Depth-Sensing-Based Algorithm for Chest Morphology Assessment in Children with Cerebral Palsy
Abstract
:1. Introduction
2. Proposed Methodology
2.1. Depth-Sensing Technology
2.2. Subjects
2.3. Data Acquisition Set-Up
2.4. The Algorithm
2.5. Point Cloud Data Processing
2.6. Mathematical Representation of the Grid of Channels
2.7. Depth Variation Data Extraction
2.8. Depth Variation Data Analysis
2.9. Continuous-Wavelet-Transform-Based Artifact Removal
2.10. Breath Curve Analysis
2.11. Point-Cloud Cross-Sections
3. Results
3.1. Analysis of the Dispersion of the CSs
3.2. Analysis of the Chest Mobility
3.3. Analysis of the Relative Change in Morphology
3.4. Comparison of Morphological Parameters between the Sessions
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Rosenbaum, P.; Paneth, N.; Leviton, A.; Goldstein, M.; Bax, M. A report: The definition and classification of cerebral palsy April 2006. Dev. Med. Child Neurol. 2007, 49 (Suppl. S2), 8–14. [Google Scholar] [CrossRef]
- Horimoto, Y.; Osuda, Y.; Takada, C.; Tsugawa, S.; Kozuka, N.; Yoshida, S.; Otani, T.; Miwa, M. Thoracic Deformity in the Transverse Plane among Adults with Severe Cerebral Palsy. J. Phys. Ther. Sci. 2012, 24, 763–766. [Google Scholar] [CrossRef]
- Bennett, S.; Siritaratiwat, W.; Tanrangka, N.; Bennett, M.J.; Kanpittaya, J. Effectiveness of the manual diaphragmatic stretching technique on respiratory function in cerebral palsy: A randomised controlled trial. Respir. Med. 2021, 184, 106443. [Google Scholar] [CrossRef]
- Park, E.S.; Park, J.H.; Rha, D.-W.; Park, C.I.; Park, C.W. Comparison of the Ratio of Upper to Lower Chest Wall in Children with Spastic Quadriplegic Cerebral Palsy and Normally Developed Children. Yonsei Med. J. 2006, 47, 237–242. [Google Scholar] [CrossRef]
- Rutka, M.; Adamczyk, W.M.; Linek, P. Effects of Physical Therapist Intervention on Pulmonary Function in Children with Cerebral Palsy: A Systematic Review and Meta-Analysis. Phys. Ther. 2021, 101, pzab129. [Google Scholar] [CrossRef] [PubMed]
- Diwan, S.; Bansal, A.; Chovatiya, H.; Kotak, D.; Vyas, N. Effect of anterior chest wall myofascial release on thoracic expansion in children with spastic cerebral palsy. Int. J. Contemp. Pediatr. 2014, 1, 1. [Google Scholar] [CrossRef]
- Sato, H. Postural deformity in children with cerebral palsy: Why it occurs and how is it managed. Phys. Ther. Res. 2020, 23, 8–14. [Google Scholar] [CrossRef]
- Obermeyer, R.J.; Goretsky, M.J. Chest Wall Deformities in Pediatric Surgery. Surg. Clin. N. Am. 2012, 92, 669–684. [Google Scholar] [CrossRef]
- Chin, E.M.; Gwynn, H.E.; Robinson, S.; Hoon, A.H. Principles of Medical and Surgical Treatment of Cerebral Palsy. Neurol. Clin. 2020, 38, 397–416. [Google Scholar] [CrossRef]
- van Bommel, E.E.H.; Arts, M.M.E.; Jongerius, P.H.; Ratter, J.; Rameckers, E.A. Physical therapy treatment in children with cerebral palsy after single-event multilevel surgery: A qualitative systematic review. A first step towards a clinical guideline for physical therapy after single-event multilevel surgery. Ther. Adv. Chronic Dis. 2019, 10, 2040622319854241. [Google Scholar] [CrossRef]
