KEYWORDS: Principal component analysis, Control systems, Sensors, Kinematics, Nose, Feature extraction, Gyroscopes, Linear filtering, Data analysis, Neuroscience, Correlation function, Data mining, Classification systems
Neurological disorders typically exhibit movement disabilities and disorders such as cerebellar ataxia (CA) can cause coordination inaccuracies often manifested as disabilities associated with gait, balance and speech. Since the severity assessment of the disorder is based on the expert clinical opinion, it is likely to be subjective. Automated versions of two upper limb tests: Finger to Nose test (FNT) and Diadochokinesia (DDK) test are investigated in this paper. Inertial Measurement Units (IMU) (BioKinTM ) are employed to capture the disability by measuring limb movements. Translational and rotational accelerations considered as kinematic parameters provided the features relevant to characteristic movements intrinsic to the disability. Principal Component Analysis (PCA) and multi-class Linear Discriminant classifier (LDA) were instrumental in dominant features correlating with the clinical scores. The relationship between clinicians assessment and the objective analysis is examined using Pearson Correlation. This study found that although FNT predominantly consist of translational movements, rotation was the dominant feature while for the case of DDK that predominantly consist of rotational movements, acceleration was the dominant feature. The degree of correlation in each test was also enhanced by combining the features in different tests.
We are engineering cartilage from autologous nasal chondrocytes and a collagen scaffold in chondrogenic conditions to treat knee cartilage defects in an ongoing phase II clinical trial. To comply with regulatory requirements, we are developing quality controls to characterize and ensure the safety and quality of the engineered cartilage products. Our preliminary results show that we can measure the Raman spectra of engineered cartilage. Here we propose a standardized procedure for collecting and preprocessing the Raman spectral data. We currently have experienced, trained technicians manufacturing the engineered cartilage, but in the future, these grafts will be made by various labs, therefore ensuring the standardization of the manufacturing process is a challenge that could be addressed with Raman spectroscopy-based quality controls. In this manuscript we discuss how Raman spectroscopy-based quality controls could be incorporated into the Good Manufacturing Practice (GMP) compliant process for our engineered cartilage.
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