Desarrollo de un instrumento de prueba de abdominales basado en la cámara Kinect
Resumen
Este estudio tuvo como objetivo desarrollar un instrumento de prueba de abdominales utilizando una cámara Kinect para medir con precisión el rendimiento y evaluar la calidad de los movimientos de abdominales. Se utilizó un diseño de investigación y desarrollo (I+D). Los sujetos consistieron en 55 atletas masculinos de entre 15 y 22 años, que representaban varios deportes, a saber: remo, clavados, voleibol, fútbol, tenis de mesa y Sepak Takraw, con una altura promedio de ±177,8 cm y un peso corporal promedio de ±67,9 kg. La prueba del producto se realizó primero en un grupo pequeño de 15 participantes, seguida de una re-prueba en un grupo más grande de 40 participantes. Este proceso tuvo como objetivo evaluar la consistencia y la preparación del producto desarrollado. El estudio contó con la participación de tres expertos en pruebas deportivas, biomecánica y software para evaluar la validez, practicidad y efectividad del producto diseñado. Según los resultados de la evaluación, el experto en pruebas y medición deportiva obtuvo una puntuación de 81, el experto en biomecánica deportiva 87 y el experto en software 86. Los valores de alfa de Cronbach (α) fueron 0,82 y 0,78, cumpliendo los requisitos de fiabilidad. Se encontró que el instrumento de prueba de abdominales basado en la cámara Kinect era efectivo y eficiente de usar, con resultados válidos y confiables en la categoría muy buena. Sin embargo, una limitación de esta herramienta es que no puede utilizarse al aire libre, ya que la cámara Kinect no es óptima para capturar movimientos bajo la luz del sol.
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1. Abbasi, J., Salarieh, H., & Alasty, A. (2021). A motion capture algorithm based on inertia-Kinect sensors for lower body elements and step length estimation. Biomedical Signal Processing and Control, 64, 1-19. https://doi.org/10.1016/j.bspc.2020.102290
2. Abdillah, L. A., & Kurniasti, A. (2022). Mobile-based COVID-19 Vaccination Registration Application Prototype. SinkrOn: Jurnal Dan Penelitian Teknik Informatika, 7(3), 2152–2159. https://doi.org/10.33395/sinkron.v7i3.11779
3. Aditya Gumantan. (2020). Pengembangan Aplikasi Pengukuran Tes Kebugaran Jasmani Berbasis Android. Jurnal Ilmu Keolahragaan, 19(2), 196–205. https://doi.org/10.24114/jik.v19i2.21828
4. Agustina, C., & Wahyudi, T. (2015). Aplikasi Game Pendidikan Berbasis Android Untuk Memperkenalkan Pakaian Adat Indonesia. Indonesian Journal on Software Engineering, 1(1), 2–3. https://doi.org/10.31294/ijse.v1i1.590
5. Banky, M., Clark, R. A., Mentiplay, B. F., Olver, J. H., Kahn, M. B., & Williams, G. (2019). Toward Accurate Clinical Spasticity Assessment: Validation of Movement Speed and Joint Angle Assessments Using Smartphones and Camera Tracking. Archives of Physical Medicine and Rehabilitation, 100(8), 1482–1491. https://doi.org/10.1016/j.apmr.2018.11.026
6. Bittar, E., Desprez, P.-É., Nocent, O., Grisonnet, B., & Soilih, A. (2017). LeBonGeste: Basketball training by entertaining. Procedia Computer Science, 112, 1281–1287. https://doi.org/10.1016/j.procs.2017.08.084
7. Blazek, D., Stastny, P., Maszczyk, A., Krawczyk, M., Matykiewicz, P., & Petr, M. (2019). Systematic review of intra-abdominal and intrathoracic pressures initiated by the Valsalva manoeuvre during high-intensity resistance exercises. Biology of Sport, 36(4), 373–386. https://doi.org/10.5114/biolsport.2019.88759
8. Bompa, T. O., & Carrera, M. (2015). Conditioning Young Athletes. Human Kinetics.
9. Childs, J. D., Teyhen, D. S., Benedict, T. M., Morris, J. B., Fortenberry, A. D., Mcqueen, R. M., Preston, J. B., Wright, A. C., Dugan, J. L., & George, A. S. Z. (2009). Effects of sit-up training versus core stabilization exercises on sit-up performance. Medicine and Science in Sports and Exercise, 41(11), 2072–2083.
