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A.J. Greidanus
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1
The Effect of Stroke Rate and Intensity on Ergometer Rowing Efficiency
Data Analysis and Modelling
The effect of stroke rate on metabolic efficiency in rowing is not fully understood. This thesis investigated if deviations from the preferred stroke rate (PSR) affect metabolic efficiency during submaximal ergometer rowing and if this effect depends on intensity. Additionally, a musculoskeletal modelling workflow was evaluated by comparing modelled and experimentally measured whole-body metabolic cost. Muscle-level metabolic contributions and activations were analysed to investigate the muscle group demands predicted by the model.
Pre-existing experimental and biomechanical data from twelve experienced male rowers were analysed. The rowers performed two different intensity conditions at 50% and 65% of their two kilometres maximum (2K max) time-trial power output. Among these fixed power outputs, the rowers used three different stroke rate conditions at PSR, PSR-15% and PSR+15%. The experimental metabolic cost was measured using a breathing gas analysis and was normalized by rowing ergometer power output. The modelled metabolic cost was estimated using a musculoskeletal modelling workflow and residual mechanical power was added separately to account for a part of the models’ limitations. A manually calibrated fibre type profile was used for the primary analysis and compared with a literature-based profile.
The experimental results show that ergometer output normalized metabolic cost was lower at 65% intensity compared to 50% intensity, indicating higher metabolic efficiency per Watt of ergometer output at the higher submaximal intensity. Across both intensities the results indicate that there is a shallow U-shaped curve relationship between stroke rate and the normalized metabolic cost. At the PSR, metabolic cost was lowest or close to lowest and metabolic cost increased when deviating from PSR. This effect was most apparent at the 65% intensity condition, where PSR+15% produced a statistically significant increase in normalized metabolic cost from PSR. At 50% intensity condition, deviations from PSR resulted in smaller and non-significant changes.
The residual-corrected musculoskeletal modelling workflow reproduced the intensity difference, but not the stroke rate dependent metabolic responses. The model predicted a larger decrease in normalized metabolic cost from 50% to 65% intensity than observed in experimental data. The model predicted a significant stroke rate effect consisting of a gradual increase in metabolic cost from PSR-15% to PSR+15% at both intensities. Therefore, the model did not reproduce the experimental U-shaped metabolic curve or the stronger PSR+15% metabolic penalty at 65% intensity. Residual mechanical power improved the absolute agreement but remained relatively constant across stroke rate conditions and did not explain the experimental response.
The vasti were the largest contributors to muscle metabolic power predicted by the model, and their contribution and mean activation increased with increasing stroke rate, while peak vasti activation decreased. However, neither vasti nor total summed activation squared showed a robust stroke rate effect. These patterns should only be interpreted as predictions of the model instead of actual physiological mechanisms because the model did not reproduce the complete experimental response. The literature-based fibre type profile produced considerably lower metabolic cost estimates than the calibrated profile and slightly changed the predicted stroke rate response.
Overall, the findings support metabolic efficiency as a factor contributing to preferred stroke rate selection, especially at higher intensity. However, the current modelling workflow was not yet able to reliably explain the muscle-level mechanisms underlying this preference. Participant personalized muscle properties, improved muscle recruitment estimation, and a more complete representation of trunk mechanics and whole-body physiological costs may be required.
...
Pre-existing experimental and biomechanical data from twelve experienced male rowers were analysed. The rowers performed two different intensity conditions at 50% and 65% of their two kilometres maximum (2K max) time-trial power output. Among these fixed power outputs, the rowers used three different stroke rate conditions at PSR, PSR-15% and PSR+15%. The experimental metabolic cost was measured using a breathing gas analysis and was normalized by rowing ergometer power output. The modelled metabolic cost was estimated using a musculoskeletal modelling workflow and residual mechanical power was added separately to account for a part of the models’ limitations. A manually calibrated fibre type profile was used for the primary analysis and compared with a literature-based profile.
