Motion Pattern Tracking Classification In Bowling Matches
Motion pattern tracking is the tracing of object movements and transferring the informative data for analyses. Previous motion tracking studies in sports focused on the changing position of the players rather than actual body segment interactions. Thus, a gap is identified for further research works...
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| Format: | Monograph |
| Language: | English |
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Universiti Sains Malaysia
2017
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| Online Access: | http://eprints.usm.my/53727/1/Motion%20Pattern%20Tracking%20Classification%20In%20Bowling%20Matches_Daryl%20Tan%20Hock%20Ann_M4_2017.pdf |
| _version_ | 1825906941331767296 |
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| author | Daryl, Tan Hock Ann |
| author_facet | Daryl, Tan Hock Ann |
| author_sort | Daryl, Tan Hock Ann |
| collection | USM |
| description | Motion pattern tracking is the tracing of object movements and transferring the informative data for analyses. Previous motion tracking studies in sports focused on the changing position of the players rather than actual body segment interactions. Thus, a gap is identified for further research works. Therefore, this paper presents a motion pattern tracking approach for bowling game posture classifications. The objectives are to (i)design a framework to recognize and classify motion patterns, (ii)explore sequences of motion strategies, (iii)analyze motion to recognize bowlers‟ relative movements by dominant posture sequences and (iv)examine motion pattern characteristics by classification analysis and rules-reasoning under several parameters namely shoulder, body bend, balance, swing angles and distance of feet. Motion is tracked on sequential image frames of video records and the numeric data retrieved using the Photoshop tool. Preprocessed data are classified using WEKA software for analytical information and grouping body motions into three predefined classes: GOOD, MODERATE, BAD. The main classifier chosen is the Random Tree. The findings show four main conditions concerning body motion to result in GOOD body motion postures for bowling mainly Rule 1: final shoulder angle is <109.32⁰ and the final body bend angle<50.13⁰, (Rule 2: final balance angle<89.03⁰ and the maximum change in swing angle<82.41⁰ and 48.27⁰< final body bend angle<50.10⁰, Rule 3: 17.78cm<maximum change in distance of feet<69.82cm and final body bend angle>39.17Rule 4: maximum change in distance of feet> 69.82cm and final body bend angle<51.48⁰ and maximum change in swing angle<56.19⁰. |
| first_indexed | 2024-03-06T15:56:59Z |
| format | Monograph |
| id | usm.eprints-53727 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:56:59Z |
| publishDate | 2017 |
| publisher | Universiti Sains Malaysia |
| record_format | dspace |
| spelling | usm.eprints-537272022-07-29T13:08:58Z http://eprints.usm.my/53727/ Motion Pattern Tracking Classification In Bowling Matches Daryl, Tan Hock Ann T Technology TJ Mechanical engineering and machinery Motion pattern tracking is the tracing of object movements and transferring the informative data for analyses. Previous motion tracking studies in sports focused on the changing position of the players rather than actual body segment interactions. Thus, a gap is identified for further research works. Therefore, this paper presents a motion pattern tracking approach for bowling game posture classifications. The objectives are to (i)design a framework to recognize and classify motion patterns, (ii)explore sequences of motion strategies, (iii)analyze motion to recognize bowlers‟ relative movements by dominant posture sequences and (iv)examine motion pattern characteristics by classification analysis and rules-reasoning under several parameters namely shoulder, body bend, balance, swing angles and distance of feet. Motion is tracked on sequential image frames of video records and the numeric data retrieved using the Photoshop tool. Preprocessed data are classified using WEKA software for analytical information and grouping body motions into three predefined classes: GOOD, MODERATE, BAD. The main classifier chosen is the Random Tree. The findings show four main conditions concerning body motion to result in GOOD body motion postures for bowling mainly Rule 1: final shoulder angle is <109.32⁰ and the final body bend angle<50.13⁰, (Rule 2: final balance angle<89.03⁰ and the maximum change in swing angle<82.41⁰ and 48.27⁰< final body bend angle<50.10⁰, Rule 3: 17.78cm<maximum change in distance of feet<69.82cm and final body bend angle>39.17Rule 4: maximum change in distance of feet> 69.82cm and final body bend angle<51.48⁰ and maximum change in swing angle<56.19⁰. Universiti Sains Malaysia 2017-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53727/1/Motion%20Pattern%20Tracking%20Classification%20In%20Bowling%20Matches_Daryl%20Tan%20Hock%20Ann_M4_2017.pdf Daryl, Tan Hock Ann (2017) Motion Pattern Tracking Classification In Bowling Matches. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Mekanik. (Submitted) |
| spellingShingle | T Technology TJ Mechanical engineering and machinery Daryl, Tan Hock Ann Motion Pattern Tracking Classification In Bowling Matches |
| title | Motion Pattern Tracking Classification In Bowling Matches |
| title_full | Motion Pattern Tracking Classification In Bowling Matches |
| title_fullStr | Motion Pattern Tracking Classification In Bowling Matches |
| title_full_unstemmed | Motion Pattern Tracking Classification In Bowling Matches |
| title_short | Motion Pattern Tracking Classification In Bowling Matches |
| title_sort | motion pattern tracking classification in bowling matches |
| topic | T Technology TJ Mechanical engineering and machinery |
| url | http://eprints.usm.my/53727/1/Motion%20Pattern%20Tracking%20Classification%20In%20Bowling%20Matches_Daryl%20Tan%20Hock%20Ann_M4_2017.pdf |
| work_keys_str_mv | AT daryltanhockann motionpatterntrackingclassificationinbowlingmatches |