- Yalcinkaya, E.Y.; Caglar, N.S.; Tugcu, B.; Tonbaklar, A. Rehabilitation Outcomes of Children with Cerebral Palsy. J. Phys. Ther. Sci. 2014, 26, 285–289. [Google Scholar] [CrossRef]
- Nuss, D.; Kelly, R.E.; Croitoru, D.P.; Katz, M.E. A 10-Year Review of a Minimally Invasive Technique for the Correction of Pectus Excavatum. J. Pediatr. Surg. 1998, 33, 545–552. [Google Scholar] [CrossRef] [PubMed]
- Bludevich, B.M.; Kauffman, J.D.; Litz, C.N.; Farach, S.M.; DeRosa, J.C.; Wharton, K.; Potthast, K.; Danielson, P.D.; Snyder, C.W.; Chandler, N.M. External caliper-based measurements of the modified percent depth as an alternative to cross-sectional imaging for assessing the severity of pectus excavatum. J. Pediatr. Surg. 2020, 55, 1058–1064. [Google Scholar] [CrossRef] [PubMed]
- Memetoğlu, Ö.İ.; Bütün, B.; Sezer, İ. Chest expansion and modified schober measurement values in a healthy, adult population. Arch. Rheumatol. 2016, 31, 145–150. [Google Scholar] [CrossRef]
- Semionov, A.; Kosiuk, J.; Ajlan, A.; Discepola, F. Imaging of thoracic wall abnormalities. Korean J. Radiol. 2019, 20, 1441–1453. [Google Scholar] [CrossRef]
- Peter, S.D.S.; Juang, D.; Garey, C.L.; Laituri, C.A.; Ostlie, D.J.; Sharp, R.J.; Snyder, C.L. A novel measure for pectus excavatum: The correction index. J. Pediatr. Surg. 2011, 46, 2270–2273. [Google Scholar] [CrossRef]
- Sujka, J.A.; Peter, S.D.S. Quantification of pectus excavatum: Anatomic indices. Semin. Pediatr. Surg. 2018, 27, 122–126. [Google Scholar] [CrossRef] [PubMed]
- Lain, A.; Garcia, L.; Gine, C.; Tiffet, O.; Lopez, M. New methods for imaging evaluation of chest wall deformities. Front. Pediatr. 2017, 5, 257. [Google Scholar] [CrossRef]
- Ando, T.; Kawamura, K.; Fujitani, J.; Koike, T.; Nishigaki, Y.; Mizuguchi, H.; Fujimoto, M.; Fujie, M.G. Biofeedback effect of thoracic excursion in chest expansion training. J. Biomech. Sci. Eng. 2012, 7, 328–334. [Google Scholar] [CrossRef]
- Ersöz, M.; Selçuk, B.; Gündüz, R.; Kurtaran, A.; Akyüz, M. Decreased chest mobility in children with spastic cerebral palsy. Turk. J. Pediatr. 2006, 48, 344–350. [Google Scholar]
- Nam, K.S.; Lee, H.Y. Differences of Chest and Waist Circumferences in Spastic Diplegic and Hemiplegic Cerebral Palsy. J. Korean Soc. Phys. Ther. 2013, 25, 155–159. [Google Scholar]
- Reddy, R.S.; Alahmari, K.A.; Silvian, P.S.; Ahmad, I.A.; Kakarparthi, V.N.; Rengaramanujam, K. Reliability of chest wall mobility and its correlation with lung functions in healthy nonsmokers, healthy smokers, and patients with COPD. Can. Respir. J. 2019, 2019, 5175949. [Google Scholar] [CrossRef]
- Pena, M.-G.; Jordan, M.; Gorgon, E. Reliability of Breathing Rate Assessment and Chest Expansion Measurement: A Pilot Study in Typically Developing Children. Internet J. Allied Heal. Sci. Pr. 2015, 13, 5. [Google Scholar] [CrossRef]
- Debouche, S.; Pitance, L.; Robert, A.; Liistro, G.; Reychler, G. Reliability and Reproducibility of Chest Wall Expansion Measurement in Young Healthy Adults. J. Manip. Physiol. Ther. 2016, 39, 443–449. [Google Scholar] [CrossRef] [PubMed]
