A new method to calculate external mechanical work using force-platform data in ecological situations in humans: Application to Parkinson's disease.
To accurately quantify the cost of physical activity and to evaluate the different components of energy expenditure in humans, it is necessary to evaluate external mechanical work (WEXT). Large platform systems surpass other currently used techniques. Here, we describe a calculation method for force-platforms to calculate long-term WEXT.



Each force-platform (2.46×1.60m and 3.80×2.48m) rests on 4 piezoelectric sensors. During long periods of recording, a drift in the speed of displacement of the center of mass (necessary to calculate WEXT) is generated. To suppress this drift, wavelet decomposition is used to low-pass filter the source signal. By using wavelet decomposition coefficients, the source signal can be recovered. To check the validity of WEXT calculations after signal processing, an oscillating pendulum system was first used; then, 10 healthy subjects performed a standardized exercise (squatting exercise). A medical application is also reported in eight Parkinsonian patients during the timed "get-up and go" test and compared with the same test in ten healthy subjects.



Values of WEXT with the oscillating pendulum showed that the system was accurate and reliable. During the squatting exercise, the average measured WEXT was 0.4% lower than theoretical work. WEXT and mechanical work efficiency during the "get-up and go" test in Parkinson's disease patients in comparison with that of healthy subjects were very coherent.



This method has numerous applications for studying physical activity and mechanical work efficiency in physiological and pathological conditions.
Taste and Food Behavior Center (CSGA), UMR 6265 CNRS, UMR 1324 INRA, University of Burgundy, Dijon, France.
Vincent
V
Gigot
Department of Geriatrics, University Hospital of Dijon, Dijon, France.
Virginie
V
Van Wymelbeke
Platform of Technology Investigation, INSERM CIC 1432, University Hospital of Dijon, Dijon, France ; INSERM U1093, University of Burgundy, Dijon, France.
Davy
D
Laroche
Taste and Food Behavior Center (CSGA), UMR 6265 CNRS, UMR 1324 INRA, University of Burgundy, Dijon, France.
Thomas
T
Mouillot
Taste and Food Behavior Center (CSGA), UMR 6265 CNRS, UMR 1324 INRA, University of Burgundy, Dijon, France; Department of Clinical Neurophysiology, University Hospital of Dijon, Dijon, France.
Agnès
A
Jacquin-Piques
Laboratory of Electronics, Information and Image (LE2I), UMR CNRS 5158, University of Burgundy, Dijon, France.
Matthieu
M
Rossé
Taste and Food Behavior Center (CSGA), UMR 6265 CNRS, UMR 1324 INRA, University of Burgundy, Dijon, France.
Michel
M
Tavan
Taste and Food Behavior Center (CSGA), UMR 6265 CNRS, UMR 1324 INRA, University of Burgundy, Dijon, France; Department of Hepato-Gastroenterology, University Hospital of Dijon, Dijon, France. Electronic address: laurent.brondel@u-bourgogne.fr.
Laurent
L
Brondel
D000328:Adult; D001696:Biomechanical Phenomena / Q000502:physiology; D016022:Case-Control Studies; D004734:Energy Metabolism / Q000502:physiology*; D005080:Exercise Test; D006801:Humans; D008297:Male; D008954:Models, Biological; D010300:Parkinson Disease / Q000503:physiopathology*
Energy expenditure; External mechanical work; Human physical activity; Parkinson's disease; Work efficiency