Differences in swimming stroke mechanics and kinematics derived from tri-axial accelerometers during a 200-IM event in South African national swimmers
- Authors: Musson, Courtney Ruth
- Date: 2020
- Subjects: Swimming
- Language: English
- Type: Thesis , Masters , MA
- Identifier: http://hdl.handle.net/10948/46337 , vital:39588
- Description: Context: Swimming is a highly competitive sport, with elite swimmers and coaches constantly looking for ways to improve and challenge themselves to meet new performance goals. The implementation of technology in swimming has proven to be a vital tool in athlete monitoring and in providing coaches with additional information on the swimmer’s performance. Example of this technology is the use of inertial sensory devices such as tri-axial accelerometers. The accelerometers can be used to provide kinematic information with regards to the swimmer’s stroke rate, stroke length and stroke mechanics. In a typical training session, coaches would have to manually time and count their swimmer’s strokes to be able to gain the kinematic information they require. Hence, the use ofinertial sensory technology, such as accelerometers, would provide the necessary information coaches require, allowing them to concentrate on other performance aspects such as theirswimmer’s technique.Aim and objectives: The aim of this study was to determine the kinematic parameters and swimming stroke mechanics that could be derived from tri-axial accelerometers, during a 200-m individual medley (IM) event in South African national level swimmers. Three objectives were set to meet the aim of the study. The first was to identify and differentiate each of the stroking styles using tri-axial accelerometers. The second was to identify and differentiate the kinematic parametersand stroke mechanicsfor all four strokes using tri-axial accelerometers. The third objective was to implement machine learning to automate the identification and interpretation of the accelerometer data. Method:A quantitative, non-experimental descriptive one group post-test only design was used, in which 15 national level swimmers, of which seven male and eight female (mean ±SD: age: 20.9 ± 2.90 years; height: 173.28 ± 10.61 cm; weight: 67.81 ± 8.09 kg; arm span: 178.21 ± 12.15 cm) were tested. Three anthropometric measures were taken (height, weight and arm span) prior to testing, with two tri-axial accelerometers and Polar V800watch and heart rate belt attached to the swimmers left wrist, upper-back and chest, respectively. All swimmerswere required to perform three main swimming sets: 50-m IM, 100-m variation and 200-mIM. Variousdescriptivestatisticsincluding mean, standard deviation and confidence intervals (95%)were used to describe the data. with further inferential statistics including paired t-test, intra-class correlation and Bland Altman analysis wereused to describe the relationship ivbetween the accelerometer and the manually estimated parameters. Additionally, arepeated measures one-way ANOVA (with post-hoc Tukey HSD test) werealso used in an inter-comparison of the stroke parameters between each of the stroking styles. A confusion matrix wasused to measure the classification accuracy of the machine learning model implemented on the accelerometer data.Results:The accelerometers proved successful in identifyingand discerningthe stroke mechanics for each of the four stroking styles, with the use of video footage to validatethe findings. In the stroke kinematic differentiation, theBland Altman analysisresultsshowed an agreement between themanual method and accelerometer-derived estimates, although a discrepancy was evident for several of the kinematic parameters, with a significant difference found with the estimated lap time, average swimming velocity and stroke rate (paired t-test: p <0.001 for all swim sets). The inter-comparison between the stroke parameters per stroking style showed a significant difference with average swimming velocity (repeated one-way ANOVA: F = 1789.37, p <0.001), averages stroke rate (repeated one-way ANOVA: F = 671.70, p <0.001) and average stroke length (repeated one-way ANOVA: F = 346.46, p<0.001) for the population group tested. Furtheranalysis with post-hoc Tukey HSD test showed no significant difference wereevident for the average swimming velocity(Tukey: p > 0.05for all strokes)andbetween freestyle and backstroke for the average stroke rate and stroke length (Tukey:p = 0.0968 andp = 0.997, respectively).Lastly, the machine learning model found a classification accuracy of 96.6% in identifyingand labelling the stroking styles fromthe accelerometer data.Conclusion: It was shown that the tri-axial accelerometers were successful in the identification and differentiation of all the stroking styles, stroke mechanics and kinematics, although a discrepancy was found with the average swimming velocity, stroke rate and lap time estimations. The machine learning model implemented proved the benefits of using artificial intelligence to ease the data process and interpretation by automatically labelling the accelerometer data. Therefore, the use of tri-axial accelerometers as a coaching aid has major potential in the swimming community. However, further research is required to eliminate the time-consuming data processingand to increasetheaccuracy of the accelerometer in the measurement of all the stroke kinematics.