- Leino, K.; Nunes, S.; Valta, P.; Takala, J. Validation of a new respiratory inductive plethysmograph. Acta Anaesthesiol. Scand. 2001, 45, 104–111. [Google Scholar] [CrossRef]
- Tawa, H.; Yonezawa, Y.; Maki, H.; Ogawa, H.; Ninomiya, I.; Sada, K.; Hamada, S.; Caldwell, W.M. A Wireless Breathing-Training Support System for Kinesitherapy. In Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 3–6 September 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 5179–5182. [Google Scholar] [CrossRef]
- Merritt, C.R.; Nagle, H.T.; Grant, E. Textile-based capacitive sensors for respiration monitoring. IEEE Sens. J. 2009, 9, 71–78. [Google Scholar] [CrossRef]
- Nishigaki, Y.; Mizuguchi, H.; Takeda, E.; Koike, T.; Ando, T.; Kawamura, K.; Shimbo, T.; Ishikawa, H.; Fujimoto, M.; Saotome, I.; et al. Development of new measurement system of thoracic excursion with biofeedback: Reliability and validity. J. Neuroeng. Rehabil. 2013, 10, 45. [Google Scholar] [CrossRef]
- Arthittayapiwat, K.; Pirompol, P.; Samanpiboon, P. Chest expansion measurement in 3-dimension by using accelerometers. Eng. J. 2019, 23, 71–84. [Google Scholar] [CrossRef]
- Hübner, P.; Clintworth, K.; Liu, Q.; Weinmann, M.; Wursthorn, S. Evaluation of hololens tracking and depth sensing for indoor mapping applications. Sensors 2019, 20, 1021. [Google Scholar] [CrossRef]
- Elaraby, A.F.; Hamdy, A.; Rehan, M. A Kinect-Based 3D Object Detection and Recognition System with Enhanced Depth Estimation Algorithm. In Proceedings of the 2018 IEEE 9th Annual Information Technology, 2018 Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 1–3 November 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 247–252. [Google Scholar] [CrossRef]
- Jalal, A.; Kamal, S.; Kim, D. Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments. J. Comput. Netw. Commun. 2016, 2016, 8087545. [Google Scholar] [CrossRef]
- Bartol, K.; Bojanic, D.; Petkovic, T.; Pribanic, T. A Review of Body Measurement Using 3D Scanning. IEEE Access 2021, 9, 67281–67301. [Google Scholar] [CrossRef]
- Sun, C.; Li, W.; Chen, C.; Wang, Z.; Chen, W. An unobtrusive and non-contact method for respiratory measurement with respiratory region detecting algorithm based on depth images. IEEE Access 2019, 7, 8300–8315. [Google Scholar] [CrossRef]
- Wheat, J.S.; Choppin, S.; Goyal, A. Development and assessment of a Microsoft Kinect based system for imaging the breast in three dimensions. Med. Eng. Phys. 2014, 36, 732–738. [Google Scholar] [CrossRef] [PubMed]
- Roy, G.; Bhuiya, A.; Mukherjee, A.; Bhaumik, S. Kinect Camera Based Gait Data Recording and Analysis for Assistive Robotics-An Alternative to Goniometer Based Measurement Technique. Procedia Comput. Sci. 2018, 133, 763–771. [Google Scholar] [CrossRef]
- Junior, H.O.; Lopes, D.R.; de Castro, J.F.; Ramos, P.d.S.; de Oliveira, A.G.F.; Lopes, A.J. Applicability of the kinect sensor in the rehabilitation of balance control in the elderly: A pilot study. Asian J. Sports Med. 2018, 9, e82017. [Google Scholar] [CrossRef]
