Kinematics Research Progress of Swim-start on the New Start Block

Yang, Fan

doi:10.5334/paah.7

Cited by 4 publications

(3 citation statements)

References 17 publications

(19 reference statements)

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“…the same is done in the case of swim start analysis, often limited only to the phases visible above the water [5][6][7]. With the recordings of these fragments of the swim start, it was found, among others, that the latest technique of performing this element, the kick start (performed with the socalled back plate), helped reduce the start time more efficiently than older techniques (track or grab start) [8]. Using the back plate enables more optimal positioning of the rear limb during push-off, thereby in-creasing the value of the horizontal component of contact force on the starting block [5,9].…”

Section: Introductionmentioning

confidence: 99%

Kinematic analysis of above- and underwater swim start phases of male swimmers aged 16–18 years

Wądrzyk¹,

Staszkiewicz²,

Kryst³

et al. 2021

Hum Mov.

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Purpose. swim start technique analysis is usually conducted in elite swimmers or only limited to above-water phases. the aim of the study was to analyse kinematic parameters of above-and underwater kick start phases among young male swimmers. Methods. the study group comprised male swimmers (FINA points per 100-m freestyle: 525 ± 84). the subjects performed a 15-m front crawl with kick start. the trials were recorded above and below water with 3 recording devices synchronized with the starting signal. Kinematic analysis by using 2 models (above-and underwater) of body was done. On the basis of time to cover 15 m, 2 groups were distinguished: quickly (Fs) and slowly (ss) performing the swim start.Results. Fs and ss demonstrated differences (p < 0.05) regarding hip joint height at starting signal (1.56 ± 0.05 m vs. 1.45 ± 0.05 m), push-off angle (23.89 ± 6.50° vs. 35.12 ± 3.43°), hip joint angle upon completing push-off (163.83 ± 8.37° vs. 149.73 ± 9.93°), and horizontal velocity during flight (3.70 ± 0.35 m/s vs. 3.24 ± 0.22 m/s), submersion (4.88 ± 0.09 m/s vs. 4.36 ± 0.22 m/s), and glide phase (3.40 ± 0.17 m/s vs. 2.99 ± 0.29 m/s). Conclusions. the obtained results indicate that position on the block significantly influences the movement course in consecutive phases. Young competitors should aim towards elevated hip positioning, allowing to maximize horizontal velocity during the flight, submersion, and glide phases.

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Section: Introductionmentioning

confidence: 99%

Kinematic analysis of above- and underwater swim start phases of male swimmers aged 16–18 years

Wądrzyk¹,

Staszkiewicz²,

Kryst³

et al. 2021

Hum Mov.

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“…Their outputs will be computed in the L1 component-wise differences and compared to have a similar output from the same class and a different output from the different classes. The input to the network is a fixed size vector, Λ t as in (15) to encode the underlying features in a gait cycle.…”

Section: Spatiotemporal-based Approachmentioning

confidence: 99%

“…Thus, in real-world settings, both marker-based and current markerless systems are not feasible options. Yang's work on the kinematic research of swim-start on a start block can be a good example [15].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Markerless Vision-Based Gait Analyses for Person Re-Identification

Kwon

Lee

2021

Sensors

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The model-based gait analysis of kinematic characteristics of the human body has been used to identify individuals. To extract gait features, spatiotemporal changes of anatomical landmarks of the human body in 3D were preferable. Without special lab settings, 2D images were easily acquired by monocular video cameras in real-world settings. The 2D and 3D locations of key joint positions were estimated by the 2D and 3D pose estimators. Then, the 3D joint positions can be estimated from the 2D image sequences in human gait. Yet, it has been challenging to have the exact gait features of a person due to viewpoint variance and occlusion of body parts in the 2D images. In the study, we conducted a comparative study of two different approaches: feature-based and spatiotemporal-based viewpoint invariant person re-identification using gait patterns. The first method is to use gait features extracted from time-series 3D joint positions to identify an individual. The second method uses a neural network, a Siamese Long Short Term Memory (LSTM) network with the 3D spatiotemporal changes of key joint positions in a gait cycle to classify an individual without extracting gait features. To validate and compare these two methods, we conducted experiments with two open datasets of the MARS and CASIA-A datasets. The results show that the Siamese LSTM outperforms the gait feature-based approaches on the MARS dataset by 20% and 55% on the CASIA-A dataset. The results show that feature-based gait analysis using 2D and 3D pose estimators is premature. As a future study, we suggest developing large-scale human gait datasets and designing accurate 2D and 3D joint position estimators specifically for gait patterns. We expect that the current comparative study and the future work could contribute to rehabilitation study, forensic gait analysis and early detection of neurological disorders.

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