An Experimental Protocol to Model Recovery of Anaerobic Work Capacity

Models of fatigue are based on physiological parameters such as Critical Power (CP) and Anaerobic Work Capacity (AWC). CP is a theoretical threshold value that a human can generate for an indefinite amount of time and AWC represents a finite expendable amount of anaerobic energy at intensities above CP. There is an increasing interest in developing mathematical models of energy expenditure and recovery for athletic training and human performance. The objective of this research is to propose and validate a model for recovery of AWC during a post exertion recovery interval of cycling. A cycling ergometer study is proposed which involves a VO2max ramp test to determine gas exchange threshold, a 3-min all-out intensity test to determine CP and AWC, and exertion-recovery interval tests to understand recovery of AWC. The results will be used to build a human in the loop control system to optimize cycling performance

Incorporating uncertainty into diagnostic analysis of mechanical systems

Analyzing systems during the conceptual stages of design for characteristics essential to the ease of fault diagnosis is important in today's mechanical systems because consumers and manufacturers are becoming increasingly concerned with cost incurred over the life cycle of the system. The increase in complexity of modem mechanical systems can often lead to systems that are difficult to diagnose, and therefore require a great deal of time and money to return the system to working condition. Mechanical systems optimized in the area of diagnosability can lead to a reduction of life cycle costs for both consumers and manufacturers and increase the useable life of the system. A methodology for completing diagnostic analysis of mechanical systems is presented. First, a diagnostic model, based on components and system indications, is constructed. Bayes formula is used in conjunction with information extracted from the Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), component reliability, and prior system knowledge to construct the diagnostic model. The diagnostic model, when presented in matrix form, is denoted as the Component-Indication Joint Probability Matrix. The Component-Indication Joint Probability Matrix presents the joint probabilities of all possible mutually exclusive diagnostic events in the system. Next, methods are developed to mathematically manipulate the Component-Indication Joint Probability Matrix into two matrices, (1) the Replacement Matrix and (2) the Replacement Probability Matrix. These matrices are used to compute a set of diagnosability metrics. The metrics are useful for comparing alternative designs and addressing diagnostic problems to the system, component and indication level, during the conceptual stages of design. Additionally, the metrics can be used to predict cost associated with fault isolation over the life cycle of the system. The methodology is applied to a hypothetical example problem for illustration, and applied to a physical system, an icemaker, for validation

A survey of mathematical models of human performance using power and energy

The ability to predict the systematic decrease of power during physical exertion gives valuable insights into health, performance, and injury. This review surveys the research of power-based models of fatigue and recovery within the area of human performance. Upon a thorough review of available literature, it is observed that the two-parameter critical power model is most popular due to its simplicity. This two-parameter model is a hyperbolic relationship between power and time with critical power as the power-asymptote and the curvature constant denoted by W′. Critical power (CP) is a theoretical power output that can be sustained indefinitely by an individual, and the curvature constant (W′) represents the amount of work that can be done above CP. Different methods and models have been validated to determine CP and W′, most of which are algebraic manipulations of the two-parameter model. The models yield different CP and W′ estimates for the same data depending on the regression fit and rounding off approximations. These estimates, at the subject level, have an inherent day-to-day variability called intra-individual variability (IIV) associated with them, which is not captured by any of the existing methods. This calls for a need for new methods to arrive at the IIV associated with CP and W′. Furthermore, existing models focus on the expenditure of W′ for efforts above CP and do not model its recovery in the sub-CP domain. Thus, there is a need for methods and models that account for (i) the IIV to measure the effectiveness of individual training prescriptions and (ii) the recovery of W′ to aid human performance optimization

Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks

Assembly time estimation is traditionally a time-intensive manual process that requires detailed geometric and process information, which is often subjective and qualitative in nature. As a result, assembly time estimation is rarely applied during early design iterations. In this paper, the authors explore the possibility of automating the assembly time estimation process while reducing the level of design detail required. In this approach, they train artificial neural networks (ANNs) to estimate the assembly times of vehicle subassemblies using either assembly connectivity or liaison graph properties, respectively, as input data. The effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results indicate that this method can provide time estimates of an assembly process with ±15% error while relying exclusively on the geometric part information rather than process instructions

