the sedentary wink activity, arrange for the subject to wink in response to a bell set to go off every three seconds. These data are from one subject performing each activity. The horizontal axis is the time in seconds.Each vertical axis is the measured force after the median of the ten measurements is subtracted. The blue curves are for the left side and the red curves are for the right side of the face. As might be expected, the maximum amplitudes of the chewing activities are large when compared to the sedentary rest, sedentary talking, and walking activities.Data of this type are transformed into input to a machine learning classification technique. After each development, this technique can be potentially useful for automatic and objective monitoring of ingestible behaviors with higher accuracy as a practical means to treat ingestive problems.

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Engineering

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 ...

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions

The use of natural gas continues to grow with increased discovery and production of unconventional shale resources. At the same time, the natural gas industry faces continued scrutiny for methane emissions from across the supply chain, due to methane&#39;s relatively high global warming potential (25-84x that of carbon dioxide, according to the Energy Information Administration). Currently, a variety of techniques of varied uncertainties exists to measure or estimate methane emissions from components or facilities. Currently, only one commercial system is available for quantification of component level emissions and recent reports have highlighted its weaknesses.
In order to improve accuracy and increase measurement flexibility, we have designed, developed, and implemented a novel full flow sampling system (FFS) for quantification of methane emissions and greenhouse gases based on transportation emissions measurement principles. The FFS is a modular system that consists of an explosive-proof blower(s), mass airflow sensor(s) (MAF), thermocouple, sample probe, constant volume sampling pump, laser based greenhouse gas sensor, data acquisition device, and analysis software. Dependent upon the blower and hose configuration employed, the current FFS is able to achieve a flow rate ranging from 40 to 1,500 standard cubic feet per minute (SCFM). Utilization of laser-based sensors mitigates interference from higher hydrocarbons (C2+). Co-measurement of water vapor allows for humidity correction. The system is portable, with multiple configurations for a variety of applications ranging from being carried by a person to being mounted in a hand drawn cart, on-road vehicle bed, or from the bed of utility terrain vehicles (UTVs). The FFS is able to quantify methane emission rates with a relative uncertainty of &#177; 4.4%. The FFS has proven, real world operation for the quantification of methane emissions occurring in conventional and remote facilities.

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

We have designed, developed, and implemented a novel full flow sampling system (FFS) for quantification of methane emissions and greenhouse gases from across the natural gas supply chain.

The use of natural gas continues to grow with increased discovery and production of unconventional shale resources. At the same time, the natural gas ...

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 ...

Measuring the Densities of Aqueous Glasses at Cryogenic Temperatures

We demonstrate a method for determining the vitreous phase cryogenic temperature densities of aqueous mixtures, and other samples that require rapid cooling, to prepare the desired cryogenic temperature phase. Microliter to picoliter size drops are cooled by projection into a liquid nitrogen-argon (N2-Ar) mixture. The cryogenic temperature phase of the drop is evaluated using a visual assay that correlates with X-ray diffraction measurements. The density of the liquid N2-Ar mixture is adjusted by adding N2 or Ar until the drop becomes neutrally buoyant. The density of this mixture and thus of the drop is determined using a test mass and Archimedes principle. With appropriate care in drop preparation, management of gas above the liquid cryogen mixture to minimize icing, and regular mixing of the cryogenic mixture to prevent density stratification and phase separation, densities accurate to &#60;0.5% of drops as small as 50 pL can readily be determined. Measurements on aqueous cryoprotectant mixtures provide insight into cryoprotectant action, and provide quantitative data to facilitate thermal contraction matching in biological cryopreservation.

A protocol for determining the vitreous phase densities of micro- to pico-liter size drops of aqueous mixtures at cryogenic temperatures is described.

We demonstrate a method for determining the vitreous phase cryogenic temperature densities of aqueous mixtures, and other samples that require rapid ...

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.