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This paper describes the framework for a simple system that could be applied to a wide range of buildings with varying levels of controls and measured sensor data.
Measured sensor data is used to identify different activities, like standing, sitting, lying and walking [ 10, 19, 20].
In general, there are two fusion estimation methods commonly used to process the measured sensor data [3, 4].
To construct locomotor activity data which corresponds to human locomotor activity data, we conducted the following processing of the measured sensor data.
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If the body sensor is found alive, then it checks whether it is a threshold measuring sensor or a continuous data monitoring sensor.
Since the workload cannot be directly measured using only sensor data from smartphones, we use other variables that provide implicit indicators of workload as confounding variables, such as the time that students spend at home and university and their deadlines.
Investigations in this paper were supported with: data processing system, measuring sensors and Lab View software.
The concept integrates sensor data measured online on the production line into the resource or grade/quality control model and continuously provides locally more accurate estimates.
The personalized accelerometer sensor data measured in outdoor areas could thus be used for predicting a mobile user's location in indoor areas.
The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550 380 m.
Preprandial glucose via glucometer four times daily was also measured for calibration of the CGMS sensor data (correlation between interstitial and meter glucose, r = 0.732; P < 0.0001; n = 840 measures).
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