Can We Quantify Aging-Associated Postural Changes Using Photogrammetry? A Systematic Review

Sensors (Basel). 2022 Sep 2;22(17):6640. doi: 10.3390/s22176640.

Abstract

Background: Aging is widely known to be associated with changes in standing posture. Recent advancements in the field of computerized image processing have allowed for improved analyses of several health conditions using photographs. However, photogrammetry's potential for assessing aging-associated postural changes is yet unclear. Thus, the aim of this review is to evaluate the potential of photogrammetry in quantifying age-related postural changes.

Materials and methods: We searched the databases PubMed Central, Scopus, Embase, and SciELO from the beginning of records to March 2021. Inclusion criteria were: (a) participants were older adults aged ≥60; (b) standing posture was assessed by photogrammetric means. PRISMA guidelines were followed. We used the Newcastle-Ottawa Scale to assess methodological quality.

Results: Of 946 articles reviewed, after screening and the removal of duplicates, 11 reports were found eligible for full-text assessment, of which 5 full studies met the inclusion criteria. Significant changes occurring with aging included deepening of thoracic kyphosis, flattening of lumbar lordosis, and increased sagittal inclination.

Conclusions: These changes agree with commonly described aging-related postural changes. However, detailed quantification of these changes was not found; the photogrammetrical methods used were often unvalidated and did not adhere to known protocols. These methodological difficulties call for further studies using validated photogrammetrical methods and improved research methodologies.

Keywords: RGBD sensors; aging; aging-associated changes; image processing; photogrammetry; posture.

Publication types

  • Review
  • Systematic Review

MeSH terms

  • Aged
  • Aging
  • Humans
  • Kyphosis*
  • Lordosis*
  • Photogrammetry / methods
  • Posture

Grants and funding

This research received no external funding.