Acceso abierto

Research on aerobics training posture motion capture based on mathematical similarity matching statistical analysis

Qiuju Chen
Qiuju Chen
 y 
Rayan Atteah Alsemmeari
Rayan Atteah Alsemmeari
   | 22 nov 2021
Applied Mathematics and Nonlinear Sciences's Cover Image
Acerca de este artículo
Artículo anterior
Artículo siguiente
Cite
Publicado en línea: 22 nov 2021
Páginas: 203 - 216
Recibido: 17 jun 2021
Aceptado: 24 sept 2021
DOI: https://doi.org/10.2478/amns.2021.2.00055
Palabras clave
© 2021 Qiuju Chen et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

3D motion data capture process.
3D motion data capture process.

Fig. 2

Laboratory scene and main equipment.
Laboratory scene and main equipment.

Fig. 3

An actor in a monochrome costume with a marker.
An actor in a monochrome costume with a marker.

Fig. 4

Data and actor binding.
Data and actor binding.

Fig. 5

Method flowchart.
Method flowchart.

Fig. 6

Dance gesture.
Dance gesture.

Fig. 7

Human skeleton and model.
Human skeleton and model.

Fig. 8

Comparison of motion trajectories in a single direction between individual landmarks.
Comparison of motion trajectories in a single direction between individual landmarks.

Fig. 9

Marker data based on Euclidean distance.
Marker data based on Euclidean distance.

Fig. 10

Plan view of human bone features.
Plan view of human bone features.

Fig. 11

Timing chart of left arm motion correlation parameters.
Timing chart of left arm motion correlation parameters.

Fig. 12

x-axis distance difference of a single feature point.
x-axis distance difference of a single feature point.

Fig. 13

Euclidean distance difference of a single feature point.
Euclidean distance difference of a single feature point.

Torso pose feature vectors.

Torso movement posture Feature vector
Head direction V5 = VLHead × VRHead
Chest direction V6 = Vchest
Hip direction V7 = (Coornavel − CoorLHip) × (Coornavel − CoorRHip)

Parameters related to the posture of the left arm to be measured.

Timing Sim (V2, Vstand) corr (VLLarm, VLFarm) corr (VLFarm, Vstand)
0–1 s 0.6357 0.9433 0.9576
1–2 s 0.6958 0.9613 0.9879
2–3 s 0.7253 0.9715 0.9646
3–4 s 0.7110 0.9698 0.9559
4–5 s 0.6906 0.8399 0.9848
5–6 s 0.6506 0.9648 0.9547
6–7 s 0.5998 0.9498 0.9646
7–8 s 0.7199 0.9650 0.9689
8–9 s 0.6826 0.9798 0.9398
9–10 s 0.6595 0.9698 0.9698

Posture correlation parameters of the left arm standard movement.

Timing Sim (V2, Vstand) Corr (VLLarm, VLFarm) Corr (VLFarm, Vstand)
0–1 s 0.6442 0.9365 0.9586
1–2 s 0.7004 0.9623 0.9956
2–3 s 0.7257 0.9726 0.9659
3–4 s 0.7126 0.9759 0.9546
4–5 s 0.6897 0.9588 0.9849
5–6 s 0.6416 0.9659 0.9546
6–7 s 0.5659 0.9588 0.9659
7–8 s 0.7258 0.9649 0.9785
8–9 s 0.6896 0.9876 0.9416
9–10 s 0.6585 0.9659 0.9755

Relative error of main movement attitude of test object/%.

Timing Sim V2, Vstand) Corr VLLarm, VLFarm) Corr VLFarm, Vstand)
0–1 s 1.32 0.72 0.11
1–2 s 0.65 0.10 0.77
2–3 s 0.05 0.11 0.13
3–4 s 0.22 0.63 0.14
4–5 s 0.13 12.54 0.01
5–6 s 1.41 0.11 0.01
6–7 s 5.65 0.95 0.13
7–8 s 0.82 0.01 0.99
8–9 s 1.02 0.79 0.19
9–10 s 0.15 0.40 0.58

Characteristic vectors of limbs.

Limb movement posture Feature vector
Stand upright Vstand = Positive Z axis direction in world coordinate system
Left upper arm VLFarm = CoorLElbow − CoorLShoulder
Left lower arm VLLarm = CoorLElbow − CoorLHand
Right upper arm VRFarm = CoorR Elbow − CoorRShoulder
Right lower arm VRLarm = CoorR Elbow − CoorRHand
Left thigh VLThigh = CoorL Knee − CoorLHip
Left calf VLCrus = CoorL Knee − CoorLAnkle
Right thigh VRThigh = CoorR Knee − CoorRHip
Right calf VRCrus = CoorR Knee − CoorRAnkle
Left arm feature plane V1 = VLLarm × VLFarm
Right arm feature plane V2 = VRLarm × VRFarm
Left leg feature plane V3 = VLThigh × VLCrus
Right leg feature plane V4 = VRThigh × VRCrus
eISSN:
2444-8656
Idioma:
Inglés
Calendario de la edición:
Volume Open
Temas de la revista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics
RSS Feed de revista