- Samad, R.; Abu Bakar, M.Z.; Pebrianti, D.; Mustafa, M.; Abdullah, N.R.H. Elbow flexion and extension rehabilitation exercise system using marker-less kinect-based method. Int. J. Electr. Comput. Eng. 2017, 7, 1602–1610. [Google Scholar] [CrossRef]
- Hernández, Ó.; Morell, V.; Ramon, J.L.; Jara, C.A. Human pose detection for robotic-assisted and rehabilitation environments. Appl. Sci. 2021, 11, 4183. [Google Scholar] [CrossRef]
- Dubois, A.; Charpillet, F. A Gait Analysis Method Based on a Depth Camera for Fall Prevention. In Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 4515–4518. [Google Scholar] [CrossRef]
- Ye, M.; Yang, C.; Stankovic, V.; Stankovic, L.; Kerr, A. A Depth Camera Motion Analysis Framework for Tele-rehabilitation: Motion Capture and Person-Centric Kinematics Analysis. IEEE J. Sel. Top. Signal Process. 2016, 10, 877–887. [Google Scholar] [CrossRef]
- Giancola, S.; Valenti, M.; Sala, R. A Survey on 3D Cameras: Metrological Comparison of Time-of-Flight, Structured-Light and Active Stereoscopy Technologies; Springer Briefs in Computer Science; Springer: Cham, Switzerland, 2018. [Google Scholar] [CrossRef]
- Zarrabi, N.; Fesharakifard, R.; Menhaj, M.B. Robot localization performance using different SLAM approaches in a homogeneous indoor environment. In Proceedings of the ICRoM 2019—7th International Conference on Robotics and Mechatronics, Tehran, Iran, 20–22 November 2019; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2019; pp. 338–344. [Google Scholar] [CrossRef]
- Neri, P.; Paoli, A.; Aruanno, B.; Barone, S.; Tamburrino, F.; Razionale, A.V. 3D scanning of Upper Limb anatomy by a depth-camera-based system. Int. J. Interact. Des. Manuf. 2023. [Google Scholar] [CrossRef]
- Intel® RealSense TM Product Family D400 Series Datasheet. 2023. Available online: https://www.intelrealsense.com/wp-content/uploads/2023/10/Intel-RealSense-D400-Series-Datasheet-September-2023.pdf (accessed on 10 June 2024).
- MathWorks—Makers of MATLAB and Simulink—MATLAB & Simulink. Available online: https://www.mathworks.com/ (accessed on 10 June 2024).
- Intel RealSense SDK 2.0. Available online: https://www.intelrealsense.com/sdk-2/ (accessed on 10 June 2024).
- LoMauro, A.; Colli, A.; Colombo, L.; Aliverti, A. Aliverti. Breathing patterns recognition: A functional data analysis approach. Comput. Methods Programs Biomed. 2022, 217, 106670. [Google Scholar] [CrossRef]
- Grossmann, A.; Morlet, J. Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape. SIAM J. Math. Anal. 1984, 15, 723–736. [Google Scholar] [CrossRef]
- Morlet, J.; Arens, G.; Fourgeau, E.; Glard, D. Wave propagation and sampling theory-Part I: Complex signal and scattering in multilayered media. Geophysics 1982, 47, 203–221. [Google Scholar] [CrossRef]
- BenSaïda, A. Shapiro-Wilk and Shapiro-Francia Normality Tests Version 1.1.0.0 (MATLAB Central File Exchange). Available online: https://www.mathworks.com/matlabcentral/fileexchange/13964-shapiro-wilk-and-shapiro-francia-normality-tests (accessed on 10 June 2024).
Exp. Phase (Sess. 1) | j = 1 | j = 2 | j = 3 | j = 4 | j = 5 | j = 6 | j = 7 |
CS1 | 0.99 (0.51) | 0.82 (0.41) | 0.73 (0.33) | 0.74 (0.33) | 0.74 (0.33) | 0.82 (0.34) | 1.1 (0.34) |