The experimental results show that ergometer output normalized metabolic cost was lower at 65% intensity compared to 50% intensity, indicating higher metabolic efficiency per Watt of ergometer output at the higher submaximal intensity. Across both intensities the results indicate that there is a shallow U-shaped curve relationship between stroke rate and the normalized metabolic cost. At the PSR, metabolic cost was lowest or close to lowest and metabolic cost increased when deviating from PSR. This effect was most apparent at the 65% intensity condition, where PSR+15% produced a statistically significant increase in normalized metabolic cost from PSR. At 50% intensity condition, deviations from PSR resulted in smaller and non-significant changes.
The residual-corrected musculoskeletal modelling workflow reproduced the intensity difference, but not the stroke rate dependent metabolic responses. The model predicted a larger decrease in normalized metabolic cost from 50% to 65% intensity than observed in experimental data. The model predicted a significant stroke rate effect consisting of a gradual increase in metabolic cost from PSR-15% to PSR+15% at both intensities. Therefore, the model did not reproduce the experimental U-shaped metabolic curve or the stronger PSR+15% metabolic penalty at 65% intensity. Residual mechanical power improved the absolute agreement but remained relatively constant across stroke rate conditions and did not explain the experimental response.
The vasti were the largest contributors to muscle metabolic power predicted by the model, and their contribution and mean activation increased with increasing stroke rate, while peak vasti activation decreased. However, neither vasti nor total summed activation squared showed a robust stroke rate effect. These patterns should only be interpreted as predictions of the model instead of actual physiological mechanisms because the model did not reproduce the complete experimental response. The literature-based fibre type profile produced considerably lower metabolic cost estimates than the calibrated profile and slightly changed the predicted stroke rate response.
Overall, the findings support metabolic efficiency as a factor contributing to preferred stroke rate selection, especially at higher intensity. However, the current modelling workflow was not yet able to reliably explain the muscle-level mechanisms underlying this preference. Participant personalized muscle properties, improved muscle recruitment estimation, and a more complete representation of trunk mechanics and whole-body physiological costs may be required.
...
The effect of stroke rate on metabolic efficiency in rowing is not fully understood. This thesis investigated if deviations from the preferred stroke rate (PSR) affect metabolic efficiency during submaximal ergometer rowing and if this effect depends on intensity. Additionally, a musculoskeletal modelling workflow was evaluated by comparing modelled and experimentally measured whole-body metabolic cost. Muscle-level metabolic contributions and activations were analysed to investigate the muscle group demands predicted by the model.
Pre-existing experimental and biomechanical data from twelve experienced male rowers were analysed. The rowers performed two different intensity conditions at 50% and 65% of their two kilometres maximum (2K max) time-trial power output. Among these fixed power outputs, the rowers used three different stroke rate conditions at PSR, PSR-15% and PSR+15%. The experimental metabolic cost was measured using a breathing gas analysis and was normalized by rowing ergometer power output. The modelled metabolic cost was estimated using a musculoskeletal modelling workflow and residual mechanical power was added separately to account for a part of the models’ limitations. A manually calibrated fibre type profile was used for the primary analysis and compared with a literature-based profile.
The experimental results show that ergometer output normalized metabolic cost was lower at 65% intensity compared to 50% intensity, indicating higher metabolic efficiency per Watt of ergometer output at the higher submaximal intensity. Across both intensities the results indicate that there is a shallow U-shaped curve relationship between stroke rate and the normalized metabolic cost. At the PSR, metabolic cost was lowest or close to lowest and metabolic cost increased when deviating from PSR. This effect was most apparent at the 65% intensity condition, where PSR+15% produced a statistically significant increase in normalized metabolic cost from PSR. At 50% intensity condition, deviations from PSR resulted in smaller and non-significant changes.