- Full Text:
- Date Issued: 2020
- Authors: Musson, Courtney Ruth
- Date: 2020
- Subjects: Swimming
- Language: English
- Type: Thesis , Masters , MA
- Identifier: http://hdl.handle.net/10948/46337 , vital:39588
- Description: Context: Swimming is a highly competitive sport, with elite swimmers and coaches constantly looking for ways to improve and challenge themselves to meet new performance goals. The implementation of technology in swimming has proven to be a vital tool in athlete monitoring and in providing coaches with additional information on the swimmer’s performance. Example of this technology is the use of inertial sensory devices such as tri-axial accelerometers. The accelerometers can be used to provide kinematic information with regards to the swimmer’s stroke rate, stroke length and stroke mechanics. In a typical training session, coaches would have to manually time and count their swimmer’s strokes to be able to gain the kinematic information they require. Hence, the use ofinertial sensory technology, such as accelerometers, would provide the necessary information coaches require, allowing them to concentrate on other performance aspects such as theirswimmer’s technique.Aim and objectives: The aim of this study was to determine the kinematic parameters and swimming stroke mechanics that could be derived from tri-axial accelerometers, during a 200-m individual medley (IM) event in South African national level swimmers. Three objectives were set to meet the aim of the study. The first was to identify and differentiate each of the stroking styles using tri-axial accelerometers. The second was to identify and differentiate the kinematic parametersand stroke mechanicsfor all four strokes using tri-axial accelerometers. The third objective was to implement machine learning to automate the identification and interpretation of the accelerometer data. Method:A quantitative, non-experimental descriptive one group post-test only design was used, in which 15 national level swimmers, of which seven male and eight female (mean ±SD: age: 20.9 ± 2.90 years; height: 173.28 ± 10.61 cm; weight: 67.81 ± 8.09 kg; arm span: 178.21 ± 12.15 cm) were tested. Three anthropometric measures were taken (height, weight and arm span) prior to testing, with two tri-axial accelerometers and Polar V800watch and heart rate belt attached to the swimmers left wrist, upper-back and chest, respectively. All swimmerswere required to perform three main swimming sets: 50-m IM, 100-m variation and 200-mIM. Variousdescriptivestatisticsincluding mean, standard deviation and confidence intervals (95%)were used to describe the data. with further inferential statistics including paired t-test, intra-class correlation and Bland Altman analysis wereused to describe the relationship ivbetween the accelerometer and the manually estimated parameters. Additionally, arepeated measures one-way ANOVA (with post-hoc Tukey HSD test) werealso used in an inter-comparison of the stroke parameters between each of the stroking styles. A confusion matrix wasused to measure the classification accuracy of the machine learning model implemented on the accelerometer data.Results:The accelerometers proved successful in identifyingand discerningthe stroke mechanics for each of the four stroking styles, with the use of video footage to validatethe findings. In the stroke kinematic differentiation, theBland Altman analysisresultsshowed an agreement between themanual method and accelerometer-derived estimates, although a discrepancy was evident for several of the kinematic parameters, with a significant difference found with the estimated lap time, average swimming velocity and stroke rate (paired t-test: p <0.001 for all swim sets). The inter-comparison between the stroke parameters per stroking style showed a significant difference with average swimming velocity (repeated one-way ANOVA: F = 1789.37, p <0.001), averages stroke rate (repeated one-way ANOVA: F = 671.70, p <0.001) and average stroke length (repeated one-way ANOVA: F = 346.46, p<0.001) for the population group tested. Furtheranalysis with post-hoc Tukey HSD test showed no significant difference wereevident for the average swimming velocity(Tukey: p > 0.05for all strokes)andbetween freestyle and backstroke for the average stroke rate and stroke length (Tukey:p = 0.0968 andp = 0.997, respectively).Lastly, the machine learning model found a classification accuracy of 96.6% in identifyingand labelling the stroking styles fromthe accelerometer data.Conclusion: It was shown that the tri-axial accelerometers were successful in the identification and differentiation of all the stroking styles, stroke mechanics and kinematics, although a discrepancy was found with the average swimming velocity, stroke rate and lap time estimations. The machine learning model implemented proved the benefits of using artificial intelligence to ease the data process and interpretation by automatically labelling the accelerometer data. Therefore, the use of tri-axial accelerometers as a coaching aid has major potential in the swimming community. However, further research is required to eliminate the time-consuming data processingand to increasetheaccuracy of the accelerometer in the measurement of all the stroke kinematics.
