Pub Date : 2023-11-14DOI: 10.1007/s12283-023-00439-z
Albert Smit, Stephan van der Zwaard, Ina Janssen, Thomas W. J. Janssen
Abstract Tandem cycling is a paralympic discipline, in which two cyclists ride on one tandem bicycle. Their performance can be improved by minimizing power losses. This study aimed to quantify power loss due to the chain drive of a tandem bicycle and influence of power input, location of power input, and rear chain wheel size. Power loss was determined by the difference between power input applied on the cranks (front or back rider) and power output measured at the rear wheel. Power output values were set from 100 to 400 W, with 50 W increments, and using two gear ratios, 53-11 and 53-13. Power input was generated at the back crank (with only the primary chain—solo bicycle—and with primary and secondary chain attached) and at the front crank (with primary and secondary chain attached). The power loss for the solo bicycle was 2.1% (± 1.5%). A significantly larger power loss was found for the tandem in the back (3.7 ± 2.4%, p < 0.001) and front positions (3.0 ± 1.8%, p < 0.001), with marginal differences between positions at higher power output. Power loss for the tandem was lower with gear 53-13 (2.9% ± 1.7%) compared to 53-11 (4.0 ± 2.8%, p < 0.001, effect size is medium). Therefore, findings suggest that back and front riders experience similar power losses due to the chain drive, but more than on a solo bicycle. Tandem cyclists can reduce their power loss in the chain drive by selecting larger gear ratios. Coaches may consider these findings for selecting and coaching their tandem cyclists.
{"title":"Power loss of the chain drive in a race tandem bicycle","authors":"Albert Smit, Stephan van der Zwaard, Ina Janssen, Thomas W. J. Janssen","doi":"10.1007/s12283-023-00439-z","DOIUrl":"https://doi.org/10.1007/s12283-023-00439-z","url":null,"abstract":"Abstract Tandem cycling is a paralympic discipline, in which two cyclists ride on one tandem bicycle. Their performance can be improved by minimizing power losses. This study aimed to quantify power loss due to the chain drive of a tandem bicycle and influence of power input, location of power input, and rear chain wheel size. Power loss was determined by the difference between power input applied on the cranks (front or back rider) and power output measured at the rear wheel. Power output values were set from 100 to 400 W, with 50 W increments, and using two gear ratios, 53-11 and 53-13. Power input was generated at the back crank (with only the primary chain—solo bicycle—and with primary and secondary chain attached) and at the front crank (with primary and secondary chain attached). The power loss for the solo bicycle was 2.1% (± 1.5%). A significantly larger power loss was found for the tandem in the back (3.7 ± 2.4%, p < 0.001) and front positions (3.0 ± 1.8%, p < 0.001), with marginal differences between positions at higher power output. Power loss for the tandem was lower with gear 53-13 (2.9% ± 1.7%) compared to 53-11 (4.0 ± 2.8%, p < 0.001, effect size is medium). Therefore, findings suggest that back and front riders experience similar power losses due to the chain drive, but more than on a solo bicycle. Tandem cyclists can reduce their power loss in the chain drive by selecting larger gear ratios. Coaches may consider these findings for selecting and coaching their tandem cyclists.","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-06DOI: 10.1007/s12283-023-00440-6
Andrew S. Perrotta, Brent D. Day, Ibrahim Wafai, Robert P. Oates, Maggie L. Peterson, Anika J. Scott, Rachel C. Barker, Athena B. Garedakis, Kayla A. Seaborn
{"title":"Concurrent validity and reliability of photoelectric and accelerometer technology for calculating vertical jump height in female athletes","authors":"Andrew S. Perrotta, Brent D. Day, Ibrahim Wafai, Robert P. Oates, Maggie L. Peterson, Anika J. Scott, Rachel C. Barker, Athena B. Garedakis, Kayla A. Seaborn","doi":"10.1007/s12283-023-00440-6","DOIUrl":"https://doi.org/10.1007/s12283-023-00440-6","url":null,"abstract":"","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1007/s12283-023-00441-5
S. M. Robbins, P. J. Renaud, N. MacInnis, D. J. Pearsall
{"title":"Differences in trunk–shoulder inter-joint coordination and sequencing between elite and recreational ice hockey players during slap shots","authors":"S. M. Robbins, P. J. Renaud, N. MacInnis, D. J. Pearsall","doi":"10.1007/s12283-023-00441-5","DOIUrl":"https://doi.org/10.1007/s12283-023-00441-5","url":null,"abstract":"","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1007/s12283-023-00438-0
Marc in het Panhuis, Luca Oggiano, David Shormann, Jimmy Freese
{"title":"Editor’s note: Topical Collection on surf engineering","authors":"Marc in het Panhuis, Luca Oggiano, David Shormann, Jimmy Freese","doi":"10.1007/s12283-023-00438-0","DOIUrl":"https://doi.org/10.1007/s12283-023-00438-0","url":null,"abstract":"","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1007/s12283-023-00437-1