The residual-corrected musculoskeletal modelling workflow reproduced the intensity difference, but not the stroke rate dependent metabolic responses. The model predicted a larger decrease in normalized metabolic cost from 50% to 65% intensity than observed in experimental data. The model predicted a significant stroke rate effect consisting of a gradual increase in metabolic cost from PSR-15% to PSR+15% at both intensities. Therefore, the model did not reproduce the experimental U-shaped metabolic curve or the stronger PSR+15% metabolic penalty at 65% intensity. Residual mechanical power improved the absolute agreement but remained relatively constant across stroke rate conditions and did not explain the experimental response.
The vasti were the largest contributors to muscle metabolic power predicted by the model, and their contribution and mean activation increased with increasing stroke rate, while peak vasti activation decreased. However, neither vasti nor total summed activation squared showed a robust stroke rate effect. These patterns should only be interpreted as predictions of the model instead of actual physiological mechanisms because the model did not reproduce the complete experimental response. The literature-based fibre type profile produced considerably lower metabolic cost estimates than the calibrated profile and slightly changed the predicted stroke rate response.
Overall, the findings support metabolic efficiency as a factor contributing to preferred stroke rate selection, especially at higher intensity. However, the current modelling workflow was not yet able to reliably explain the muscle-level mechanisms underlying this preference. Participant personalized muscle properties, improved muscle recruitment estimation, and a more complete representation of trunk mechanics and whole-body physiological costs may be required.
Pre-existing experimental and biomechanical data from twelve experienced male rowers were analysed. The rowers performed two different intensity conditions at 50% and 65% of their two kilometres maximum (2K max) time-trial power output. Among these fixed power outputs, the rowers used three different stroke rate conditions at PSR, PSR-15% and PSR+15%. The experimental metabolic cost was measured using a breathing gas analysis and was normalized by rowing ergometer power output. The modelled metabolic cost was estimated using a musculoskeletal modelling workflow and residual mechanical power was added separately to account for a part of the models’ limitations. A manually calibrated fibre type profile was used for the primary analysis and compared with a literature-based profile.
The experimental results show that ergometer output normalized metabolic cost was lower at 65% intensity compared to 50% intensity, indicating higher metabolic efficiency per Watt of ergometer output at the higher submaximal intensity. Across both intensities the results indicate that there is a shallow U-shaped curve relationship between stroke rate and the normalized metabolic cost. At the PSR, metabolic cost was lowest or close to lowest and metabolic cost increased when deviating from PSR. This effect was most apparent at the 65% intensity condition, where PSR+15% produced a statistically significant increase in normalized metabolic cost from PSR. At 50% intensity condition, deviations from PSR resulted in smaller and non-significant changes.
The residual-corrected musculoskeletal modelling workflow reproduced the intensity difference, but not the stroke rate dependent metabolic responses. The model predicted a larger decrease in normalized metabolic cost from 50% to 65% intensity than observed in experimental data. The model predicted a significant stroke rate effect consisting of a gradual increase in metabolic cost from PSR-15% to PSR+15% at both intensities. Therefore, the model did not reproduce the experimental U-shaped metabolic curve or the stronger PSR+15% metabolic penalty at 65% intensity. Residual mechanical power improved the absolute agreement but remained relatively constant across stroke rate conditions and did not explain the experimental response.
The vasti were the largest contributors to muscle metabolic power predicted by the model, and their contribution and mean activation increased with increasing stroke rate, while peak vasti activation decreased. However, neither vasti nor total summed activation squared showed a robust stroke rate effect. These patterns should only be interpreted as predictions of the model instead of actual physiological mechanisms because the model did not reproduce the complete experimental response. The literature-based fibre type profile produced considerably lower metabolic cost estimates than the calibrated profile and slightly changed the predicted stroke rate response.
Overall, the findings support metabolic efficiency as a factor contributing to preferred stroke rate selection, especially at higher intensity. However, the current modelling workflow was not yet able to reliably explain the muscle-level mechanisms underlying this preference. Participant personalized muscle properties, improved muscle recruitment estimation, and a more complete representation of trunk mechanics and whole-body physiological costs may be required.