Experimental Modeling of Cyclists Fatigue and Recovery Dynamics Enabling Optimal Pacing in a Time Trial

Improving a cyclist performance during a time-trial effort has been a challenge for sport scientists for several decades. There has been a lot of work on understanding the physiological concepts behind it. The concepts of Critical Power (CP) and Anaerobic Work Capacity (AWC) have been discussed often in recent cycling performance related articles. CP is a power that can be maintained by a cyclist for a long time; meaning pedaling at or below this limit, theoretically, can be continued for infinite amount of time. However, there is a limited source of energy for generating power above CP. This limited energy source is AWC. After burning energy from this tank, a cyclist can recover some by pedaling below CP. In this paper we utilize the concepts of CP and AWC to mathematically model muscle fatigue and recovery of a cyclist. Then, the models are used to formulate an optimal control problem for a time trial effort on a 10.3 km course located in Greenville SC. The course is simulated in a laboratory environment using a CompuTrainer. At the end, the optimal simulation results are compared to the performance of one subject on CompuTrainer.Comment: 6 pages, 8 figure

Modeling the Expenditure and Recovery of Anaerobic Work Capacity in Cycling

The objective of this research is to model the expenditure and recovery of Anaerobic Work Capacity (AWC) as related to Critical Power (CP) during cycling. CP is a theoretical value at which a human can operate indefinitely and AWC is the energy that can be expended above CP. There are several models to predict AWC-depletion, however, only a few to model AWC recovery. A cycling study was conducted with nine recreationally active subjects. CP and AWC were determined by a 3-min all-out test. The subjects performed interval tests at three recovery intervals (15 s, 30 s, or 60 s) and three recovery powers (0.50CP, 0.75CP, and CP). It was determined that the rate of expenditure exceeds recovery and the amount of AWC recovered is influenced more by recovery power level than recovery duration. Moreover, recovery rate varies by individual and thus, a robust mathematical model for expenditure and recovery of AWC is needed

An Experimental Protocol to Model Recovery of Anaerobic Work Capacity

Models of fatigue are based on physiological parameters such as Critical Power (CP) and Anaerobic Work Capacity (AWC). CP is a theoretical threshold value that a human can generate for an indefinite amount of time and AWC represents a finite expendable amount of anaerobic energy at intensities above CP. There is an increasing interest in developing mathematical models of energy expenditure and recovery for athletic training and human performance. The objective of this research is to propose and validate a model for recovery of AWC during a post exertion recovery interval of cycling. A cycling ergometer study is proposed which involves a VO2max ramp test to determine gas exchange threshold, a 3-min all-out intensity test to determine CP and AWC, and exertion-recovery interval tests to understand recovery of AWC. The results will be used to build a human in the loop control system to optimize cycling performance

Modeling the Expenditure and Recovery of Anaerobic Work Capacity in Cycling

The objective of this research is to model the expenditure and recovery of Anaerobic Work Capacity (AWC) as related to Critical Power (CP) during cycling. CP is a theoretical value at which a human can operate indefinitely and AWC is the energy that can be expended above CP. There are several models to predict AWC-depletion, however, only a few to model AWC recovery. A cycling study was conducted with nine recreationally active subjects. CP and AWC were determined by a 3-min all-out test. The subjects performed interval tests at three recovery intervals (15 s, 30 s, or 60 s) and three recovery powers (0.50CP, 0.75CP, and CP). It was determined that the rate of expenditure exceeds recovery and the amount of AWC recovered is influenced more by recovery power level than recovery duration. Moreover, recovery rate varies by individual and thus, a robust mathematical model for expenditure and recovery of AWC is needed