Hideto Komori, Mariko Isogawa, Dan Mikami, Takasuke Nagai, Yoshimitsu Aoki
Abstract Vision-based human activity classification has remarkable potential for various applications in the sports context (e.g., motion analysis for performance enhancement, active sensing for athletes, etc.). Recently, learning-based human activity classifications have been widely researched. However, in sports scenes in which more detailed and player-specific classifications are required, this is a quite challenging task; in many cases, only a limited number of datasets are available, unlike daily movements such as walking or climbing stairs. Therefore, this paper proposes a time-weighted motion history image, an effective image sequence representation for learning-based human activity classification. Unlike conventional MHI based on the assumption that “the newer frame is more important,” our method generates importance-aware representation so that the predictor can “see” the frames that contribute to analyzing the specific human activity. Experimental results have shown the superiority of our method.
{"title":"Time-weighted motion history image for human activity classification in sports","authors":"Hideto Komori, Mariko Isogawa, Dan Mikami, Takasuke Nagai, Yoshimitsu Aoki","doi":"10.1007/s12283-023-00437-1","DOIUrl":"https://doi.org/10.1007/s12283-023-00437-1","url":null,"abstract":"Abstract Vision-based human activity classification has remarkable potential for various applications in the sports context (e.g., motion analysis for performance enhancement, active sensing for athletes, etc.). Recently, learning-based human activity classifications have been widely researched. However, in sports scenes in which more detailed and player-specific classifications are required, this is a quite challenging task; in many cases, only a limited number of datasets are available, unlike daily movements such as walking or climbing stairs. Therefore, this paper proposes a time-weighted motion history image, an effective image sequence representation for learning-based human activity classification. Unlike conventional MHI based on the assumption that “the newer frame is more important,” our method generates importance-aware representation so that the predictor can “see” the frames that contribute to analyzing the specific human activity. Experimental results have shown the superiority of our method.","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135268074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.1007/s12283-023-00435-3
Conlan M. Burbrink, Chase M. Straw, Weston F. Floyd, Athol Thomson, Steven E. Riechman
{"title":"Influence of force reduction within-field variability on peak tibial accelerations using wearable inertial measurement units","authors":"Conlan M. Burbrink, Chase M. Straw, Weston F. Floyd, Athol Thomson, Steven E. Riechman","doi":"10.1007/s12283-023-00435-3","DOIUrl":"https://doi.org/10.1007/s12283-023-00435-3","url":null,"abstract":"","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135616899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1007/s12283-023-00436-2
Bahador Keshvari, Long Lehoang, Veit Senner
Abstract Studded football boots and their interaction with the pitch surface play a major role in generating traction and on the risk of injuries and performance. The aim of this study was to establish a methodological framework to predict a safe zone of traction for different specific football movements in natural preloads. We measured peak pressure distribution among 17 male football players in four specific football movements (cutting 135°, sprinting, turning, and penalty kick) on artificial turf using a baseline football boot with an insole pressure sensor. A mechanical prosthetic foot was adjusted to replicate similar peak pressure distribution based on these four movements. Traction was measured under three preloads: 400, 600, and 800 N. They were lower than those measured with the players to avoid damage to the mechanical test device. This procedure was conducted for seven different outsole configurations. Rotational and translational traction was estimated for high preloads (above 2000 N) using an artificial neural network. Our findings show pressure distribution is an important bridge between subjective measurement (field tests) and objective measurement (laboratory tests) for accurate traction measurement. Artificial neural networks can aid in finding the upper and lower ranges of traction in the natural preloads. Such findings could help footwear developers, trainers, players, and governing institutions to choose an appropriate football boot outsole according to the safe zone of traction established in this study.
