Modelling Human Locomotion
Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or deman...
Ausführliche Beschreibung
Autor*in: |
Olds, Tim [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2001 |
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Schlagwörter: |
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Anmerkung: |
© Adis International Limited 2001 |
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Übergeordnetes Werk: |
Enthalten in: Sports medicine - Berlin [u.a.] : Springer, 1984, 31(2001), 7 vom: Juni, Seite 497-509 |
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Übergeordnetes Werk: |
volume:31 ; year:2001 ; number:7 ; month:06 ; pages:497-509 |
Links: |
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DOI / URN: |
10.2165/00007256-200131070-00005 |
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Katalog-ID: |
SPR035621656 |
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520 | |a Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/ orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a ‘level playing field’ to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables. | ||
650 | 4 | |a Power Demand |7 (dpeaa)DE-He213 | |
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650 | 4 | |a Modify Power Supply |7 (dpeaa)DE-He213 | |
650 | 4 | |a Cycling Speed |7 (dpeaa)DE-He213 | |
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10.2165/00007256-200131070-00005 doi (DE-627)SPR035621656 (SPR)00007256-200131070-00005-e DE-627 ger DE-627 rakwb eng Olds, Tim verfasserin aut Modelling Human Locomotion 2001 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Adis International Limited 2001 Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/ orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a ‘level playing field’ to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables. Power Demand (dpeaa)DE-He213 Rolling Resistance (dpeaa)DE-He213 Metabolic Power (dpeaa)DE-He213 Modify Power Supply (dpeaa)DE-He213 Cycling Speed (dpeaa)DE-He213 Enthalten in Sports medicine Berlin [u.a.] : Springer, 1984 31(2001), 7 vom: Juni, Seite 497-509 (DE-627)32064717X (DE-600)2025521-4 1179-2035 nnns volume:31 year:2001 number:7 month:06 pages:497-509 https://dx.doi.org/10.2165/00007256-200131070-00005 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2001 7 06 497-509 |
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10.2165/00007256-200131070-00005 doi (DE-627)SPR035621656 (SPR)00007256-200131070-00005-e DE-627 ger DE-627 rakwb eng Olds, Tim verfasserin aut Modelling Human Locomotion 2001 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Adis International Limited 2001 Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/ orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a ‘level playing field’ to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables. Power Demand (dpeaa)DE-He213 Rolling Resistance (dpeaa)DE-He213 Metabolic Power (dpeaa)DE-He213 Modify Power Supply (dpeaa)DE-He213 Cycling Speed (dpeaa)DE-He213 Enthalten in Sports medicine Berlin [u.a.] : Springer, 1984 31(2001), 7 vom: Juni, Seite 497-509 (DE-627)32064717X (DE-600)2025521-4 1179-2035 nnns volume:31 year:2001 number:7 month:06 pages:497-509 https://dx.doi.org/10.2165/00007256-200131070-00005 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2001 7 06 497-509 |
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10.2165/00007256-200131070-00005 doi (DE-627)SPR035621656 (SPR)00007256-200131070-00005-e DE-627 ger DE-627 rakwb eng Olds, Tim verfasserin aut Modelling Human Locomotion 2001 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Adis International Limited 2001 Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/ orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a ‘level playing field’ to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables. Power Demand (dpeaa)DE-He213 Rolling Resistance (dpeaa)DE-He213 Metabolic Power (dpeaa)DE-He213 Modify Power Supply (dpeaa)DE-He213 Cycling Speed (dpeaa)DE-He213 Enthalten in Sports medicine Berlin [u.a.] : Springer, 1984 31(2001), 7 vom: Juni, Seite 497-509 (DE-627)32064717X (DE-600)2025521-4 1179-2035 nnns volume:31 year:2001 number:7 month:06 pages:497-509 https://dx.doi.org/10.2165/00007256-200131070-00005 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2001 7 06 497-509 |