10. Choppin, S., & Wheat, J. (2013). The potential of the Microsoft Kinect in sports analysis and biomechanics. Sports Technology, 6(2), 37–41. https://doi.org/http://dx.doi.org/10.1080/19346182.2013.819008
11. Çubukçu, B., Yüzgeç, U., Zileli, R., & Zileli, A. (2020). Reliability and validity analyzes of Kinect V2 based measurement system for shoulder motions. Medical Engineering and Physics, 76, 20–31. https://doi.org/10.1016/j.medengphy.2019.10.017
12. Currell, K., & Jeukendrup, A. E. (2008). Validity, reliability and sensitivity of measures of sporting performance. Sports Medicine, 38(4), 297–316. https://doi.org/10.2165/00007256-200838040-00003
13. del Rocío Medrano Ureña, M., Ruiz, R. O., & de Dios Benítez Sillero, J. (2023). The Role of Self-Efficacy and Physical Fitness in Sustaining Physical Activity in Middle Aged Women. Retos, 49, 701–712. https://doi.org/10.47197/RETOS.V49.98105
14. Dhevangga Pristawan Abhimasta, Stanislause Wiriawan, O. (2020). Perbandingan Hasil Tes Kondisi Fisik Atlit Anggar Tahun 2018 Dan 2019 Koni Sidoarjo. Jurnal Prestasi Olahraga, 3(3), 1-7.
15. Efendi, R. (2021). Pengembangan alat penghitung jumlah tes sit up dengan menggunakan Arduino (Undergraduate thesis, Universitas Negeri Padang). http://repository.unp.ac.id/40375/
16. Eltoukhy, M., Kuenze, C., Oh, J., Wooten, S., & Signorile, J. (2017). Kinect-based assessment of lower limb kinematics and dynamic postural control during the star excursion balance test. Gait and Posture, 58, 421–427. https://doi.org/10.1016/j.gaitpost.2017.09.010
17. Fikri, A., Pratama, R. R., Widiastuti, Samsudin, Muslimin, Haqiyah, A., Ramadhan, A., Hardiyono, B., & Hidayat, A. (2022). Tennis Ball Exersice: Variation to Increase Arm Muscle Strength in Martial Athletes at Sriwijaya State Sports School. International Journal of Human Movement and Sports Sciences, 10(5), 964–972. https://doi.org/10.13189/saj.2022.100513
18. Geerse, D. J., Coolen, B. H., & Roerdink, M. (2015). Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait Assessments. PLOS ONE, 10(10), 1-15. https://doi.org/10.1371/JOURNAL.PONE.0139913
19. González, P. P., Sánchez-Infante, J., & Fernández-Galván, L. M. (2022). Do young adult males aiming to improve strength or develop muscle hypertrophy train according to the current strength and conditioning recommendations? Retos, 46, 714–724. https://doi.org/10.47197/retos.v46.93785
20. Guess, T. M., Bliss, R., Hall, J. B., & Kiselica, A. M. (2022). Comparison of Azure Kinect overground gait spatiotemporal parameters to marker based optical motion capture. Gait and Posture, 96, 130–136. https://doi.org/10.1016/j.gaitpost.2022.05.021
21. Hackett, D. A., & Chow, C. M. (2013). The valsalva maneuver: Its effect on intra-abdominal pressure and safety issues during resistance exercise. Journal of Strength and Conditioning Research, 27(8), 2338–2345. https://doi.org/10.1519/JSC.0b013e31827de07d
22. Hoeger, W. W. K., & Hoeger, S. A. (2016). Principles and labs for physical fitness (10th ed.). Cengage Learning.
23. Iliukhin, V. N., Mitkovskii, K. B., Bizyanova, D. A., & Akopyan, A. A. (2017). The development of motion capture system based on Kinect Sensor and Bluetooth-Gloves. Dynamics and Vibroacoustics of Machines (DVM2016), 176, 506–513. https://doi.org/10.1016/j.proeng.2017.02.350
24. Kalb, R., Brown, T. R., Coote, S., Costello, K., Dalgas, U., Garmon, E., Giesser, B., Halper, J., Karpatkin, H., Keller, J., Ng, A. V., Pilutti, L. A., Rohrig, A., Van Asch, P., Zackowski, K., & Motl, R. W. (2020). Exercise and lifestyle physical activity recommendations for people with multiple sclerosis throughout the disease course. Multiple Sclerosis Journal, 26(12), 1459–1469. https://doi.org/10.1177/1352458520915629
25. Kukić, F., Orr, R., Marković, M., Dawes, J. J., Čvorović, A., & Koropanovski, N. (2022). Factorial and Construct Validity of Sit-Up Test of Different Durations to Assess Muscular Endurance of Police Students. Sustainability, 14(20), 1-9. https://doi.org/10.3390/su142013630
26. Kurniawan, I. (2021). Pengembangan alat tes sit up berbasis teknologi sensor pada tim sepakbola Garuda Muda Ogan Komering Ulu (Undergraduate thesis, Universitas Sriwijaya). Universitas Sriwijaya Repository. https://repository.unsri.ac.id/52979/