Relative Muscle Contributions to Mechanical Work in Ergometer Rowing
Experiment, Modeling, and Analysis
In running and cycling, efficiency and its dependency upon frequency have been investigated extensively. In rowing however, there is still much to be uncovered. In literature, metabolic efficiency seems to be unaffected by stroke rate, despite theory suggesting the existence of some optimal frequency. In order to better understand the mechanisms behind rowing technique, more research is needed.
Until recently, rowing research was largely conducted through experiments. However, muscle analysis from experimental data alone is limited to EMG data collected during the experiment. With the rise of musculoskeletal models, not only muscle activations, but also other muscle variables can be simulated from kinematics and external forces, enabling muscle analysis in more detail.
This report describes the complete process of gathering comprehensive data in an experiment, processing and preparing this data for use in musculoskeletal simulation, and then using this data to validate the model and generate results. Forces, motion, EMG and breathing gas data are collected with three main goals in mind; to extensively validate the musculoskeletal model, to evaluate muscle contributions to total work across different stroke rates and power outputs, and to assess the effects of stroke rate and power output on muscle contributions and metabolic efficiency.
The experimental results, as well as modeling and simulation outputs, in this report, are in accordance with values reported in literature. Additionally, errors in marker tracking and residual and reserve forces and torques have been found to be within acceptable limits, though it should be noted that errors in the upper body are higher than in the legs. Nevertheless, the model is deemed to be valid for the application of ergometer rowing.
Throughout stroke rates and power outputs, no statistically significant effect of stroke rate or power output on muscle contributions have been found. Additionally, the effects of stroke rate and power output on metabolic efficiency are deemed insignificant. Across all stroke rates and outputs, the quadriceps, and more specifically the Vastus Medialis, have been identified as the largest contributors to total work at the muscle level. At the joint level however, the hips are the main contributors, despite the Vasti only acting at the knee. This illustrates that work is exchanged between joints by tendinous action from biarticular muscles such as the hamstrings, an effect also known as Lombard's paradox.
...
Until recently, rowing research was largely conducted through experiments. However, muscle analysis from experimental data alone is limited to EMG data collected during the experiment. With the rise of musculoskeletal models, not only muscle activations, but also other muscle variables can be simulated from kinematics and external forces, enabling muscle analysis in more detail.
This report describes the complete process of gathering comprehensive data in an experiment, processing and preparing this data for use in musculoskeletal simulation, and then using this data to validate the model and generate results. Forces, motion, EMG and breathing gas data are collected with three main goals in mind; to extensively validate the musculoskeletal model, to evaluate muscle contributions to total work across different stroke rates and power outputs, and to assess the effects of stroke rate and power output on muscle contributions and metabolic efficiency.
The experimental results, as well as modeling and simulation outputs, in this report, are in accordance with values reported in literature. Additionally, errors in marker tracking and residual and reserve forces and torques have been found to be within acceptable limits, though it should be noted that errors in the upper body are higher than in the legs. Nevertheless, the model is deemed to be valid for the application of ergometer rowing.
Throughout stroke rates and power outputs, no statistically significant effect of stroke rate or power output on muscle contributions have been found. Additionally, the effects of stroke rate and power output on metabolic efficiency are deemed insignificant. Across all stroke rates and outputs, the quadriceps, and more specifically the Vastus Medialis, have been identified as the largest contributors to total work at the muscle level. At the joint level however, the hips are the main contributors, despite the Vasti only acting at the knee. This illustrates that work is exchanged between joints by tendinous action from biarticular muscles such as the hamstrings, an effect also known as Lombard's paradox.
...
In running and cycling, efficiency and its dependency upon frequency have been investigated extensively. In rowing however, there is still much to be uncovered. In literature, metabolic efficiency seems to be unaffected by stroke rate, despite theory suggesting the existence of some optimal frequency. In order to better understand the mechanisms behind rowing technique, more research is needed.
