Planning Fitness Training Sessions Using the Bat Algorithm

Transcription

1 J. Yaghob (Ed.): ITAT 2015 pp Charles Unversty n Prague, Prague, 2015 Plannng Ftness Tranng Sessons Usng the Bat Algorthm Iztok Fster Jr. 1, Samo Rauter 2, Karn Ljubč Fster 3, Dušan Fster 1, and Iztok Fster 1 1 Unversty of Marbor, Faculty of Electrcal Engneerng and Computer Scence, Smetanova 17, 2000 Marbor, Slovena, ztok.fster1@um.s 2 Unversty of Ljubljana, Faculty of Sport Gortanova 22, 1000 Ljubljana 3 Unversty of Marbor, Faculty of Medcne, Taborska 8, 2000 Marbor, Slovena Abstract: Over farly recent years the concept of an artfcal sport traner has been proposed n lterature. Ths concept s based on computatonal ntellgence algorthms. In ths paper, we try to extend the artfcal sports traner by plannng ftness tranng sessons that are sutable for athletes, especally durng dle seasons when no competton takes place (e.g., wnter). The bat algorthm was used for plannng ftness tranng sessons and results showed promse for the proposed soluton. Future drectons for development are also outlned n the paper. 1 Introducton Sport becomes hghly addctve for many people n the world. A few decades ago, people around the world spend ther free tme dong dfferent actvtes, lke: short walks through the park, vstng the cnema or galleres, fshng, vstng a thermal spa and also meet frends. A lot of lesure studes also proved ths. However, over recent decades, lfestyles have been substantally changed especally because of globalzaton that has transformed the whole earth nto a global vllage. Due to a lack of tme as well as personal wllngness, people do not want to lve lke they used to. For ths reason, dfferent knd of new actvtes have emerged over recent past years. One of the bgger revtalzatons has been sport whch has became extremely n popular because of the emergence of dfferent mass sports events. For nstance, the man mass sport events are: Trathlon - Ths dscplne conssts of three sports: swmmng, cyclng and runnng. Addtonally, there are dfferent dstances whch vary from short va medum to long dstances. One of the more famous dstances s the Ironman trathlon [1, 2, 3], whch s also known as the hardest one day sport event. Ironman conssts of 3.8 km of swmmng, 180 km of cyclng and 42.2 km of runnng. Road marathons - A road marathon [4] conssts of 42.2 km pure runnng and s a challenge for myrad of people. Bg cty marathons especally are the most popular and attract large numbers of runners. Some marathons can accommodate more than 40,000 runners [5, 6]. Recreatonal cyclng marathons - Ths knd of mass [7, 8] sports events was very popular approxmately 10 years ago but stll represents a challenge for numerous partcpants. The current world economc problems ncreased the prces of cycles and consequently less partcpants could partcpate on cyclng marathons. Partcpants of the mentoned events partcpate mostly because of two goals. The frst goal s to enjoy (n other words: to have a nce tme) and the second s to fnsh the tral. Usually, fnshers are awarded wth medals whch are bg stmulants for partcpants. In ths case, every year many more partcpants have also began to take these compettons more serously,.e., sem professonal. In lne wth ths, they nvest much more tme n preparatons for compettons. Unfortunately, there s a long way for good preparatons for such knds of compettons. Ths good preparaton conssts of proper sports tranng, good eatng and also good restng. To mantan all these factors as hgh as possble s very hard for numerous athletes, snce they do not have enough experence. Newbe athletes especally suffer from the unwanted effects of rregular tranng called over-tranng syndrome [9, 10, 11] whch s reflected n reduced form. One of the possble solutons for avodng ths s to hre a personal traner or jon dverse tranng groups. However, these cost a lot of money and therefore many of them can not afford them. In order to break ths barrer, we began the development of an artfcal sport traner. An artfcal sports traner was presented recently n [12] and s based on computatonal ntellgence [13] algorthms that are able for plan the sports tranng over both short-term and long-term. Ths traner s also able to dscover the dfferent habts of athletes, avod over-tranng, etc. Data for the artfcal sports traner are obtaned from sports trackers [14] and sports watches lke Garmn. Ths paper, extends the artfcal sport traner wth plannng ftness tranng sessons. These knds of tranng sessons are very mportant for athletes especally durng dle seasons. In Europe, the dle season s usually durng wnter months when the athletes prepare ther form for the whole season. Plannng ftness sessons were performed usng a bat algorthm [15, 16] whch s a member of the

2 122 I. Fster Jr., S. Rauter, K. L. Fster, D. Fster, I. Fster computatonal ntellgence famly. The plannng of the ftness sessons was defned as a constrant satsfacton problem, where the bat algorthm searches for feasble solutons arsng when the number of constrant volatons acheved the value of zero. Organzaton of the remander of ths workshop paper s as follows: n secton 2 we dscuss about charactersts of ftness tranng, whle secton 3 presents swarm ntellgence algorthms and bat algorthm. Experments and results are presented n secton 4, whle secton 5 concludes the paper. 2 Fgure 2: Squats exercse Characterstcs of Ftness Tranng Ths study, focused on ftness tranng n regard to cyclng. Incorporaton of strength tranng n cyclsts preparatory perods has receved more attenton over the last two decades. Most of the serous and compettve cyclsts also nclude strength tranng n ther tranng programs. It s also evdent n some prevous research that addng strength tranng to an endurance tranng program can ncrease endurance performance [17, 18]. A combnaton of endurance and strength tranng (concurrent tranng) mght therefore be a potental tranng strategy for promotng muscle oxdatve capacty. It mght be related to an mproved cyclng economy, as observed after addng strength tranng to the ongong endurance tranng, namely because a stronger muscles at a certan ntensty operates longer wth a lower percentage of maxmum capacty. It s well-known that addng strength tranng to endurance tranng can ncrease the maxmal strengths and rate of force developments n cyclsts. In theory, ths may mprove pedalng characterstcs by ncreasng peak torque n the pedal stroke, reducng tme to peak torque and reducng the pedalng torque relatve to maxmal strength, whch n turn may allow for hgher power output and/or ncreased blood flow. The most mportant thng for developng a cyclng strength program s to know, whch muscle groups are the most actve durng the pedal stroke. Some prevous studes have detected a strong correlaton between cyclng performance and some strength exercses, lke leg presses, squats, and deadlfts [17, 18, 19, 20]. Some of the more useful exercses for ftness tranng are presented n Fgs. 1 to 3. Fgure 3: Lunge exercse For the smart plannng of sports tranng, quantfcatons, regulatng the ntensty of a workout s the key for success as ndcated as basc knowledge n sports tranng lterature. Ths fact also holds for ftness tranng. As an estmate of the ntensty of a ftness workout, two man measures are employed lke a: the number of repettons per set of exercses (NR), the maxmum amount of weght that can be generated n one maxmum contracton (1RM). The logc behnd the frst measure s as follows. The heaver the weght, the hgher the ntensty and the fewer repettons (also reps) an athlete wll be able to lft t for. On the other hand, the 1RM determnes the desred load for an exercse (typcally as a percentage of the 1RM). Let us notce that a coach determnes the measure of 1RM for a defnte athlete usng tests at the begnnng of the ftness tranng and then calculates the number of repeats (NR) n regard to ths characterstc value. 3 Fgure 1: Deadlft exercse Swarm Intellgence Based Algorthms Swarm ntellgence (SI) s a paradgm that belongs to computatonal ntellgence (CI). Accordng to the [21], SI concerns the collectve, emergng behavor of multple, nteractng agents that are capable of performng smple actons. Whle each agent may be consdered as unntellgent, the whole system of multple agents shows some

