The Force Floor: Design and Development of a Low-Cost 3D Force Sensing Area Which Utilises Machine Learning to Estimate 3D GRF and CoP from Single-Axis Loadcells
| dc.contributor.advisor | Patel, Amir | |
| dc.contributor.advisor | Albertus Yumna | |
| dc.contributor.author | Stickells, Devin | |
| dc.date.accessioned | 2023-07-30T07:55:21Z | |
| dc.date.available | 2023-07-30T07:55:21Z | |
| dc.date.issued | 2023 | |
| dc.date.updated | 2023-07-30T07:50:51Z | |
| dc.description.abstract | Along with motion capture tools, ground reaction force (GRF) sensors form the crux of objective biomechanical analysis. Advances in computer vision have significantly lowered the costs associated with 3D motion capture, but the same cannot be said of 3-axis force plates – the gold standard for GRF capture. If wholistic biomechanics analysis is to become more accessible, a more affordable method of 3D GRF measurement is needed. Single-axis loadcells are significantly cheaper than their 3-axis equivalents, though when axes are not mechanically isolated there is the possibility for crosstalk and the absorption of forces which cannot be measured, leading to a system that cannot be fully described analytically - and is possibly nonlinear in its behaviour. This research investigates the design and small-scale manufacture (to 20 units) of a low-cost force plate design that utilises a machine learning model to overcome these limitations and estimate 3D GRF and centre of pressure from a series of single-axis loadcells. A literature review was performed to understand and compare the relevant approaches to the core aspects of the project. An early proof of concept plate was built and tested along with a simple neural network to establish the feasibility of the idea. Following further investigation, it was discovered that the internal geometry of the plate played an integral role in its accuracy. To this end, the force plate was simulated, and an extensive hardware design process undertaken prior to the design of a full-scale prototype. It was subsequently hypothesised that the ease of repetition of the design could be aided by the development of an automated data creation rig, as well as the use of recently-developed machine learning techniques which reduce data dependency, such as Sim2Real transfer learning and physicsinformed residual networks. A data creation rig was built for purpose. Twenty prototype plates were built, with sixteen of them being interlinked to create the prototype Force Floor - a large force sensing area. The performance of a subset of these plates and their corresponding models was tested against an Advanced Mechanical Technology Inc. (AMTI) BMS6001200 force plate, with the best obtaining average measurement disagreements in the X-, Y- and Z-directions of 1.23, 1.08, and 1.11 percent of the full-scale force respectively (with full-scale deflections of 600 N, 600 N and 2000 N respectively). Analysis of the project's results was encouraging as far as the viability of this design and approach for use in the production of an affordable 3-axis force plate is concerned. | |
| dc.identifier.apacitation | Stickells, D. (2023). <i>The Force Floor: Design and Development of a Low-Cost 3D Force Sensing Area Which Utilises Machine Learning to Estimate 3D GRF and CoP from Single-Axis Loadcells</i>. (). ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/38180 | en_ZA |
| dc.identifier.chicagocitation | Stickells, Devin. <i>"The Force Floor: Design and Development of a Low-Cost 3D Force Sensing Area Which Utilises Machine Learning to Estimate 3D GRF and CoP from Single-Axis Loadcells."</i> ., ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2023. http://hdl.handle.net/11427/38180 | en_ZA |
| dc.identifier.citation | Stickells, D. 2023. The Force Floor: Design and Development of a Low-Cost 3D Force Sensing Area Which Utilises Machine Learning to Estimate 3D GRF and CoP from Single-Axis Loadcells. . ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/38180 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Stickells, Devin AB - Along with motion capture tools, ground reaction force (GRF) sensors form the crux of objective biomechanical analysis. Advances in computer vision have significantly lowered the costs associated with 3D motion capture, but the same cannot be said of 3-axis force plates – the gold standard for GRF capture. If wholistic biomechanics analysis is to become more accessible, a more affordable method of 3D GRF measurement is needed. Single-axis loadcells are significantly cheaper than their 3-axis equivalents, though when axes are not mechanically isolated there is the possibility for crosstalk and the absorption of forces which cannot be measured, leading to a system that cannot be fully described analytically - and is possibly nonlinear in its behaviour. This research investigates the design and small-scale manufacture (to 20 units) of a low-cost force plate design that utilises a machine learning model to overcome these limitations and estimate 3D GRF and centre of pressure from a series of single-axis loadcells. A literature review was performed to understand and compare the relevant approaches to the core aspects of the project. An early proof of concept plate was built and tested along with a simple neural network to establish the feasibility of the idea. Following further investigation, it was discovered that the internal geometry of the plate played an integral role in its accuracy. To this end, the force plate was simulated, and an extensive hardware design process undertaken prior to the design of a full-scale prototype. It was subsequently hypothesised that the ease of repetition of the design could be aided by the development of an automated data creation rig, as well as the use of recently-developed machine learning techniques which reduce data dependency, such as Sim2Real transfer learning and physicsinformed residual networks. A data creation rig was built for purpose. Twenty prototype plates were built, with sixteen of them being interlinked to create the prototype Force Floor - a large force sensing area. The performance of a subset of these plates and their corresponding models was tested against an Advanced Mechanical Technology Inc. (AMTI) BMS6001200 force plate, with the best obtaining average measurement disagreements in the X-, Y- and Z-directions of 1.23, 1.08, and 1.11 percent of the full-scale force respectively (with full-scale deflections of 600 N, 600 N and 2000 N respectively). Analysis of the project's results was encouraging as far as the viability of this design and approach for use in the production of an affordable 3-axis force plate is concerned. DA - 2023_ DB - OpenUCT DP - University of Cape Town KW - Engineering LK - https://open.uct.ac.za PY - 2023 T1 - The Force Floor: Design and Development of a Low-Cost 3D Force Sensing Area Which Utilises Machine Learning to Estimate 3D GRF and CoP from Single-Axis Loadcells TI - The Force Floor: Design and Development of a Low-Cost 3D Force Sensing Area Which Utilises Machine Learning to Estimate 3D GRF and CoP from Single-Axis Loadcells UR - http://hdl.handle.net/11427/38180 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/38180 | |
| dc.identifier.vancouvercitation | Stickells D. The Force Floor: Design and Development of a Low-Cost 3D Force Sensing Area Which Utilises Machine Learning to Estimate 3D GRF and CoP from Single-Axis Loadcells. []. ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/38180 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.subject | Engineering | |
| dc.title | The Force Floor: Design and Development of a Low-Cost 3D Force Sensing Area Which Utilises Machine Learning to Estimate 3D GRF and CoP from Single-Axis Loadcells | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |