Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749845
Title: Estimates of load rates on the lower limb joints using smartphone accelerometers during physical activity
Author: Nazirizadeh, Susan
ISNI:       0000 0004 7234 2990
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2018
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Abstract:
Although the causes and pathology of the progression of osteoarthritis are not entirely understood, an active lifestyle avoiding excessive load on the joints can control symptoms of osteoarthritis (e.g. joint pain and stiffness). The aim of this thesis was to develop, validate, and test an algorithm for estimating impact loading through the lower limbs using wearables (smartphones and smartwatches). The viscoelastic nature of articular cartilage means it is susceptible to high load rates, hence, the mean load rate magnitude was estimated from accelerometer recordings of wearables and used as a surrogate for estimating impact loading on the lower limb joints. The validity of the mean load rate magnitude was assessed against the gold standard equipment, the force plate (R2 = 0.77). Further, the mean load rate magnitude was used as a feature in the classification of everyday activities with support vector machine classifiers with an accuracy of 80%. An app was then developed which monitored mean load rate magnitude using Markov chain Monte Carlo methods for testing the reliability of monitoring over a period of seven days. The accumulated mean load rate magnitude was used to estimate the error, smartphone = 2.66%, for seven-day recordings. Finally, a function to score pain was added to the final version of the app, termed OAppTM. A single case study assessed the ability of OAppTM to compare osteoarthritis-related pain to mean load rate magnitude with a low positive correlation of r = 0.38. To conclude, this thesis developed, assessed the validation, and tested a load rate magnitude algorithm, which estimated load rate on the lower limb joints with the accelerometer sensors of wearables. These results form the basis for further research to develop a clinical tool for monitoring load rate and supporting patients to maintain an active lifestyle by avoiding excessive load on their lower limb joints.
Supervisor: Stokes, Maria ; Forrester, Alexander ; Arden, Nigel Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.749845  DOI: Not available
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