- Full Text:
- Date Issued: 2020
Comparison of the population growth potential of South African loggerhead (Caretta caretta) and leatherback (Dermochelys coriacea) sea turtles
- Authors: Tucek, Jenny Bianka
- Date: 2014
- Subjects: Sea turtles -- Population viability analysis -- South Africa , Migratory animals -- South Africa
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/5032 , vital:20793
- Description: A beach conservation programme protecting nesting loggerhead (Caretta caretta) and leatherback (Dermochelys coriacea) sea turtles in South Africa was started in 1963. As initial numbers of nesting females were low for both species (107 loggerheads and 24 leatherbacks) it was proposed that the protection of eggs, hatchlings and nesting females along the nesting beach would induce population growth and prohibit local extinction. Today, 50 years later, the loggerhead population exceeds 650 females per annum, whereas the leatherback population counts about 65 nesting females per year. The trend for leatherback turtles is that the population has been stable for about 30 years whereas loggerheads are increasing exponentially. Thus, this thesis investigated several life-history traits to explain the differing responses to the ongoing beach conservation programme. Reproductive output and success were assessed for both species; it was hypothesised that environmental conditions are sub-optimal for leatherback turtles to reproduce successfully. It was ascertained that nesting loggerhead females deposit larger clutches than leatherbacks (112 ± SD 20 eggs and 100 ± SD 23 eggs, respectively), but that annual reproductive output per individual leatherback female exceeds that of loggerhead turtles (±700 eggs and ±448 eggs, respectively) because they exhibit a higher intra-seasonal nesting frequency (leatherbacks n = 7 and loggerheads n = 4 from Nel et al. 2013). Emergence success (i.e. the percentage of hatchlings produced) per nest was similar for both species (loggerhead 73.6 ± SD 27.68 % and leatherback turtles 73.8 ± SD 22.70 %), but as loggerhead turtles nest in greater numbers, i.e. producing more hatchlings per year, the absolute population growth potential favours the loggerhead turtle. The second factor investigated was sex ratio because sea turtles display temperature-dependent sex determination (TSD) where extreme incubation temperatures can skew the sex ratio (i.e. feminising or masculinising a clutch). It was suspected that leatherback turtles are male-biased as this is the southern-most rookery (for both species). Further, leatherback nests are generally closer to the high tide mark, which might induce a cooling effect. Standard histological techniques were applied to sex hatchlings and a generalized linear model (GLM) was used to approximate annual sex ratio. Loggerhead sex ratio (2009 - 2011) was estimated at 86.9 ± SE 0.35 % female-biased; however, sufficient replication for the leatherback population was only obtained for season 2010, which indicated a 97.1 % (95 % CI 93.3 - 98.7) female bias. Both species are, thus, highly female-biased, and current sex ratio for leatherback turtles is not prohibiting population growth. Current sex ratios, however, are not necessarily indicative of sex ratios in the past which would have induced present population growth. Thus, to account for present population growth profiles, sex ratios from the past needed to be ascertained. Annual sex ratios (1997 - 2011) were modelled from historical air and sea surface temperatures (SSTs) but no significant change over time was obtained for either loggerhead or leatherback turtles (linear regression; p ≥ 0.45). The average sex ratio over this 15-year period for the South African loggerhead turtle was approximated at 77.1 ± SE 3.36 % female-biased, whereas leatherbacks exhibited a 99.5 ± SE 0.24 % female bias. Re-analysing data from the mid-80s by Maxwell et al. (1988) also indicated a 77.4 % female bias for the South African loggerhead population. It is, therefore, highly likely that sex ratios of the South African loggerhead and leatherback sea turtle populations have been stable for at least three decades and are not accountable for the differing population growth profiles as they are displayed today. Another possibility that could explain the opposed population growth profiles is the time taken for animals to replace themselves, i.e. age at maturity. It was suspected that age at maturity for the South African loggerhead turtle is comparable with that for leatherbacks. Using data from a 30-year mutilation tagging experiment (i.e. notching), age at first reproduction for South African loggerhead females was estimated. Results ranged broadly but a mean of 36.2 ± SD 7.71 years was obtained using a Gaussian distribution. Age at reproduction of the South African leatherback turtle was not determined but the literature suggests a much younger age of 13.3 - 26.8 years (Zug & Parham 1996, Dutton et al. 2005, Avens et al. 2009, Jones et al. 2011). Therefore, population growth would favour leatherback turtles as they exhibit a much shorter generation time. Finally, it was concluded that all life-history parameters investigated favour leatherback turtles, yet loggerheads are displaying population growth. However, as there were no obvious constraints to population growth on the nesting beach, it is suspected that population growth of the South African leatherback turtle is either unobserved (due to inadequate monitoring not capturing sufficient numbers of nesting events to establish a trend) or that population growth is prohibited by some offshore factor such as industrial fisheries (or some other driver not yet identified). Monitoring should, thus, be expanded and offshore mortality monitored as the leatherback population nesting in South Africa is still critically endangered with nesting numbers dangerously low.