Until recently, rowing research was largely conducted through experiments. However, muscle analysis from experimental data alone is limited to EMG data collected during the experiment. With the rise of musculoskeletal models, not only muscle activations, but also other muscle variables can be simulated from kinematics and external forces, enabling muscle analysis in more detail.
This report describes the complete process of gathering comprehensive data in an experiment, processing and preparing this data for use in musculoskeletal simulation, and then using this data to validate the model and generate results. Forces, motion, EMG and breathing gas data are collected with three main goals in mind; to extensively validate the musculoskeletal model, to evaluate muscle contributions to total work across different stroke rates and power outputs, and to assess the effects of stroke rate and power output on muscle contributions and metabolic efficiency.
The experimental results, as well as modeling and simulation outputs, in this report, are in accordance with values reported in literature. Additionally, errors in marker tracking and residual and reserve forces and torques have been found to be within acceptable limits, though it should be noted that errors in the upper body are higher than in the legs. Nevertheless, the model is deemed to be valid for the application of ergometer rowing.
Throughout stroke rates and power outputs, no statistically significant effect of stroke rate or power output on muscle contributions have been found. Additionally, the effects of stroke rate and power output on metabolic efficiency are deemed insignificant. Across all stroke rates and outputs, the quadriceps, and more specifically the Vastus Medialis, have been identified as the largest contributors to total work at the muscle level. At the joint level however, the hips are the main contributors, despite the Vasti only acting at the knee. This illustrates that work is exchanged between joints by tendinous action from biarticular muscles such as the hamstrings, an effect also known as Lombard's paradox.
Until recently, rowing research was largely conducted through experiments. However, muscle analysis from experimental data alone is limited to EMG data collected during the experiment. With the rise of musculoskeletal models, not only muscle activations, but also other muscle variables can be simulated from kinematics and external forces, enabling muscle analysis in more detail.
This report describes the complete process of gathering comprehensive data in an experiment, processing and preparing this data for use in musculoskeletal simulation, and then using this data to validate the model and generate results. Forces, motion, EMG and breathing gas data are collected with three main goals in mind; to extensively validate the musculoskeletal model, to evaluate muscle contributions to total work across different stroke rates and power outputs, and to assess the effects of stroke rate and power output on muscle contributions and metabolic efficiency.
The experimental results, as well as modeling and simulation outputs, in this report, are in accordance with values reported in literature. Additionally, errors in marker tracking and residual and reserve forces and torques have been found to be within acceptable limits, though it should be noted that errors in the upper body are higher than in the legs. Nevertheless, the model is deemed to be valid for the application of ergometer rowing.
Throughout stroke rates and power outputs, no statistically significant effect of stroke rate or power output on muscle contributions have been found. Additionally, the effects of stroke rate and power output on metabolic efficiency are deemed insignificant. Across all stroke rates and outputs, the quadriceps, and more specifically the Vastus Medialis, have been identified as the largest contributors to total work at the muscle level. At the joint level however, the hips are the main contributors, despite the Vasti only acting at the knee. This illustrates that work is exchanged between joints by tendinous action from biarticular muscles such as the hamstrings, an effect also known as Lombard's paradox.