3 Plannng Ftness Tranng Sessons Usng the Bat Algorthm 123 self-organzatonal behavor and thus can behave lke some sort of collectve ntellgence. The basc pseudo-code of SI-based algorthms s presented n Algorthm 1. Nowadays, the bat algorthm s one of the promsng members of the SI famly. It s very easy to mplement and shows effcent results especally when solvng small dmensonal problems. Algorthm 1 Swarm Intellgence 1: ntalze_populaton_wth_random_canddate_partcles; 2: eval = evaluate_each_partcle; 3: whle termnaton_condton_not_meet do 4: move_partcles_towards_the_best_ndvdual; 5: eval += evaluate_each_partcle; 6: select_the_best_ndvduals_for_the_next_generaton; 7: end whle Next subsecton descrbes the mentoned algorthm n detal. 3.1 Bat Algorthm The bat algorthm was developed by Yang n The man purposes of ths algorthm were to be: smple, effcent and applcable to varos problem domans. The nspraton for the bat algorthm came from the phenomenon of the echolocaton characterstcs of some types of mcrobats. Developer used a three smplfed rules descrbng the bat behavor, as follows [22]: All bats use echolocaton to sense dstance to target objects. Bats fly randomly wth the velocty v at poston x, the frequency Q [Q mn,q max ] (also the wavelength λ ), the rate of pulse emsson r [0,1], and the loudness A [A 0,A mn ]. The frequency (and wavelength) can be adjusted dependng on the proxmtes of ther targets. The loudness vares from a large (postve) A 0 to a mnmum constant value A mn. The algorthm s pseudo-code s presented n Algorthm 2. The man bat algorthm components [23] are summarzed as follows: ntalzaton (lnes 1-3): ntalzng the algorthm parameters, generatng the ntal populaton, evaluatng ths, and fnally, determnng the best soluton x best n the populaton, generate_the_new_soluton (lne 6): movng the vrtual bats n the search space accordng to the physcal rules of bat echolocaton, local_search_step (lnes 7-9): mprovng the best soluton usng random walk drect explotaton (RWDE) heurstc, Algorthm 2 Bat algorthm Input: Bat populaton x = (x 1,...,x D ) T for = 1...N p, MAX_FE. Output: The best soluton x best and ts correspondng value f mn = mn( f (x)). 1: nt_bat(); 2: eval = evaluate_the_new_populaton; 3: f mn = fnd_the_best_soluton(x best ); {ntalzaton} 4: whle termnaton_condton_not_meet do 5: for = 1 to N p do 6: y = generate_new_soluton(x ); 7: f rand(0,1) > r then 8: y = mprove_the_best_soluton(x best ) 9: end f{ local search step } 10: f new = evaluate_the_new_soluton(y); 11: eval = eval + 1; 12: f f new f and N(0,1) < A then 13: x = y; f = f new ; 14: end f{ save the best soluton condtonally } 15: f mn =fnd_the_best_soluton(x best ); 16: end for 17: end whle evaluate_the_new_soluton (lne 10): evaluatng the new soluton, save_the_best_soluton_condtonaly (lnes 12-14): savng the new best soluton under some probablty A, fnd_the_best_soluton (lne 15): fndng the current best soluton. Generatng the new soluton s governed by the followng equaton: Q (t) v (t+1) x (t+1) = Q mn + (Q max Q mn )N(0,1), = v t + (x t best)q (t), = x (t) + v (t+1), where N(0,1) s a random number drawn from a Gaussan dstrbuton wth zero mean and a standard devaton of one. A RWDE heurstc mplemented n the functon mprove_the_best_soluton modfes the current best soluton accordng to the equaton: (1) x (t) = best + εa (t) N(0,1), (2) where N(0, 1) denotes the random number drawn from a Gaussan dstrbuton wth zero mean and a standard devaton of one, ε beng the scalng factor, and A (t) the loudness. Contemporary work on bat algorthms captures many varants and applcaton domans. Some recent works are presented n papers [24, 25, 26, 27, 28]

