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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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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