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Kinetics and energetics during exercise: A model evaluation
S50
Abstracts / Gait & Posture 30S (2009) S26–S74
and the negative work (slope of Etot < 0). Total muscular metabolic task cost was calculated according to [1]. For this calculation the efficiency for positive work was set to 25% and for negative work to −120%. A resting oxygen uptake of 0.21 l O2 min−1 was added giving the total muscle metabolic rate. Results: Model results of energy expenditure were smaller but close to the values obtained experimentally. However, at the higher frequency, the model underestimated more measured oxygen consumption. Results of oxygen consumption are shown in Fig. 1a and 1b. Discussion: Preliminary results obtained in comparing model predictions with experimental data are promising. The underestimation of energy expenditure predicted by the model at the higher frequency can be explained with an increase in energy consumption of the non muscular mass with movement velocity [3]. In the work by Laursen [1] results were totally in agreement with experimental data: this may be due to the fact that the model was applied to walking uphill, downhill and horizontally, at the same relatively low speed.
Fig. 1. Curves of normal and clinical case (hemiplegic patient).
Kinetics and energetics during exercise: A model evaluation M.C. Bisi ∗ , F. Riva, R. Stagni, G. Gnudi DEIS, University of Bologna, Cesena, Italy
References
Introduction: During exercise the skeletal muscular tissues are exposed to both mechanical and metabolic loading. In 2000, a simple biomechanical model was proposed in order to assess both mechanical and metabolic loading of musculoskeletal system during load carrying horizontally, uphill and downhill [1]. The aim of the present study was to test this model for prediction of whole-body energy turnover from kinematic and anthropometric data during running on an elliptic ergometer. Methods: Three participant [25 ± 1y, 1.79 ± 0.01m, 75 ± 3Kg] performed a 5 min exercise on an elliptic cardiovascular training equipment (Vario, Technogym, Italy) at two frequencies (90, 100 spm) and two power levels (1, 5). During the tests, kinematic (SmartE, BTS, Milan, Italy) and gas exchange data (Quark b2, Cosmed, Italy) were acquired. A 12 segment model of the subject was obtained from kinematic data using CAST protocol [2]. The instantaneous total mechanical energy in Joules was calculated from
[1] Laursen B, et al. Applied Ergonomics 2000;31:159–66. [2] Cappozzo A, et al. Clin Biomech 1995;10(4):171–8. [3] Poole DC. J Appl Physiol 1992;72:805–10.
Etot =
Epot +
Etrans +
Erot
(1)
where is the summation over the 12 segments, Epot the potential energy, Etrans the kinetic translational energy and Erot the kinetic rotational energy of each segment [1]. Changes in the total body energy, Erot , result when work is done by the subjects; an increase in the curve of the energy level with time corresponds to a phase of positive work; a decrease in the curve to a phase of negative work. It is assumed that energy transfers both between body segments and within body segments and that the muscles have to perform both the positive (slope of Etot > 0)
doi:10.1016/j.gaitpost.2009.07.043 Design of a wearable kit for the assessment of the gait properties in daily tele-rehabilitation D. Giansanti 1,∗ , G. Maccioni 1 , S. Grigioni 1 , A. Giordano 2 1 2
Morelli 1 , V.
Macellari 1 , M.
Istituto Superiore di Sanità, Roma, Italia Fondazione Maugeri,Veruno (NO), Italia
Introduction: The assessment of gait parameters during rehabilitation remains a strong interest to document the evolution of the patient’s response to the therapy. The walking patterns follow the same evolution, while the subject’s ability allows him to face from handrails instrumented walkways to walkways based on canes with 4,3,2,1 to-ground supports, to the use of the properly designed prosthesis (as for example the Codivilla spring [1]) to finally abandon any supports. Objective assessment of this evolution is still performed using traditional, sub-optimal approaches, hence the interest in design and construction of equipment based on properly designed walkways. The daily use of the commonly used optoelectronic gait-analysis methodologies is obviously overstated, expensive and in general not feasible at patient’s home for telemonitoring purposes. Furthermore too many parameters are furnished by optoelectronic methodologies with the need of a very
Fig. 1. Mean and standard deviation of oxygen uptake (l O2 min−1 ) predicted by the model (mod, black triangles), and measured with indirect calorimetry (exp, grey squares) for power level 1 (Fig. 1a) and power level 5 (Fig. 1b).