27. Kurt, C., Canli, U., & Prieto-González, P. (2024). Exploring the Relationship Between Motor Competence and Physical Performance in Preschool Chil-dren: A Cross-Sectional Study. Retos, 55, 635–641. https://doi.org/10.47197/retos.v55.104197
28. Li, G., & Li, C. (2020). Learning skeleton information for human action analysis using Kinect. Signal Processing: Image Communication, 84(1), 1-18. https://doi.org/10.1016/j.image.2020.115814
29. Lin, Y. K., Tsai, K. Z., Han, C. L., Lee, J. T., & Lin, G. M. (2021). Athlete’s Heart Assessed by Sit-Up Strength Exercises in Military Men and Women: The CHIEF Heart Study. Frontiers in Cardiovascular Medicine, 8, 1-6. https://doi.org/10.3389/fcvm.2021.737607
30. Llerena, A. M., Blanco, P. C., & Hernández, E. H. (2020). Design, validation, and reliability of an instrument to assess tactical behaviors in volleyball initiation. Retos, 83, 661–666. https://doi.org/https://doi.org/10.47197/retos.v38i38.77792
31. Lopes Dos Santos, M., Thompson, M., Dinyer-Mcneely, T., Torrence, T., Lockie, R. G., Orr, R. M., & Dawes, J. J. (2023). Differences and Relationships between Push-up and Sit-up Variations among Male Law Enforcement Cadets. Journal of Strength and Conditioning Research, 37(9), 1865–1869. https://doi.org/10.1519/JSC.0000000000004466
32. Lopez-Sanchez, G. F., Radziminski, L., Skalska, M., Jastrzebska, J., Smith, L., Wakuluk, D., & Jastrzebski, Z. (2019). Body composition, physical fitness , physical activity and nutrition in Polish and Spanish. Science & Sports, 19(6), 1–8. https://doi.org/10.1016/j.scispo.2019.04.002
33. Mardela, R., Irawan, R., Laksana, A. A. N. P., Marlina, Y., & Efendi, R. (2023). Development of a Digital-Based Push Up and Sit Up Test Counter. Halaman Olahraga Nusantara, 6(1), 1-18. https://doi.org/10.31851/hon.v6i1.10723
34. Mashud, Arifin, S., Warni, H., Samodra, Y. T. J., Yosika, G. F., Basuki, S., Suryadi, D., & Suyudi, I. (2024). Physical Fitness: Effects of active lifestyle internalization through physical literacy awarenes based project. Retos, 51, 1299–1308. https://doi.org/10.47197/RETOS.V51.101662
35. Mora-Gonzalez, J., Esteban-Cornejo, I., Cadenas-Sanchez, C., Migueles, J. H., Molina-Garcia, P., Rodriguez-Ayllon, M., Henriksson, P., Pontifex, M. B., Catena, A., & Ortega, F. B. (2019). Physical Fitness, Physical Activity, and the Executive Function in Children with Overweight and Obesity. Journal of Pediatrics, 208, 50-56. https://doi.org/10.1016/j.jpeds.2018.12.028
36. Müller, B., Ilg, W., Giese, M. A., & Ludolph, N. (2017). Improved Kinect sensor based motion capturing system for gait assessment. Journal PONE, 12(4), 14–16. https://doi.org/10.1101/098863
37. Muñoz, C. L., Campillo, R. R., Gil, P. T., & Sáez, E. S. de V. (2024). Effects of Combined Strength Training Methods on Athletes and Healthy Participants Sprint and Strength Performance: A Systematic Review and Meta‑analysis of Controlled Studies. Retos, 55, 999–1009. https://doi.org/10.47197/retos.v55.105264
38. Nour-Frías, D. I., Fernández-Ozcorta, E. J., & Ramos-Véliz, R. (2024). Strength Training Practices in Team Sports. Retos, 51, 1395–1403. https://doi.org/10.47197/RETOS.V51.100966
39. Oh, J., Kuenze, C., Jacopetti, M., Signorile, J. F., & Eltoukhy, M. (2018). Validity of the Microsoft Kinect™ in assessing spatiotemporal and lower extremity kinematics during stair ascent and descent in healthy young individuals. Medical Engineering & Physics, 60, 70–76. https://doi.org/10.1016/j.medengphy.2018.07.011
40. Özsoy, U., Yıldırım, Y., Karaşin, S., Şekerci, R., & Süzen, L. B. (2022). Reliability and agreement of Azure Kinect and Kinect v2 depth sensors in the shoulder joint range of motion estimation. Journal of Shoulder and Elbow Surgery, 31(10), 2049–2056. https://doi.org/10.1016/j.jse.2022.04.007
41. Peper, C. (Lieke) E., de Boer, B. J., de Poel, H. J., & Beek, P. J. (2008). Interlimb coupling strength scales with movement amplitude. Neuroscience Letters, 437(1), 10–14. https://doi.org/10.1016/j.neulet.2008.03.066
42. Raditya, Y., & Irawati, D. A. (2015). Rancang bangun aplikasi game olahraga lari halang rintang dengan Kinect. Prosiding Seminar Informatika Aplikatif Polinema 2015 (SIAP 2015), 113–117.
