the following detailed description , including the use of patient data , is intended to be exemplary of a preferred method of utilizing the concepts of the present invention and is not intended to be exhaustive or limiting in any manner with respect to similar methods and additional or other steps which might occur to those skilled in the art . the following description further utilizes illustrative examples , which are believed sufficient to convey an adequate understanding of the broader concepts to those skilled in the art , and exhaustive examples are believed unnecessary . it is becoming increasingly clear in the fitness markets that individuals are interested in a more scientific approach to exercise training using objective measurements rather than estimates . the objective information obtained from such testing may prove valuable on several levels including : 1 ) assessing the training effect of an exercise prescription , 2 ) assessing the compliance with an exercise prescription , and 3 ) providing goals and incentives to improve the wellness of individuals . the present invention includes a pattern recognition system consisting of a ) a cardiopulmonary exercise gas exchange analyzer that gathers the observations to be classified or described , b ) a feature extraction mechanism that computes numeric information from the observations , and c ) a classification or description scheme that does the actual job of classifying or describing observations based on the extracted features . as indicated and shown in fig2 , the general class of data utilized in the present invention , cardiopulmonary exercise gas exchange measurements , is obtained 1 ) at rest , 2 ) during physical exercise testing performed in accordance with a standardized workload protocol as the forcing function to elicit physiologic changes resulting from the workload , and 3 ) during a short recovery period following exercise termination . the data measured during exercise quantifies how an individual is able to function in the physical world in terms of the physiologic changes that the individual experiences when engaged in the performance of daily physical work . the physiologic changes are measured using a cardiopulmonary exercise testing system ( cpx ) to measure selected variables associated with oxygen consumption , vo 2 , carbon dioxide production , vco 2 , end tidal co 2 , petco 2 , ventilation , ve , respiratory exchange ratio , rer , and heart rate , hr . as indicated , the data gathering aspect of the invention involves known techniques and analyses , and the calculations for formulating predictive assessments are readily available in the scientific literature . however , by means of aspects of the feature extraction mechanism and the classification scheme , the present invention enables an observer to gain new and valuable insight into the present condition and condition trends in patients . thus , in accordance with a preferred method , a cardiopulmonary exercise gas exchange analysis is made for each test data set . the performance of such a test is well understood by individuals skilled in the art , and no further explanation of this is believed necessary . with this in mind typical hardware is shown in fig1 , which illustrates typical equipment whereby a cardiopulmonary exercise test ( cpx ) may be conducted and the results displayed in accordance with the method of the present invention . the system is seen to include a data processing device , here shown as a personal computer of pc 12 , which comprises a video display terminal 14 with associated mouse 16 , report printer 17 and a keyboard 18 . the system further has a floppy disc handler 20 with associated floppy disc 22 . as is well known in the art , the floppy - disc handler 20 input / output interfaces comprise read / write devices for reading prerecorded information stored , deleting , adding or changing recorded information , on a machine - readable medium , i . e ., a floppy disc , and for providing signals which can be considered as data or operands to be manipulated in accordance with a software program loaded into the ram or rom memory ( not shown ) included in the computing module 12 . the equipment used in the exercise protocol can be a simple stair step of a known height . a cpx testing system 34 interfaces with the subject 30 during operation of the exercise test . the physiological variables may be selected from heart rate ( hr ), ventilation ( ve ), rate of oxygen uptake or consumption ( vo 2 ), carbon dioxide production ( vco 2 ), end tidal co 2 ( petco 2 ), respiratory exchange ratio ( rer ), or other variables derived from these basic measurements . physiological data collected is fed into the computing module 12 via a conductor 31 , or other communication device . the workload protocol is illustrated in fig2 and is organized in to a rest phase 50 , and exercise phase 52 , and a recovery phase 54 . although not required , the workload may also be quantified by requiring the patient to maintain a desired stepping cadence by the addition of an audible metronome that guides the frequency of the steps taken during the exercise phase . all data acquired by the cpx system is stored in a relational database as illustrated in fig3 . most importantly , data for each patient and each test is stored into separate subsets of data representing the rest phase 386 , the exercise phase 387 , and the recovery phase 388 for use by the feature extraction mechanism . step 1 — the equations for determining substrate utilization have been available for several years , originally by prof . j . b . de v . weir in 1948 in prof . victor and frank katch &# 39 ; s textbook ( w . mcardle , f . katch , and v . katch . exercise physiology : energy , nutrition and human performance ; chapter 8 ; measurement of human energy expenditure . 1991 ( third edition ). p 145 - 158 , a table is presented p 153 ) showing the value of % cho and % fat for each value of the rer within the range of human physiology . fig4 shows the data from this table plotted with rer on the x - axis and % cho and % fat on the y - axis . when a linear regression analysis is performed on each , the resulting equation is also displayed . step 2 — in fig5 , each of these equations are used to determine the % cho and % fat for the measured values of rer for the test . from this data , a line is drawn from the crossover point , where % cho =% fat , the line intersecting at the hr value at the same time in the test . this is the first heart rate zone delineation point , and is the easiest criteria for programmatic determination . step 3 — petco 2 plateau — in fig5 , the petco 2 values measured during the test are displayed . where this value reaches a peak and starts to decrease ( referred to in the scientific literature as the respiratory compensation point , rc ), another line is drawn , intersecting again at the hr value at the same time as the test . this is the second heart rate zone delineation point , which can be easily determined programmatically . step 4 — fat max — in fig5 , a line is drawn at the peak of the % fat plot , and this is again intersected with the heart rate value at the same time of the test . in some tests , the fat max point will be the same time in the test as the 50 / 50 time . in this case , there will be only 3 zones instead of the 4 zones identified in fig5 . in fig6 a and 6b , subject test data is used in a printed report . several options are available for recording heart rate and other data on wearable wireless devices . once recorded , the data can be uploaded to a website for display and additional analysis of the uploaded data . one such provider is a - life ( a - life . eu . com ). in fig7 , heart rate date collected during a 5 hour cycling session is plotted . in fig7 , the same data is overlaid with heart rate training zones as determined above . in fig8 , a bar chart format is illustrated in which the percentage of heart rate data points from one or more workouts are computed as the number of heart rate data points in each zone divided by the total number of heart rate data points for the workout time period ( single or multiple workouts ). the invention has been described in considerable detail in order to comply with the patent statutes and to provide those skilled in the art with the information needed to apply the novel principles and to construct and use such specialized components as are required . however , it is to be understood that the invention can be carried out by specifically different equipment and devices , and that various modifications , both as the equipment details and operating procedures can be accomplished without departing from the scope of the invention itself .
a method of pattern recognition for defining aerobic hr training zones measured during incremental cardiopulmonary exercise testing wherein such hr training zones are derived from distinct points in the incremental exercise response for % fat , % cho , and petco 2 . also disclosed is a method for providing feedback to the exercising subject indicating the % time the subject exercised in each of the zones using recorded hr data from each workout .