{"title":"Investigating the effect of outsole configurations on rotational and translational traction using a mechanical prosthetic foot","authors":"Bahador Keshvari, Long Lehoang, Veit Senner","doi":"10.1007/s12283-023-00436-2","DOIUrl":"https://doi.org/10.1007/s12283-023-00436-2","url":null,"abstract":"Abstract Studded football boots and their interaction with the pitch surface play a major role in generating traction and on the risk of injuries and performance. The aim of this study was to establish a methodological framework to predict a safe zone of traction for different specific football movements in natural preloads. We measured peak pressure distribution among 17 male football players in four specific football movements (cutting 135°, sprinting, turning, and penalty kick) on artificial turf using a baseline football boot with an insole pressure sensor. A mechanical prosthetic foot was adjusted to replicate similar peak pressure distribution based on these four movements. Traction was measured under three preloads: 400, 600, and 800 N. They were lower than those measured with the players to avoid damage to the mechanical test device. This procedure was conducted for seven different outsole configurations. Rotational and translational traction was estimated for high preloads (above 2000 N) using an artificial neural network. Our findings show pressure distribution is an important bridge between subjective measurement (field tests) and objective measurement (laboratory tests) for accurate traction measurement. Artificial neural networks can aid in finding the upper and lower ranges of traction in the natural preloads. Such findings could help footwear developers, trainers, players, and governing institutions to choose an appropriate football boot outsole according to the safe zone of traction established in this study.","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-05DOI: 10.1007/s12283-023-00431-7
Dominik Pandurevic, Alexander Sutor, Klaus Hochradel
Abstract Competitive sport climbing progressed massively within the last quarter century. Development of technology enabling qualitative and quantitative analysis is required to withstand the challenges for athletes and trainers. This paper deals with the statistical study of a data set generated by the application of several image processing algorithms and neural networks on competition recordings. Therefore, calculated parameters are combined with random variables for the implementation of a linear mixed effect model. The resulting model enables the prediction of the end time of different athletes and the determination of its correlation with the input variables. Furthermore, analysis of velocity and path of the centre of gravity in different wall sections is done for all available speed climbing athletes. The observed data set consists of 297 runs in total divided into two subsets of 202 observations of 47 male and 95 of 25 female athletes. Among others, the statistical model was used for the validation of the measured parameters and the review and impact of proven techniques like the Tomoa skip in the start section. Likewise interesting is the high influence of the parameters, measured especially in the middle section of the wall, on the end time.
{"title":"Towards statistical analysis of predictive parameters in competitive speed climbing","authors":"Dominik Pandurevic, Alexander Sutor, Klaus Hochradel","doi":"10.1007/s12283-023-00431-7","DOIUrl":"https://doi.org/10.1007/s12283-023-00431-7","url":null,"abstract":"Abstract Competitive sport climbing progressed massively within the last quarter century. Development of technology enabling qualitative and quantitative analysis is required to withstand the challenges for athletes and trainers. This paper deals with the statistical study of a data set generated by the application of several image processing algorithms and neural networks on competition recordings. Therefore, calculated parameters are combined with random variables for the implementation of a linear mixed effect model. The resulting model enables the prediction of the end time of different athletes and the determination of its correlation with the input variables. Furthermore, analysis of velocity and path of the centre of gravity in different wall sections is done for all available speed climbing athletes. The observed data set consists of 297 runs in total divided into two subsets of 202 observations of 47 male and 95 of 25 female athletes. Among others, the statistical model was used for the validation of the measured parameters and the review and impact of proven techniques like the Tomoa skip in the start section. Likewise interesting is the high influence of the parameters, measured especially in the middle section of the wall, on the end time.","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.1007/s12283-023-00434-4
K. Austin, Kieran Jai Nicholas, Chris M Jones, Mike Loosemore
{"title":"Criterion validity and reliability of an instrumented mouthguard under pendulum impactor conditions","authors":"K. Austin, Kieran Jai Nicholas, Chris M Jones, Mike Loosemore","doi":"10.1007/s12283-023-00434-4","DOIUrl":"https://doi.org/10.1007/s12283-023-00434-4","url":null,"abstract":"","PeriodicalId":46387,"journal":{"name":"Sports Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49040600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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