43. Reynolds, J. E., Thornton, A. L., Lay, B. S., Braham, R., & Rosenberg, M. (2014). Human Movement Science Does movement proficiency impact on exergaming performance ? Human Movement Science, 34, 1–11. https://doi.org/10.1016/j.humov.2014.02.007
44. Rifki, M. S., Farma, F., Komaini, A., Sepdanius, E., Alimuddin, & Ayubi, N. (2022). Development of Sit Up Measuring Tools Based on Arduino and Ultrasonic Sensors With Android Applications. International Journal of Interactive Mobile Technologies, 16(8), 182–189. https://doi.org/10.3991/ijim.v16i08.30673
45. Ripic, Z., Kuenze, C., Andersen, M. S., Theodorakos, I., Signorile, J., & Eltoukhy, M. (2022). Ground reaction force and joint moment estimation during gait using an Azure Kinect-driven musculoskeletal modeling approach. Gait and Posture, 95, 49–55. https://doi.org/10.1016/j.gaitpost.2022.04.005
46. Robertson, S. J., Burnett, A. F., & Cochrane, J. (2014). Tests examining skill outcomes in sport: A systematic review of measurement properties and feasibility. Sports Medicine, 44(4), 501–518. https://doi.org/10.1007/s40279-013-0131-0
47. Rodriguez-Fuentes, G., Campo-Prieto, P., & Cancela-Carral, J. M. (2022). Lifestyles and habits of a Spanish University Community in times of COVID-19: a cross-sectional study. Retos, 46, 283–293. https://doi.org/10.47197/retos.v46.93101
48. Suchomel, T. J., Nimphius, S., & Stone, M. H. (2016). The importance of muscular strength in athletic performance. Sports Med, 46(14), 19–49.
49. Susiono, R., Sugiyanto, F. X., Lumintuarso, R., Tomoliyus, Sukamti, E. R., Fauzi, Hariono, A., & Prabowo, T. A. (2024). Y Agility Test Innovation on Special Badminton Athletes for the Junior Category (U17): Validity and Reliability. Retos, 53, 547–553. https://doi.org/10.47197/retos.v53.103282
50. Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
51. Thomas, J., Hall, J. B., Bliss, R., & Guess, T. M. (2022). Comparison of Azure Kinect and optical retroreflective motion capture for kinematic and spatiotemporal evaluation of the sit-to-stand test. Gait and Posture, 94, 153–159. https://doi.org/10.1016/j.gaitpost.2022.03.011
52. Usra, M., Lesmana, I. B., Octara, K., Bayu, W. I., Badau, A., Ishak, A., & Setiawan, E. (2024). Augmented Reality Training on Combat Sport: Improving the Quality of Physical Fitness and Technical Performance of Young Athletes. Retos, 54, 835–843. https://doi.org/10.47197/retos.v54.103743
53. Vancampfort, D., Vandael, H., Hallgren, M., Probst, M., Hagemann, N., Bouckaert, F., & Van Damme, T. (2019). Physical fitness and physical activity levels in people with alcohol use disorder versus matched healthy controls: A pilot study. Alcohol, 76, 73–79. https://doi.org/10.1016/j.alcohol.2018.07.014
54. Wanangsyah, W., Wurijanto, T., & Sutanto, T. (2014). Aplikasi Virtual Punch Training Menggunakan Microsoft XBOX Kinect. Jurnal Sistem Informasi, 3(1), 94-101. http://jurnal.stikom.edu/index.php/
55. Woldegiorgis, B. H., Lin, C. J., & Sananta, R. (2021). Using Kinect body joint detection system to predict energy expenditures during physical activities. Applied Ergonomics, 97(8), 1-21. https://doi.org/10.1016/j.apergo.2021.103540
56. Xu, X., & McGorry, R. W. (2015). The validity of the first and second generation Microsoft Kinect for identifying joint center locations during static postures. Applied Ergonomics, 49, 47–54. https://doi.org/10.1016/j.apergo.2015.01.005
57. Yang, M. T., & Chuang, M. W. (2013). Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home. Sensors, 13(12), 16985–17005. https://doi.org/10.3390/S131216985
58. Zhannisa, U. H., & Sugiyanto, F. (2015). Model Tes Fisik Pencarian Bakat Olahraga Bulutangkis Usia Di Bawah 11 Tahun Di Diy. Jurnal Keolahragaan, 3(1), 117–126. https://doi.org/10.21831/jk.v3i1.4974
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