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Fig. 3 Sample of acceleration data of a stroller.
By using this system, it can be handled physical data of a stroller, a bicycle and a wheelchair, the user's movement as well as the user's subjectivity data and vital data.
It can be handled physical data of a stroller, a bicycle and a wheelchair, the user's movement as well as the user's subjectivity data and vital data synthetically by introducing FDML. Figure 3 is an example of data from the smartphone acceleration sensor attached to the stroller, representing the difference between data from a gravel sidewalk with severe unevenness and a maintained sidewalk.
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As a specific example, we show the results of evaluating the estimation by machine learning for the gradient and unevenness of the sidewalk from the sensor data during the passage of a stroller.
From the results in Table 1, it is apparent that particulate concentrations are higher at the height of a stroller.
It was also observed that higher concentrations of diesel particulate matter were measured at the height of a stroller than were measured at the mouth of a mannequin.
Once the diapers have taken on the basic form of a stroller, finishing touches can be added.
For that matter, somebody out there would feel judged by the loan of either a stroller or a Baby Bjorn.
As the future works, we consider expansion of input sensor information, introduction of vital information, a variation of strollers and a variation of movement.
Mother who wipes the mouth of toddler in a stroller.
Leave enough room for a couple of adults and a stroller to fit comfortably between your stand and the road.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com