For total EE , correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices .
For active EE , correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices .
The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome .
Triaxial and multisensor devices tend to be more valid monitors .
The assessment of physical activity in healthy population s and in those with chronic diseases is challenging .
The aim of this systematic review was to identify whether available activity monitors ( AM ) have been appropriately vali date d for use in assessing physical activity in these groups .
Energy expenditure prediction using a miniaturized ear-worn sensor.
PURPOSE This study aim ed to predict human energy expenditure and activity type using a miniature lightweight ear-worn inertia sensor and a novel pattern recognition algorithm for activity detection . METHODS This study used a protocol of 11 activities of daily living : lying down , st and ing , computer work , vacuuming , stairs , slow walking , brisk walking , slow running , fast running , cycling , and rowing .  Subjects included 25 healthy r and omized subjects ( 18 males and 7 females ) . Each participant wore the ear sensor to record posture and linear acceleration  , as well as the Cosmed K4b system for indirect calorimetry . The main outcome measure was the continuous energy expenditure per minute prediction for both task-known and task-blind estimation . RESULTS The values for METs predicted using the proposed algorithm and the measured METs using the K4b showed good agreement with low values for the systematic bias ( lying down=0.01 , st and ing=-0.02 , computer work=-0.04 , vacuuming=-0.17 , stairs=-0.02 , slow walking=0.01 , fast walking=0.04 , slow running=0.14 , fast running=-0.35 , cycling=0.32 , and rowing=0.10 ) . For task-blind prediction , the agreement between predicted and measured METs is also good with low values of the systematic bias ( lying down=0.11 , st and ing=0.14 , computer work=-0.06 , vacuuming=0.47 , stairs=-0.47 , slow walking=0.53 , fast walking=-0.11 , slow running=0.83 , fast running=-1.18 , cycling=0.31 , and rowing=-0.67 ) . Activity is also well predicted ( for task-blind prediction ) with an overall success rate of 88.99 % and individual correct classification rates of lying down=89.62 % , st and ing/computer work=99.10 % , vacuuming=76.60 % , stairs=89.13 % , walking=85.11 % , running=98.96 % , and cycling=79.79 % . CONCLUSIONS The ear-worn sensor presented in this work is a novel lightweight device that can be used to predict energy expenditure for a range of activities without behavior interference or modification
For total EE , correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices .
For active EE , correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices .
The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome .
Triaxial and multisensor devices tend to be more valid monitors .
The assessment of physical activity in healthy population s and in those with chronic diseases is challenging .
The aim of this systematic review was to identify whether available activity monitors ( AM ) have been appropriately vali date d for use in assessing physical activity in these groups .