4 124 I. Fster Jr., S. Rauter, K. L. Fster, D. Fster, I. Fster 3.2 Bat Algorthm for Plannng Ftness Sessons Based on the orgnal bat algorthm, we have developed a modfed bat algorthm for plannng ftness sessons. Development of ths algorthm demanded the followng four steps: determnng the ftness exercses, defnng constrants, modfyng the orgnal bat algorthm, representng the results and ther vsualzatons. In the remander of ths paper, all these steps are descrbed n detal. Selectng Ftness Exercses. We need to determne specfc exercses for dfferent muscle groups before the ftness tranng can start. Although sports medcne recognzes more than 15 muscle groups that must be ncluded wthn ftness tranng, we focus on four groups (.e., legs, core, arms and back) n ths prelmnary study. Furthermore, some of these groups can be repeated durng the tranng. Each muscle group s assocated wth three prescrbed exercses as presented n Table 1. Muscle groups LEGS CORE ARMS LEGS BACK CORE LEGS ARMS Exercse LEG PRESS, SQUATS, LUNGE LEG SCISSORS, PLANK, LEG LIFTS PULLDOWN, PUSH UPS, UNDERARM ISOMETRIC EXERCISE LEG PRESS, SQUATS, LUNGE BACK EXTENSION, DEADLIFT, BAR ROWS LEG SCISSORS, PLANK, LEG LIFTS LEG PRESS, SQUATS, LUNGE PULLDOWN, PUSH UPS, UNDERARM ISOMETRIC EXERCISE Table 1: Muscle groups and assocated exercses On the other hand, the ntenstes of the exercses must be determned n a ftness tranng plan. Ths ntensty s assocated wth a measure 1RM measured for a specfc athlete. Here, three levels of ntensty are supported n our study, where each level s mapped accordng to the 1RM, as can be seen n Table 2. Intensty 1RM measure HIGH 1RM > 80% MEDIUM 60% < 1RM 80% LOW 1RM 60% Table 2: Intensty mappng Note that the data n Tables 1 and 2 were specfed accordng to the suggestons of ftness traners. Defnng constrants The purpose of the ftness tranng plan s to prescrbe suffcent numbers of exercses for each of the prescrbed muscle groups, ther number of repeats (NR) and the proper ntenstes (%1RM) such that an athlete smultaneously develops all the muscle groups needed for buldng the cyclst s basc form. Therefore, traners determne the proper amount of a specfc exercse n the plan n regardng to the others. In order to regulate the relatons between exercses n the ftness tranng plan, the followng constrants are defned: at least four exercses must have the number of repeats over 25 tmes (.e., NR>25), each tranng plan should have at least two exercses of hgh ntensty, each muscle group repeatng n Table 1 more than once does not have the same exercse, f the last exercse n the ftness tranng plan was of hgher ntensty, the next exercse should be of medum or hgh ntensty. In the remander of ths paper, these constrants were captured wthn the algorthmc structure of the orgnal bat algorthm for plannng the ftness tranng plan. Modfyng the orgnal bat algorthm Each soluton n the modfed bat (MBA) algorthm conssts of 24 floatngpont elements representng the ftness tranng plans for some athlete. The elements of the soluton are dvded nto three groups of elements. In other words, the soluton s expressed as x = (x 1,...,x 8,x 9,...,x 16,x 17,...,x 24 ) T, (3) where elements x 1,...,x 8 denote exercses from Table 1, x 9,...,x 16 are the number of repeats NR and x 17,...,x 24 the correspondng ntensty, respectvely. Ths means, each ftness tranng plan conssts of eght exercses wth an assgned number of repeats and correspondng ntenstes. Whle the number of repeats s selected from nterval NR [1, 40], parameters exercses and ntenstes are drawn from the nterval [0,1], and ther proper values are encoded as ndces nto a dscrete set of features accordng to the followng equatons ex(x, j ) = 3.0 x, j, for j = 1,...,8, (4) nt(x, j ) = 3.0 x, j, for j = 17,...,24, (5) where ex(x, j ) and nt(x, j ) determne the element n the feature sets as represented n Tables 1 and 2. For nstance, the functon ntensty can obtan the followng values from the feature set HIGH, f 0 x, j < 3 1, nt(x, j ) = MEDIUM, f 1 3 x, j < 2 3, LOW, f 2 3 x, j < 1,

