Run: A Subject Seven Novel

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If it was necessary to estimate the meal times to a higher degree of accuracy less than five minutes, a solution could be found by changing the time to calculate the MQF in accordance with the required accuracy. However, it is not theoretically possible to reduce the error to zero because this algorithm uses statistical processing.

Next, we discuss the impact of individual differences in shape changes to the ear canal on the meal time estimation results.

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All subjects had large MQF values for the actual meal times; however, these values differed for each subject. When the experimental results for Mean1 were compared between the seven subjects, we observed the largest Mean1 in subject A, at 1. The difference in these values can be indicative of the effect of individual differences in shape changes to the ear canal. The MQF is calculated from the sum of the mean and standard deviation of the amount of change based on the output voltage of the sensor.

When the MQF is high, the mean and standard deviation for the amount of change are also large, which in turn indicate a substantial change in the distance between the sensor and eardrum. In the case of this experiment, subject A exhibited the largest shape change to the ear canal, while subject B exhibited the smallest. Shape changes to the ear canal differ according to each individual, and these individual differences directly influence the MQF. These results suggest that meal times can be estimated within the range of error by setting a threshold based on the maximum and minimum MQF values of each subject.

This is also performed in this algorithm, without being influenced by individual differences in shape changes to the ear canal. In the future, we aim to improve the accuracy of this algorithm by continuing to study meal time windows e. We next discuss if chewing eating can be differentiated from running.


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We found that this sensor can be used to differentiate eating from running within the scope of our experimental results. This is because the measured results for the left and right ear canals during running strongly correlated with each other, whereas the results during chewing did not. If these activities are categorized based on correlation coefficients, they can be distinguished from each other. In particular, the measured waveforms of the left and right ear canals during running are similar, as shown in Figure 6 , whereas measurements during chewing are not similar between the left and right ear canals.

Table 3 shows that the correlation coefficient r during running was 0. Meanwhile, the correlation coefficient r during chewing was 0. Therefore, these findings suggest that this is a viable method for using a single fixed threshold to differentiate eating from running, in marked contrast with the mealtime estimation algorithm, which adaptively varies the threshold every time a user wears the sensor. For example, running is identified when the correlation coefficient r calculated from the measured results of shape changes to the left and right ear canals exhibits a strong correlation equal to or greater than 0.

In contrast, eating is identified when the correlation coefficient r exhibits no correlation or a weak correlation equal to or lower than 0. Now, we further discuss running and chewing based on the data presented in Figure 6 and Figure 7. The running waveform in Figure 6 shows that the left and right ear canal waveforms are similar during running. On the other hand, the chewing waveform in Figure 7 reveals that the left and right ear canal waveforms differ during chewing.

This indicates that during running, the same waveform resulting from running is superimposed onto the measured results for the left and right ear canals. The reason for this superimposition of the waveform onto the measured results is due to vibrations generated by the foot. The vibrations that occur when the foot makes contact with the ground are transmitted to the ear via the body and are consequently measured by the sensor.

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Running involves one foot at a time making contact with the ground; however, the same running waveform is superimposed onto the measured results for shape changes to the left and right ear canals. This is because the transmission pathways of the vibrations generated by the feet are the same. The pathways by which the vibrations that occur when the feet make contact with the ground differ in how they are transmitted between the left and right feet from the soles of the feet up to the hips.

However, from the hips to the lower back, chest, neck, and head, vibrations arrive via the same pathway, regardless of the foot from which they originated.

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This is why transmitted vibrations are not biased toward any side and why running leads to the same results being superimposed onto the measured results for shape changes to the left and right ear canals. Therefore, the measured results for shape changes to the left and right ear canals associated with running are similar. In contrast, the measured results for shape changes to the left and right ear canals associated with chewing are not the same, as evident in Figure 7. Chewing causes the temporal muscles to expand and contract, which results in movement of the ear canal.

If shape changes to the left and right ear canals associated with chewing were the same, the temporal muscles would expand and contract at the same time and in the same manner. Furthermore, if the left and right temporal muscles were to expand and contract at the same time and in the same way as a result of chewing, the jaw would move precisely up and down. However, the actual jaw movement during chewing is biased toward either the left or right. Because the left and right temporal muscles do not expand and contract at the same time or in the same way, shape changes to the ear canal associated with chewing differ between the left and right sides.

Based on the above findings, we intend to estimate the amount of movement by analyzing the waveforms during running. In addition to estimating the amount of movement, we are currently conducting pulse measurements within the ear canal by using our proposed method and the same type of optical sensor.

If we can estimate the amount of movement and improve the accuracy of pulse measurements, we can create a device capable of supporting a healthy lifestyle among users regarding exercise and diet by simply obtaining measurements from just one of the optical sensors inserted into the left or right ear canals. In this paper, we described a method for estimating meal times based on temporal changes in the amount of light received by a small optical sensor composed of an LED and a phototransistor inserted in the right ear canal.

The device shines light toward the inner ear canal and receives light reflected back by the ear canal. An experiment performed using seven subjects was successful in estimating meal times in all subjects within a set range of error. Furthermore, an analysis of the measured values during running and chewing obtained from the same sensor inserted into the left and right ear canals of four subjects revealed that measurements from the left and right ear canals were strongly correlated during running, but not correlated during chewing. These findings allow running and chewing to be differentiated based on correlation coefficients.

In the future, we intend to develop a method for estimating the amount of movement of the body by analyzing waveforms during running. In addition to improving our meal time estimation algorithm and continuing to investigate methods for differentiating running and chewing, we aim to conduct further experiments using larger subject samples.

We also hope to establish a method for measuring the pulse within the ear canal using our proposed method and the same type of optical sensor. Among the research on detecting chewing sounds via the ear, there is a study that used a microphone worn in the ear to sense chewing sounds transmitted through the bone [ 24 ]. More accurate meal time measurements might be achieved by combining the results of our study with those from the study that used a microphone.

By combining all these research results, we aim to develop a device capable of supporting a healthy lifestyle of users regarding both exercise and diet. National Center for Biotechnology Information , U. Journal List Sensors Basel v.

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Passaro, Academic Editor. Author information Article notes Copyright and License information Disclaimer. Received Oct 25; Accepted Jan This article has been cited by other articles in PMC. Abstract In this study, we describe a technique for estimating meal times using an earphone-type wearable sensor. Keywords: wearable device, ear canal, meal time estimation, body vibration on running, dietary support. Background and Objective The use of wearable devices is growing in popularity in the field of healthcare [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].

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Open in a separate window. Figure 1. Materials and Methods 2.


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Measurement Principle and Sensor Prototype Figure 2 presents a schematic diagram of a sensor designed to estimate meal times. Figure 2. Figure 3. Figure 4. Meal Time Estimation Algorithm To obtain preliminary data to design a meal time estimation algorithm, we measured the movement of the ear canal of subject X by means of the earphone-type sensor shown in Figure 4. Figure 5. Figure 6. Figure 7. Data Collection Protocol The following two experiments were performed to confirm if meal times could be measured by the proposed method and if eating could be differentiated from running.

Results The results of the questionnaire and meal time estimation algorithm for subjects A to G are presented in Table 1. Table 1 Experimental results of meal time measurements. Figure 8. Table 2 Experimental results.