Standing
Undergraduate
Type of Proposal
Visual Presentation (Poster, Installation, Demonstration)
Faculty
Faculty of Human Kinetics
Faculty Sponsor
Dr. David Andrews
Proposal
Accurate tissue mass estimates from living people are required to better model the response of the musculoskeletal system following impulsive impact events [1]. To date, prediction equations have been developed to estimate the soft and rigid tissue masses of all body segments from surface anthropometric measures and validated against actual tissue masses obtained from Dual-energy X-ray Absorptiometry scans (DXA) [2-5]. Good to excellent reliability has been reported for all anthropometric measures (lengths, circumferences, breadths, skinfolds) [6-7], and tissue masses obtained manually from the DXA scans [8-9]. Despite the accuracy and reliability of these equations, this process of obtaining tissue masses from living people is limited by: inconsistencies in variable names and measurement definitions between studies; susceptibility of errors when transferring written measurements to digital form, and; substantial time spent on an overly complex manual process. Therefore, the intention of the current study is to address the limitations of the current process used to estimate segment-specific soft and rigid tissue masses by developing a tool that incorporates existing equations into one location (with unified variable names and measurement definitions), automating, and linking the data recording and analysis processes. Existing tissue mass prediction equations will be compiled on a tablet device for the segments of the upper and lower extremities, as well as the head, neck, trunk and pelvis. A simple user-interface is being developed using MATLABâ(R2017a) to enable input of anthropometric measurements using an interactive human manikin. Measurement inputs will be linked to the prediction equations to streamline the analysis process. Additionally, variable names and measurement definitions from the different equations have been reconciled to increase usability. This tool will facilitate the prediction of soft and rigid tissue masses from anthropometric measurements by increasing data collection efficiency, reducing data transfer errors, and enabling large population studies of tissue masses for all body segments.
Grand Challenges
Viable, Healthy and Safe Communities
Special Considerations
References:
[1] Gruber K. et al. (1998). J. Biomech 31(5); p. 439-44.
[2] Holmes JD. et al. (2005). J. Appl Biomech 21; p. 371-382.
[3] Arthurs KL. et al. (2009). J. Biomech 42 (3); p. 389-394.
[4] Gyemi DL. et al. (2017). J. Appl Biomech 33; p.366-372.
[5] Kahelin C. (2016). [Unpublished Master’s thesis].
[6] Burkhart TA. et al. (2008). J. Biomech 41(7); p. 1604-1610.
[7] George NC. et al. (2017). J. Appl Biomech 33; p. 373-378.
[8] Burkhart TA. et al. (2009). J. Biomech 42; p. 1138-1142.
[9] Nilam Z. et al. (2017). Proceedings of the 14th Annual Ontario Biomechanics Conference.
Towards the Development of a Comprehensive Tissue Mass Prediction Tool for Living Men and Women
Accurate tissue mass estimates from living people are required to better model the response of the musculoskeletal system following impulsive impact events [1]. To date, prediction equations have been developed to estimate the soft and rigid tissue masses of all body segments from surface anthropometric measures and validated against actual tissue masses obtained from Dual-energy X-ray Absorptiometry scans (DXA) [2-5]. Good to excellent reliability has been reported for all anthropometric measures (lengths, circumferences, breadths, skinfolds) [6-7], and tissue masses obtained manually from the DXA scans [8-9]. Despite the accuracy and reliability of these equations, this process of obtaining tissue masses from living people is limited by: inconsistencies in variable names and measurement definitions between studies; susceptibility of errors when transferring written measurements to digital form, and; substantial time spent on an overly complex manual process. Therefore, the intention of the current study is to address the limitations of the current process used to estimate segment-specific soft and rigid tissue masses by developing a tool that incorporates existing equations into one location (with unified variable names and measurement definitions), automating, and linking the data recording and analysis processes. Existing tissue mass prediction equations will be compiled on a tablet device for the segments of the upper and lower extremities, as well as the head, neck, trunk and pelvis. A simple user-interface is being developed using MATLABâ(R2017a) to enable input of anthropometric measurements using an interactive human manikin. Measurement inputs will be linked to the prediction equations to streamline the analysis process. Additionally, variable names and measurement definitions from the different equations have been reconciled to increase usability. This tool will facilitate the prediction of soft and rigid tissue masses from anthropometric measurements by increasing data collection efficiency, reducing data transfer errors, and enabling large population studies of tissue masses for all body segments.