Real-Time Back Surface Landmark Determination Using a Time-of-Flight Camera

Postural disorders, their prevention, and therapies are still growing modern problems. The currently used diagnostic methods are questionable due to the exposure to side effects (radiological methods) as well as being time-consuming and subjective (manual methods). Although the computer-aided diagnosis of posture disorders is well developed, there is still the need to improve existing solutions, search for new measurement methods, and create new algorithms for data processing. Based on point clouds from a Time-of-Flight camera, the presented method allows a non-contact, real-time detection of anatomical landmarks on the subject’s back and, thus, an objective determination of trunk surface metrics. Based on a comparison of the obtained results with the evaluation of three independent experts, the accuracy of the obtained results was confirmed. The average distance between the expert indications and method results for all landmarks was 27.73 mm. A direct comparison showed that the compared differences were statically significantly different; however, the effect was negligible. Compared with other automatic anatomical landmark detection methods, ours has a similar accuracy with the possibility of real-time analysis. The advantages of the presented method are non-invasiveness, non-contact, and the possibility of continuous observation, also during exercise. The proposed solution is another step in the general trend of objectivization in physiotherapeutic diagnostics.

[1]  Juan R. Rabuñal,et al.  Wearable Postural Control System for Low Back Pain Therapy , 2021, IEEE Transactions on Instrumentation and Measurement.

[2]  António H. J. Moreira,et al.  Evaluation of spinal posture using Microsoft Kinect™: A preliminary case‐study with 98 volunteers , 2017, Porto biomedical journal.

[3]  C. Ferguson An effect size primer: A guide for clinicians and researchers. , 2009 .

[4]  Alan Lewis,et al.  Development of a 3D scan posture-correction procedure to facilitate the direct-digital splinting approach , 2018, Virtual and Physical Prototyping.

[5]  Gail M. Sullivan,et al.  Using Effect Size-or Why the P Value Is Not Enough. , 2012, Journal of graduate medical education.

[6]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[7]  Julie A. Hides,et al.  Different Ways to Balance the Spine: Subtle Changes in Sagittal Spinal Curves Affect Regional Muscle Activity , 2009, Spine.

[8]  Radu Horaud,et al.  Time-of-Flight Cameras , 2012, SpringerBriefs in Computer Science.

[9]  Massimo Messina,et al.  Posture and posturology, anatomical and physiological profiles: overview and current state of art , 2017, Acta bio-medica : Atenei Parmensis.

[10]  Silviu Butnariu,et al.  Measurement and Geometric Modelling of Human Spine Posture for Medical Rehabilitation Purposes Using a Wearable Monitoring System Based on Inertial Sensors , 2016, Sensors.

[11]  C. Candotti,et al.  Photogrammetry as a tool for the postural evaluation of the spine: A systematic review. , 2016, World journal of orthopedics.

[12]  Masaaki Mochimaru,et al.  Errors in landmarking and the evaluation of the accuracy of traditional and 3D anthropometry. , 2011, Applied ergonomics.

[13]  D. Edwards,et al.  Normative data for human postural vertical: A systematic review and meta-analysis , 2018, PloS one.

[14]  Marta Danch-Wierzchowska,et al.  Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods , 2021, Sensors.

[15]  Dynamic spinal posture and pelvic position analysis using a rasterstereographic device , 2020, Journal of Orthopaedic Surgery and Research.

[16]  Vicente Romo-Perez,et al.  Validity and reliability of a tool for accelerometric assessment of static balance in women , 2017 .

[17]  S. Rothstock,et al.  Innovative decision support for scoliosis brace therapy based on statistical modelling of markerless 3D trunk surface data , 2020, Computer methods in biomechanics and biomedical engineering.

[18]  S. Simeão,et al.  [Prevalence of lower back pain and associated factors in students]. , 2011, Cadernos de saude publica.

[19]  Ariel Soares Teles,et al.  Mobile Applications for Assessing Human Posture: A Systematic Literature Review , 2020, Electronics.

[20]  Marta Danch-Wierzchowska,et al.  Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures , 2020, Sensors.

[21]  G. Kandasamy,et al.  Posture and Back Shape Measurement Tools: A Narrative Literature Review , 2020, Spinal Deformities in Adolescents, Adults and Older Adults.

[22]  Effectiveness of Chêneau brace treatment for idiopathic scoliosis: prospective study in 79 patients followed to skeletal maturity , 2011, Scoliosis.

[23]  K. Jarrod Millman,et al.  Array programming with NumPy , 2020, Nat..

[24]  M. Noll,et al.  Risk factors associated with structural postural changes in the spinal column of children and adolescents , 2015, Revista paulista de pediatria : orgao oficial da Sociedade de Pediatria de Sao Paulo.

[25]  Robert Sitnik,et al.  Algorithm for Detecting Characteristic Points on a Three-Dimensional, Whole-Body Human Scan , 2020 .

[26]  K. Grimmer-Somers,et al.  The quality of evidence of psychometric properties of three-dimensional spinal posture-measuring instruments , 2011, BMC musculoskeletal disorders.

[27]  Ewa Pietka,et al.  ToF-Data-Based Modelling of Skin Surface Deformation , 2016, ITIB.

[28]  Posture and Health: Are the Biomechanical Postural Evaluation and the Postural Evaluation Questionnaire Comparable to and Predictive of the Digitized Biometrics Examination? , 2021, International journal of environmental research and public health.

[29]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[30]  Vladlen Koltun,et al.  Open3D: A Modern Library for 3D Data Processing , 2018, ArXiv.

[31]  S. Paśko,et al.  Combining 3D Structured Light Imaging and Spine X-ray Data Improves Visualization of the Spinous Lines in the Scoliotic Spine , 2020, Applied Sciences.

[32]  P. Patias,et al.  A review of the trunk surface metrics used as Scoliosis and other deformities evaluation indices , 2010, Scoliosis.

[33]  Björn Krüger,et al.  Analyzing Spinal Shape Changes During Posture Training Using a Wearable Device , 2019, Italian National Conference on Sensors.

[34]  W. Crow,et al.  Estimating Cost of Care for Patients With Acute Low Back Pain: A Retrospective Review of Patient Records , 2009, The Journal of the American Osteopathic Association.