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10.2165/00007256-200131070-00005 doi (DE-627)SPR035621656 (SPR)00007256-200131070-00005-e DE-627 ger DE-627 rakwb eng Olds, Tim verfasserin aut Modelling Human Locomotion 2001 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Adis International Limited 2001 Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/ orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a ‘level playing field’ to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables. Power Demand (dpeaa)DE-He213 Rolling Resistance (dpeaa)DE-He213 Metabolic Power (dpeaa)DE-He213 Modify Power Supply (dpeaa)DE-He213 Cycling Speed (dpeaa)DE-He213 Enthalten in Sports medicine Berlin [u.a.] : Springer, 1984 31(2001), 7 vom: Juni, Seite 497-509 (DE-627)32064717X (DE-600)2025521-4 1179-2035 nnns volume:31 year:2001 number:7 month:06 pages:497-509 https://dx.doi.org/10.2165/00007256-200131070-00005 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2001 7 06 497-509 |
allfieldsSound |
10.2165/00007256-200131070-00005 doi (DE-627)SPR035621656 (SPR)00007256-200131070-00005-e DE-627 ger DE-627 rakwb eng Olds, Tim verfasserin aut Modelling Human Locomotion 2001 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Adis International Limited 2001 Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/ orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a ‘level playing field’ to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables. Power Demand (dpeaa)DE-He213 Rolling Resistance (dpeaa)DE-He213 Metabolic Power (dpeaa)DE-He213 Modify Power Supply (dpeaa)DE-He213 Cycling Speed (dpeaa)DE-He213 Enthalten in Sports medicine Berlin [u.a.] : Springer, 1984 31(2001), 7 vom: Juni, Seite 497-509 (DE-627)32064717X (DE-600)2025521-4 1179-2035 nnns volume:31 year:2001 number:7 month:06 pages:497-509 https://dx.doi.org/10.2165/00007256-200131070-00005 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2001 7 06 497-509 |
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Enthalten in Sports medicine 31(2001), 7 vom: Juni, Seite 497-509 volume:31 year:2001 number:7 month:06 pages:497-509 |
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Enthalten in Sports medicine 31(2001), 7 vom: Juni, Seite 497-509 volume:31 year:2001 number:7 month:06 pages:497-509 |
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abstract |
Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/ orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a ‘level playing field’ to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables. © Adis International Limited 2001 |
abstractGer |
Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/ orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a ‘level playing field’ to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables. © Adis International Limited 2001 |
abstract_unstemmed |
Abstract Mathematical models of performance in locomotor sports are reducible to functions of the sort y = f(x) where y is some performance variable, such as time, distance or speed, and x is a combination of predictor variables which may include expressions for power (or energy) supply and/or demand. The most valid and useful models are first-principles models that equate expressions for power supply and power demand. Power demand in cycling is the sum of the power required to overcome air resistance and rolling resistance, the power required to change the kinetic energy of the system, and the power required to ride up or down a grade. Power supply is drawn from aerobic and anaerobic sources, and modellers must consider not only the rate but also the kinetics and pattern of power supply. The relative contributions of air resistance to total demand, and of aerobic energy to total supply, increase curvilinearly with performance time, while the importance of other factors decreases. Factors such as crosswinds, aerodynamic accessories and drafting can modify the power demand in cycling, while body configuration/ orientation and altitude will affect both power demand and power supply, often in opposite directions. Mathematical models have been used to solve specific problems in cycling, such as the chance of success of a breakaway, the optimal altitude for performance, creating a ‘level playing field’ to compare performances for selection purposes, and to quantify, in the common currency of minutes and seconds, the effects on performance of changes in physiological, environmental and equipment variables. The development of crank dynamometers and portable gas-analysis systems, combined with a modelling approach, will in the future provide valuable information on the effect of changes in equipment, configuration and environment on both supply and demand-side variables. © Adis International Limited 2001 |
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title_short |
Modelling Human Locomotion |
url |
https://dx.doi.org/10.2165/00007256-200131070-00005 |
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up_date |
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