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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/25401
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dc.contributor.advisorDeen, M Jamal-
dc.contributor.authorFaisal, Abu Ilius-
dc.date.accessioned2020-04-24T15:00:24Z-
dc.date.available2020-04-24T15:00:24Z-
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/11375/25401-
dc.description.abstractThe loss of mobility among the elderly has become a significant health and socio-economic concern worldwide. Poor mobility due to gradual deterioration of the musculoskeletal system causes older adults to be more vulnerable to serious health risks such as joint injuries, bone fractures and traumatic brain injury. The costs associated with the treatment and management of these injuries are a huge financial/social burden on the government, society and family. Knee is one of the key joints that bear most of the body weight, so its proper function is essential for good mobility. Further, Continuous monitoring of the knee joint can potentially provide important quantitative information related to knee health and mobility that can be utilized for health assessment and early diagnoses of mobility-related problems. In this research work, we developed an easy-to-use, low-cost, multi-sensor-based wearable device to monitor and assess the knee joint and proposed an analysis system to characterize and classify an individual’s knee joint features with respect to the baseline characteristics of his/her peer group. The system is composed of a set of different miniaturized sensors (inertial motion, temperature, pressure and galvanic skin response) to obtain linear acceleration, angular velocity, skin temperature, muscle pressure and sweat rate of a knee joint during different daily activities. A database is constructed from 70 healthy adults in the age range from 18 to 86 years using the combination of all signals from our knee joint monitoring system. In order to extract relevant features from the datasets, we employed computationally efficient methods such as complementary filter and wavelet packet decomposition. Minimum redundancy maximum relevance algorithm and principal component analysis were used to select key features and reduce the dimension of the feature vectors. The obtained features were classified using the support vector machine, forming two distinct clusters in the baseline knee joint characteristics corresponding to gender, age, body mass index and knee/leg health condition. Thus, this simple, easy‐to‐use, cost-effective, non-invasive and unobtrusive knee monitoring system can be used for real-time evaluation and early diagnoses of joint disorders, fall detection, mobility monitoring and rehabilitation.en_US
dc.language.isoenen_US
dc.subjectKnee joint monitoring systemen_US
dc.subjectWearableen_US
dc.subjectMobilityen_US
dc.subjectSensor fusionen_US
dc.subjectInertial sensorsen_US
dc.subjectGait analysisen_US
dc.subjectFeature extractionen_US
dc.subjectSupport vector machineen_US
dc.titleDevelopment of a Low-Cost and Easy-to-Use Wearable Knee Joint Monitoring Systemen_US
dc.title.alternativeA Wearable Knee Joint Monitoring Systemen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreetypeThesisen_US
dc.description.degreeMaster of Applied Science (MASc)en_US
Appears in Collections:Open Access Dissertations and Theses

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