Mariners' physical activity classification at sea using a wrist worn wearable sensor.
A long term sea voyage imposes a special living environment on mariners that directly influences their physical health. To our best knowledge, there have been few research efforts that evaluate mariners' physical health during sea life. This study aims to develop wearable based mariner physical activity classification models. Twenty eight participants (n=7 females, n=21 males, mean age=21.4, and mean BMI=22.9) wore a single accelerometer on their dominant hand. The wrist acceleration data were collected and analyzed to extract wrist motion features compared to the criterion measures (i.e. direct observation) including four major physical activity types in a maritime setting. Three machine learning algorithms were applied to develop an accurate classification model. The results of the criterion based classification show that more than 95% of mariners' daily physical activities were accurately classified. Based on the experimental results, we conclude that the wrist motion features efficiently differentiate major physical activity patterns in a maritime environment. The proposed physical activity classification models can be used as an objective measurement of mariners' physical activity levels during their long voyage.