- Full Text:
- Date Issued: 2014
- Authors: Tucek, Jenny Bianka
- Date: 2014
- Subjects: Sea turtles -- Population viability analysis -- South Africa , Migratory animals -- South Africa
- Language: English
- Type: Thesis , Doctoral , DPhil
- Identifier: http://hdl.handle.net/10948/5032 , vital:20793
- Description: A beach conservation programme protecting nesting loggerhead (Caretta caretta) and leatherback (Dermochelys coriacea) sea turtles in South Africa was started in 1963. As initial numbers of nesting females were low for both species (107 loggerheads and 24 leatherbacks) it was proposed that the protection of eggs, hatchlings and nesting females along the nesting beach would induce population growth and prohibit local extinction. Today, 50 years later, the loggerhead population exceeds 650 females per annum, whereas the leatherback population counts about 65 nesting females per year. The trend for leatherback turtles is that the population has been stable for about 30 years whereas loggerheads are increasing exponentially. Thus, this thesis investigated several life-history traits to explain the differing responses to the ongoing beach conservation programme. Reproductive output and success were assessed for both species; it was hypothesised that environmental conditions are sub-optimal for leatherback turtles to reproduce successfully. It was ascertained that nesting loggerhead females deposit larger clutches than leatherbacks (112 ± SD 20 eggs and 100 ± SD 23 eggs, respectively), but that annual reproductive output per individual leatherback female exceeds that of loggerhead turtles (±700 eggs and ±448 eggs, respectively) because they exhibit a higher intra-seasonal nesting frequency (leatherbacks n = 7 and loggerheads n = 4 from Nel et al. 2013). Emergence success (i.e. the percentage of hatchlings produced) per nest was similar for both species (loggerhead 73.6 ± SD 27.68 % and leatherback turtles 73.8 ± SD 22.70 %), but as loggerhead turtles nest in greater numbers, i.e. producing more hatchlings per year, the absolute population growth potential favours the loggerhead turtle. The second factor investigated was sex ratio because sea turtles display temperature-dependent sex determination (TSD) where extreme incubation temperatures can skew the sex ratio (i.e. feminising or masculinising a clutch). It was suspected that leatherback turtles are male-biased as this is the southern-most rookery (for both species). Further, leatherback nests are generally closer to the high tide mark, which might induce a cooling effect. Standard histological techniques were applied to sex hatchlings and a generalized linear model (GLM) was used to approximate annual sex ratio. Loggerhead sex ratio (2009 - 2011) was estimated at 86.9 ± SE 0.35 % female-biased; however, sufficient replication for the leatherback population was only obtained for season 2010, which indicated a 97.1 % (95 % CI 93.3 - 98.7) female bias. Both species are, thus, highly female-biased, and current sex ratio for leatherback turtles is not prohibiting population growth. Current sex ratios, however, are not necessarily indicative of sex ratios in the past which would have induced present population growth. Thus, to account for present population growth profiles, sex ratios from the past needed to be ascertained. Annual sex ratios (1997 - 2011) were modelled from historical air and sea surface temperatures (SSTs) but no significant change over time was obtained for either loggerhead or leatherback turtles (linear regression; p ≥ 0.45). The average sex ratio over this 15-year period for the South African loggerhead turtle was approximated at 77.1 ± SE 3.36 % female-biased, whereas leatherbacks exhibited a 99.5 ± SE 0.24 % female bias. Re-analysing data from the mid-80s by Maxwell et al. (1988) also indicated a 77.4 % female bias for the South African loggerhead population. It is, therefore, highly likely that sex ratios of the South African loggerhead and leatherback sea turtle populations have been stable for at least three decades and are not accountable for the differing population growth profiles as they are displayed today. Another possibility that could explain the opposed population growth profiles is the time taken for animals to replace themselves, i.e. age at maturity. It was suspected that age at maturity for the South African loggerhead turtle is comparable with that for leatherbacks. Using data from a 30-year mutilation tagging experiment (i.e. notching), age at first reproduction for South African loggerhead females was estimated. Results ranged broadly but a mean of 36.2 ± SD 7.71 years was obtained using a Gaussian distribution. Age at reproduction of the South African leatherback turtle was not determined but the literature suggests a much younger age of 13.3 - 26.8 years (Zug & Parham 1996, Dutton et al. 2005, Avens et al. 2009, Jones et al. 2011). Therefore, population growth would favour leatherback turtles as they exhibit a much shorter generation time. Finally, it was concluded that all life-history parameters investigated favour leatherback turtles, yet loggerheads are displaying population growth. However, as there were no obvious constraints to population growth on the nesting beach, it is suspected that population growth of the South African leatherback turtle is either unobserved (due to inadequate monitoring not capturing sufficient numbers of nesting events to establish a trend) or that population growth is prohibited by some offshore factor such as industrial fisheries (or some other driver not yet identified). Monitoring should, thus, be expanded and offshore mortality monitored as the leatherback population nesting in South Africa is still critically endangered with nesting numbers dangerously low.
- Full Text:
- Date Issued: 2014
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