CS2 | 0.71 (0.32) | 0.55 (0.24) | 0.56 (0.35) | 0.61 (0.34) | 0.52 (0.23) | 0.63 (0.24) | 0.86 (0.3) |
CS3 | 0.71 (0.34) | 0.7 (0.28) | 0.98 (0.46) | 1.11 (0.64) | 0.99 (0.64) | 0.82 (0.4) | 0.9 (0.27) |
Exp. Phase (Sess. 2) | j = 1 | j = 2 | j = 3 | j = 4 | j = 5 | j = 6 | j = 7 |
CS1 | 0.82 (0.29) | 0.68 (0.17) | 0.61 (0.17) | 0.51 (0.19) | 0.6 (0.28) | 0.7 (0.28) | 0.96 (0.34) |
CS2 | 0.76 (0.13) | 0.46 (0.1) | 0.36 (0.15) | 0.4 (0.2) | 0.4 (0.19) | 0.5 (0.13) | 0.8 (0.23) |
CS3 | 0.72 (0.21) | 0.64 (0.22) | 0.8 (0.35) | 0.9 (0.42) | 0.81 (0.37) | 0.67 (0.21) | 0.85 (0.33) |
Insp. Phase (Sess. 1) | j = 1 | j = 2 | j = 3 | j = 4 | j = 5 | j = 6 | j = 7 |
CS1 | 1 (0.51) | 0.86 (0.35) | 0.75 (0.3) | 0.68 (0.34) | 0.71 (0.32) | 0.79 (0.31) | 1.08 (0.25) |
CS2 | 0.7 (0.25) | 0.54 (0.24) | 0.56 (0.33) | 0.65 (0.41) | 0.61 (0.32) | 0.63 (0.22) | 0.91 (0.35) |
CS3 | 0.84 (0.35) | 0.77 (0.37) | 0.92 (0.53) | 1.06 (0.55) | 1.04 (0.52) | 0.84 (0.41) | 0.98 (0.31) |
Insp. Phase (Sess. 2) | j = 1 | j = 2 | j = 3 | j = 4 | j = 5 | j = 6 | j = 7 |
CS1 | 0.95 (0.29) | 0.77 (0.2) | 0.67 (0.19) | 0.6 (0.21) | 0.63 (0.23) | 0.73 (0.25) | 0.93 (0.31) |
CS2 | 0.76 (0.17) | 0.44 (0.11) | 0.36 (0.16) | 0.4 (0.1) | 0.45 (0.18) | 0.52 (0.14) | 0.78 (0.19) |
CS3 | 0.73 (0.2) | 0.77 (0.23) | 1.03 (0.26) | 1.17 (0.27) | 1.04 (0.28) | 0.82 (0.2) | 0.85 (0.25) |
Exp. Phase (Session 1) | Exp. Phase (Session 2) | Insp. Phase (Session 1) | Insp. Phase (Session 2) | |
---|---|---|---|---|
CS1 | 0.74 (0.35) | 0.55 (0.19) | 0.73 (0.32) | 0.62 (0.21) |
CS2 | 0.52 (0.25) | 0.34 (0.13) | 0.54 (0.27) | 0.34 (0.11) |
CS3 | 0.77 (0.41) | 0.61 (0.29) | 0.81 (0.44) | 0.83 (0.21) |
CS1 | t(9) = 1.66, p = 0.13 |
CS2 | t(9) = −0.46, p = 0.66 |
CS3 | t(9) = 0.10, p = 0.92 |
CS1 | t(9) = 1.68, p = 0.13 |
CS2 | t(9) = −0.47, p = 0.65 |
CS3 | W = 30, p = 0.85 |
Inspiration CSs | Expiration CSs | |
---|---|---|
CS1 | t(9) = 1.64, p = 0.14 | t(9) = 1.32, p = 0.22 |
CS2 | t(9) = 1.74, p = 0.12 | t(9) = 1.88, p = 0.09 |
CS3 | t(9) = 1.05, p = 0.32 | t(9) = 1.03, p = 0.33 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tomašević, O.; Ivančić, A.; Mejić, L.; Lužanin, Z.; Jorgovanović, N. Depth-Sensing-Based Algorithm for Chest Morphology Assessment in Children with Cerebral Palsy. Sensors 2024, 24, 5575. https://doi.org/10.3390/s24175575
Tomašević O, Ivančić A, Mejić L, Lužanin Z, Jorgovanović N. Depth-Sensing-Based Algorithm for Chest Morphology Assessment in Children with Cerebral Palsy. Sensors. 2024; 24(17):5575. https://doi.org/10.3390/s24175575
Chicago/Turabian StyleTomašević, Olivera, Aleksandra Ivančić, Luka Mejić, Zorana Lužanin, and Nikola Jorgovanović. 2024. "Depth-Sensing-Based Algorithm for Chest Morphology Assessment in Children with Cerebral Palsy" Sensors 24, no. 17: 5575. https://doi.org/10.3390/s24175575
APA StyleTomašević, O., Ivančić, A., Mejić, L., Lužanin, Z., & Jorgovanović, N. (2024). Depth-Sensing-Based Algorithm for Chest Morphology Assessment in Children with Cerebral Palsy. Sensors, 24(17), 5575. https://doi.org/10.3390/s24175575