5 Plannng Ftness Tranng Sessons Usng the Bat Algorthm 125 respectvely. The plannng of the ftness tranng sessons s defned as a constrant satsfacton problem that s formally defned as where and k<4 Mnmze f (x ) = χ k (x ), subject to 15 j=8 y j 4, 24 z j 2, j=16 k=1 x,1 x,4 x,7 x,2 x,6 x,3 x,8, nt(x, j ) HIGH nt(x, j+1 ) HIGH, { +1 f x, y j = j 25, +0 otherwse, { +1 f nt(x, z j = j ) HIGH, +0 otherwse, The proper soluton to the problem s found, when the f (x) = 0. Exercse Repeats Intensty SQUATS 34 LOW LEG SCISSORS 22 MEDIUM UNDERARM ISOMETRIC 40 HIGH LEG PRESS 15 LOW BAR ROWS 36 HIGH LEG LIFTS 27 LOW LUNGE 15 MEDIUM PULLDOWN 22 LOW Table 4: Second set of ftness workouts Exercse Repeats Intensty LEG PRESS 21 HIGH LEG LIFTS 29 MEDIUM UNDERARM ISOMETRIC 28 MEDIUM LUNGE 39 MEDIUM BACK EXTENSION 15 LOW PLANK 35 MEDIUM SQUATS 25 HIGH PUSH UPS 38 MEDIUM Table 5: Thrd set of ftness workouts Representaton of Results. Although the results could be vsualzed, the numercal results n the tables are presented only n ths prelmnary verson of the modfed bat algorthm. 4 Experments and Results The results of our experments are llustrated n Tables 3 to Table 5, where the tables represent the three sets of exercses. An athlete has some free tme for restng after fnshng each set. We run algorthm 25 tmes and after the run we selected three generated tranng sessons whch were successfully found by bat algorthm. Exercse Reps[NR] Intensty[1RM] LUNGE 26 HIGH LEG SCISSORS 23 LOW PULLDOWN 18 MEDIUM SQUATS 27 HIGH BAR ROWS 40 MEDIUM LEG LIFTS 22 HIGH LEG PRESS 35 MEDIUM PUSH UPS 39 LOW Table 3: Frst set of ftness workouts The obtaned results were evaluated by human traner who evaluated and approved t. The obtaned results confrm that the dea of automatc ftness tranng sessons was worth nvestgaton and the promsng results also show the potentals of the soluton when used n practce. On the other hand, we would also lke to present some problems and bottlenecks whch we encountered durng development. Frstly, t seems that t wll be good to test our dea wth evolutonary algorthms n the future. Experments showed that the success of the bat algorthm n satsfyng all constrants was about 25% of runs only. The problem s that the bat algorthm s hghly dependent on the best soluton. From ths reason, our algorthm went nto local optmum a lot of tmes. We beleve that advanced mechansms e.g. arthmetc crossover would behave much better. Moreover, usng adaptve and self-adaptve bat varants could also be suggested snce we spent a lot of tme tunng parameters. On the other hand, many more constrants should be defned n order to have very precse solutons whch should be very smlar to those solutons created by the human sport traner. 5 Conclusons In ths workshop paper, we presented a smple, yet effcent soluton for plannng ftness tranng sessons automatcally. The bat algorthm was employed n order to tackle ths problem. Ths algorthm successfully generated tranng sessons whch were evaluated and confrmed by a human traner who had more than 20 years of experence. In the future, there are many tasks to do n ths drecton lke for example testng wth other nature-nspred algorthms, employng arthmetc crossover and takng more constrants and exercces nto account.

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