Master thesis
(2023)
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W.H.J. Houtman, A.J. Greidanus, J. Westerweel, W. van de Water, G. Mulder, P. Simões Costa
Swimming is a sport where an incremental gain in performance can lead to either a medal or leaving the swimmer empty-handed. In the front crawl, water is driven backwards by the limbs where most propulsive forces come from the hands. Two main stroke patterns can be observed, the ”straight I pull” is commonly taught to generate forces with drag, and the ”curved S-pull” applies lift as well. This investigation uses a new industrial 6-DOF robot arm to study angle-dependent forces, stroke patterns and hand configuration variables. The forearm and hand are analysed using force measurements and PIV to obtain quantitative information, while a parameterisation of the forearm and hand is used to obtain qualitative analysis. The flow is analysed qualitatively using two analytical methods, but it is discovered that these methods can only be applied to a limited range of cases to predict the angle-dependent lift force. Numerical investigations into the parameterised arm model reveal that forces generated were similar in magnitude to those reported in the literature for a pseudo-transient simulation. Quantitative research shows that lift only surpasses drag at extreme angles of attack immediately after a rapid start. This provides clear evidence that drag-based propulsion is preferred for front-crawl swimming. We utilize PIV technology to visualise the flow field around two cases where the lift was highly prevalent. The results reveal various aspects, including the nature of the observed lift peak. Finally, the study compares three distinct strokes: one utilizing a straight drag-based approach and two utilizing a sinusoidal path, resembling a curved pull. The findings indicate that the sinusoidal path’s lift-to-drag ratio remains relatively consistent compared to steady-state, constant-angle experiments. Additionally, the results suggest that the straight stroke is optimal for propulsion, while one of the sinusoidal strokes might be more energy-efficient. An additional aim of this research is to determine the suitability of an industrial 6-DOF robot arm for stroke experiments. It has been discovered that some experiments need a longer path and should preferably be conducted in a wind or water tunnel.
...
Swimming is a sport where an incremental gain in performance can lead to either a medal or leaving the swimmer empty-handed. In the front crawl, water is driven backwards by the limbs where most propulsive forces come from the hands. Two main stroke patterns can be observed, the ”straight I pull” is commonly taught to generate forces with drag, and the ”curved S-pull” applies lift as well. This investigation uses a new industrial 6-DOF robot arm to study angle-dependent forces, stroke patterns and hand configuration variables. The forearm and hand are analysed using force measurements and PIV to obtain quantitative information, while a parameterisation of the forearm and hand is used to obtain qualitative analysis. The flow is analysed qualitatively using two analytical methods, but it is discovered that these methods can only be applied to a limited range of cases to predict the angle-dependent lift force. Numerical investigations into the parameterised arm model reveal that forces generated were similar in magnitude to those reported in the literature for a pseudo-transient simulation. Quantitative research shows that lift only surpasses drag at extreme angles of attack immediately after a rapid start. This provides clear evidence that drag-based propulsion is preferred for front-crawl swimming. We utilize PIV technology to visualise the flow field around two cases where the lift was highly prevalent. The results reveal various aspects, including the nature of the observed lift peak. Finally, the study compares three distinct strokes: one utilizing a straight drag-based approach and two utilizing a sinusoidal path, resembling a curved pull. The findings indicate that the sinusoidal path’s lift-to-drag ratio remains relatively consistent compared to steady-state, constant-angle experiments. Additionally, the results suggest that the straight stroke is optimal for propulsion, while one of the sinusoidal strokes might be more energy-efficient. An additional aim of this research is to determine the suitability of an industrial 6-DOF robot arm for stroke experiments. It has been discovered that some experiments need a longer path and should preferably be conducted in a wind or water tunnel.
Surface pressure measurements are a crucial part of aerodynamic/hydrodynamic analysis and are essential when studying complex flow phenomena. Although pressure-sensitive paint (PSP) has become established as a non-intrusive field measurement technique for high-speed applications in air, a comparable method does not yet exist for usage underwater....
...
Surface pressure measurements are a crucial part of aerodynamic/hydrodynamic analysis and are essential when studying complex flow phenomena. Although pressure-sensitive paint (PSP) has become established as a non-intrusive field measurement technique for high-speed applications in air, a comparable method does not yet exist for usage underwater....
Play your run!
A superhuman sports running game in mixed reality
This thesis describes the process of designing a running game in mixed reality, inspired by superhuman sports.
The process started with analysing the possible design directions in the development of (superhuman) sports and corresponding emerging technologies. For this purpose, the concept of superhuman sports is considered. Superhuman sports are activities that rely on technology for human augmentation, involve physical fitness and skills, which are played for fun, competition or health reasons.
The next step was the decision for a design direction. This decision was made to design a new superhuman sport, in the form of a running game in mixed reality, to make the running experience itself more fun and motivating and distract runners from the exertion activity. An ideation phase followed in which different ideas were considered and eventually a combination for a final game design was chosen.