Abstracts / Gait & Posture 30S (2009) S26–S74
and the negative work (slope of Etot < 0). Total muscular metabolic task cost was calculated according to [1]. For this calculation the efficiency for positive work was set to 25% and for negative work to −120%. A resting oxygen uptake of 0.21 l O2 min−1 was added giving the total muscle metabolic rate. Results: Model results of energy expenditure were smaller but close to the values obtained experimentally. However, at the higher frequency, the model underestimated more measured oxygen consumption. Results of oxygen consumption are shown in Fig. 1a and 1b. Discussion: Preliminary results obtained in comparing model predictions with experimental data are promising. The underestimation of energy expenditure predicted by the model at the higher frequency can be explained with an increase in energy consumption of the non muscular mass with movement velocity [3]. In the work by Laursen [1] results were totally in agreement with experimental data: this may be due to the fact that the model was applied to walking uphill, downhill and horizontally, at the same relatively low speed.
Fig. 1. Curves of normal and clinical case (hemiplegic patient).
Kinetics and energetics during exercise: A model evaluation M.C. Bisi ∗ , F. Riva, R. Stagni, G. Gnudi DEIS, University of Bologna, Cesena, Italy
References
Introduction: During exercise the skeletal muscular tissues are exposed to both mechanical and metabolic loading. In 2000, a simple biomechanical model was proposed in order to assess both mechanical and metabolic loading of musculoskeletal system during load carrying horizontally, uphill and downhill [1]. The aim of the present study was to test this model for prediction of whole-body energy turnover from kinematic and anthropometric data during running on an elliptic ergometer. Methods: Three participant [25 ± 1y, 1.79 ± 0.01m, 75 ± 3Kg] performed a 5 min exercise on an elliptic cardiovascular training equipment (Vario, Technogym, Italy) at two frequencies (90, 100 spm) and two power levels (1, 5). During the tests, kinematic (SmartE, BTS, Milan, Italy) and gas exchange data (Quark b2, Cosmed, Italy) were acquired. A 12 segment model of the subject was obtained from kinematic data using CAST protocol [2]. The instantaneous total mechanical energy in Joules was calculated from
[1] Laursen B, et al. Applied Ergonomics 2000;31:159–66. [2] Cappozzo A, et al. Clin Biomech 1995;10(4):171–8. [3] Poole DC. J Appl Physiol 1992;72:805–10.
Etot =
Epot +
Etrans +
Erot
(1)
where is the summation over the 12 segments, Epot the potential energy, Etrans the kinetic translational energy and Erot the kinetic rotational energy of each segment [1]. Changes in the total body energy, Erot , result when work is done by the subjects; an increase in the curve of the energy level with time corresponds to a phase of positive work; a decrease in the curve to a phase of negative work. It is assumed that energy transfers both between body segments and within body segments and that the muscles have to perform both the positive (slope of Etot > 0)
doi:10.1016/j.gaitpost.2009.07.043 Design of a wearable kit for the assessment of the gait properties in daily tele-rehabilitation D. Giansanti 1,∗ , G. Maccioni 1 , S. Grigioni 1 , A. Giordano 2 1 2
Morelli 1 , V.
Macellari 1 , M.
Istituto Superiore di Sanità, Roma, Italia Fondazione Maugeri,Veruno (NO), Italia
Introduction: The assessment of gait parameters during rehabilitation remains a strong interest to document the evolution of the patient’s response to the therapy. The walking patterns follow the same evolution, while the subject’s ability allows him to face from handrails instrumented walkways to walkways based on canes with 4,3,2,1 to-ground supports, to the use of the properly designed prosthesis (as for example the Codivilla spring [1]) to finally abandon any supports. Objective assessment of this evolution is still performed using traditional, sub-optimal approaches, hence the interest in design and construction of equipment based on properly designed walkways. The daily use of the commonly used optoelectronic gait-analysis methodologies is obviously overstated, expensive and in general not feasible at patient’s home for telemonitoring purposes. Furthermore too many parameters are furnished by optoelectronic methodologies with the need of a very
Fig. 1. Mean and standard deviation of oxygen uptake (l O2 min−1 ) predicted by the model (mod, black triangles), and measured with indirect calorimetry (exp, grey squares) for power level 1 (Fig. 1a) and power level 5 (Fig. 1b).