The study of human locomotion, known as gait analysis, has for a long time been performed withexpensive equipment in laboratory settings. However, the emergence of machine learning sparkedinterest in integrating this technology in gait analysis, thus simplifying the process. This study’saim is to substitute the pressure insoles used during gait cycle analysis of a walking subject, with amachine learning model.To achieve this, a model based on Long-Short Term Memory networks that predicts vertical groundreaction force based on data from inertial measurement unit sensors was used. This serves as asubstitution for pressure insoles or pressure plates. The model was trained with time series datasetscontaining inertial measurement unit data and corresponding pressure insole data. Subsequently, itwas tested for intersubjective, out-of-sample data.The model was able to capture the periodicity of the gait cycle as well as predict the general shapeof the vertical ground reaction force curves, where the accuracy was quantified using normalisedroot mean squared error. The error was in a range between 17.8% and 13.4% and had an average of15.2%, when tested intersubjectively and out-of-sample. The most significant factor contributing tothe error was the model’s amplitude inaccuracies which was, most likely, due to information beinglost during the processing of the data, as well as simply having an insufficient amount of data.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-349665 |
| Date | January 2024 |
| Creators | Nilsson, Loke, Soric, Malte |
| Publisher | KTH, Skolan för teknikvetenskap (SCI) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-SCI-GRU ; 2024:204 |
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