The final game design Play your run! is a running game that can be played location independent and provides a virtual replication of the opponent by wearing augmented reality glasses. Thus, the running game augments the real environment of the runner. In the game, runners compete with each other in a virtual time measured race on a pre-set real distance. They can harass each other by obtaining power-up items by running into virtual boxes placed on the course in the real world. The power-ups are placed in relation to geolocation and meshed surfaces. These items, e.g., add time to the virtual time of the opponent, require the opponent to freeze, or offer protection. The type of item that a player receives from the box is influenced by the runners’ speed, heart rate and current position in the virtual race. Such dynamic game balancing makes it possible for runners of all different levels to play the game.
A tournament with six runners was organized in order to investigated whether this running game let runners experience more fun, motivation and engagement. During this tournament, the runners played the running game in which the way they perceived and obtained virtual power-ups was controlled by the researcher. In order to keep the burden of extra technology for the runners low and focus on evaluating the game experience, stationary objects and projection were used to visualize the power-ups. Based on the experience of the runners, the game rules were proved to improve performance, distract the runner from discomfort and involve the audience.
A presentation movie of a possible game scenario was create to explain and present the game play. This report provides a description of the scenario and visuals to support and explain the intended gameplay. At some points the technology is not yet developed enough to play the game the initial way. Therefore a future implementation plan and possible future suggestions are given. This report describes the process of a new superhuman sport, a running game in mixed reality. The decision for other emerging technologies or sports can result in more new (superhuman) sports, that can performed during the International Superhuman Sports Championship in 2020. ...
The process started with analysing the possible design directions in the development of (superhuman) sports and corresponding emerging technologies. For this purpose, the concept of superhuman sports is considered. Superhuman sports are activities that rely on technology for human augmentation, involve physical fitness and skills, which are played for fun, competition or health reasons.
The next step was the decision for a design direction. This decision was made to design a new superhuman sport, in the form of a running game in mixed reality, to make the running experience itself more fun and motivating and distract runners from the exertion activity. An ideation phase followed in which different ideas were considered and eventually a combination for a final game design was chosen.
The final game design Play your run! is a running game that can be played location independent and provides a virtual replication of the opponent by wearing augmented reality glasses. Thus, the running game augments the real environment of the runner. In the game, runners compete with each other in a virtual time measured race on a pre-set real distance. They can harass each other by obtaining power-up items by running into virtual boxes placed on the course in the real world. The power-ups are placed in relation to geolocation and meshed surfaces. These items, e.g., add time to the virtual time of the opponent, require the opponent to freeze, or offer protection. The type of item that a player receives from the box is influenced by the runners’ speed, heart rate and current position in the virtual race. Such dynamic game balancing makes it possible for runners of all different levels to play the game.
A tournament with six runners was organized in order to investigated whether this running game let runners experience more fun, motivation and engagement. During this tournament, the runners played the running game in which the way they perceived and obtained virtual power-ups was controlled by the researcher. In order to keep the burden of extra technology for the runners low and focus on evaluating the game experience, stationary objects and projection were used to visualize the power-ups. Based on the experience of the runners, the game rules were proved to improve performance, distract the runner from discomfort and involve the audience.
A presentation movie of a possible game scenario was create to explain and present the game play. This report provides a description of the scenario and visuals to support and explain the intended gameplay. At some points the technology is not yet developed enough to play the game the initial way. Therefore a future implementation plan and possible future suggestions are given. This report describes the process of a new superhuman sport, a running game in mixed reality. The decision for other emerging technologies or sports can result in more new (superhuman) sports, that can performed during the International Superhuman Sports Championship in 2020. ...
This thesis describes the process of designing a running game in mixed reality, inspired by superhuman sports.
The process started with analysing the possible design directions in the development of (superhuman) sports and corresponding emerging technologies. For this purpose, the concept of superhuman sports is considered. Superhuman sports are activities that rely on technology for human augmentation, involve physical fitness and skills, which are played for fun, competition or health reasons.
The next step was the decision for a design direction. This decision was made to design a new superhuman sport, in the form of a running game in mixed reality, to make the running experience itself more fun and motivating and distract runners from the exertion activity. An ideation phase followed in which different ideas were considered and eventually a combination for a final game design was chosen.
The final game design Play your run! is a running game that can be played location independent and provides a virtual replication of the opponent by wearing augmented reality glasses. Thus, the running game augments the real environment of the runner. In the game, runners compete with each other in a virtual time measured race on a pre-set real distance. They can harass each other by obtaining power-up items by running into virtual boxes placed on the course in the real world. The power-ups are placed in relation to geolocation and meshed surfaces. These items, e.g., add time to the virtual time of the opponent, require the opponent to freeze, or offer protection. The type of item that a player receives from the box is influenced by the runners’ speed, heart rate and current position in the virtual race. Such dynamic game balancing makes it possible for runners of all different levels to play the game.
A tournament with six runners was organized in order to investigated whether this running game let runners experience more fun, motivation and engagement. During this tournament, the runners played the running game in which the way they perceived and obtained virtual power-ups was controlled by the researcher. In order to keep the burden of extra technology for the runners low and focus on evaluating the game experience, stationary objects and projection were used to visualize the power-ups. Based on the experience of the runners, the game rules were proved to improve performance, distract the runner from discomfort and involve the audience.
A presentation movie of a possible game scenario was create to explain and present the game play. This report provides a description of the scenario and visuals to support and explain the intended gameplay. At some points the technology is not yet developed enough to play the game the initial way. Therefore a future implementation plan and possible future suggestions are given. This report describes the process of a new superhuman sport, a running game in mixed reality. The decision for other emerging technologies or sports can result in more new (superhuman) sports, that can performed during the International Superhuman Sports Championship in 2020.
The process started with analysing the possible design directions in the development of (superhuman) sports and corresponding emerging technologies. For this purpose, the concept of superhuman sports is considered. Superhuman sports are activities that rely on technology for human augmentation, involve physical fitness and skills, which are played for fun, competition or health reasons.
The next step was the decision for a design direction. This decision was made to design a new superhuman sport, in the form of a running game in mixed reality, to make the running experience itself more fun and motivating and distract runners from the exertion activity. An ideation phase followed in which different ideas were considered and eventually a combination for a final game design was chosen.
The final game design Play your run! is a running game that can be played location independent and provides a virtual replication of the opponent by wearing augmented reality glasses. Thus, the running game augments the real environment of the runner. In the game, runners compete with each other in a virtual time measured race on a pre-set real distance. They can harass each other by obtaining power-up items by running into virtual boxes placed on the course in the real world. The power-ups are placed in relation to geolocation and meshed surfaces. These items, e.g., add time to the virtual time of the opponent, require the opponent to freeze, or offer protection. The type of item that a player receives from the box is influenced by the runners’ speed, heart rate and current position in the virtual race. Such dynamic game balancing makes it possible for runners of all different levels to play the game.
A tournament with six runners was organized in order to investigated whether this running game let runners experience more fun, motivation and engagement. During this tournament, the runners played the running game in which the way they perceived and obtained virtual power-ups was controlled by the researcher. In order to keep the burden of extra technology for the runners low and focus on evaluating the game experience, stationary objects and projection were used to visualize the power-ups. Based on the experience of the runners, the game rules were proved to improve performance, distract the runner from discomfort and involve the audience.
A presentation movie of a possible game scenario was create to explain and present the game play. This report provides a description of the scenario and visuals to support and explain the intended gameplay. At some points the technology is not yet developed enough to play the game the initial way. Therefore a future implementation plan and possible future suggestions are given. This report describes the process of a new superhuman sport, a running game in mixed reality. The decision for other emerging technologies or sports can result in more new (superhuman) sports, that can performed during the International Superhuman Sports Championship in 2020.