scholarly journals A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity

1997 ◽  
Vol 44 (3) ◽  
pp. 136-147 ◽  
Author(s):  
C.V.C. Bouten ◽  
K.T.M. Koekkoek ◽  
M. Verduin ◽  
R. Kodde ◽  
J.D. Janssen
Keyword(s):  
Physical Activity ◽  
Data Processing ◽  
Daily Physical Activity ◽  
Processing Unit ◽  
Triaxial Accelerometer ◽  
Data Processing Unit

scholarly journals Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm

2011 ◽  
Vol 105 (11) ◽  
pp. 1681-1691 ◽  
Author(s):  
Kazunori Ohkawara ◽  
Yoshitake Oshima ◽  
Yuki Hikihara ◽  
Kazuko Ishikawa-Takata ◽  
Izumi Tabata ◽  
...  
Keyword(s):  
Physical Activity ◽  
Linear Regression ◽  
Cross Validation ◽  
Classification Algorithm ◽  
Daily Physical Activity ◽  
Triaxial Accelerometer ◽  
Validation Group ◽  
Activity Intensity ◽  
Metabolic Equivalents ◽  
Non Linear

We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P < 0·001) and locomotive (r 0·961, P < 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cut-off classification models. This method may be useful for field investigations as well as for self-monitoring by general users.

Pouring Point Location Method for Concrete Pump Truck

2014 ◽  
Vol 635-637 ◽  
pp. 1256-1259
Author(s):  
Qin Xing ◽  
Li Yin Zhang ◽  
Yong Wei Sun ◽  
Tao Sun ◽  
Xue Hua Yu
Keyword(s):  
Data Processing ◽  
Processing Unit ◽  
Point Location ◽  
Data Processing Unit ◽  
Displacement Sensors ◽  
Hydraulic Cylinders ◽  
Concrete Pump Truck ◽  
Concrete Pump ◽  
The Relationship ◽  
Contraction And Expansion

A method is presented for determining the pouring point location of concrete pump truck. The method does not change the structure of the concrete pump truck, only need to replace the hydraulic cylinders of booms with the hydraulic cylinders with displacement senor, and the displacement sensors are mounted in the inside of the hydraulic cylinders and do not need any extra protection. The relationship formulas between the contraction and expansion amounts of the hydraulic cylinders and the pouring point location are derived and programmed to input into a data processing unit. The contraction and expansion amounts of the hydraulic cylinders are timely acquired and inputted into the data processing unit, then the pouring point location can be displayed in real-time on the screen of radio controller, and the workers can quickly and accurately locate the pouring point of the concrete pump truck to poured point.

The RHESSI Spacecraft Instrument Data Processing Unit

2003 ◽  
pp. 115-124
Author(s):  
D. W. Curtis ◽  
P. Berg ◽  
D. Gordon ◽  
P. R. Harvey ◽  
D. M. Smith ◽  
...  
Keyword(s):  
Data Processing ◽  
Processing Unit ◽  
Data Processing Unit ◽  
Instrument Data Processing Unit
Sign in / Sign up

Export Citation Format

Share Document