This study investigated the effect of adding strontium (Sr)-doped cobalt ferrite (CoFe2O4) nanoparticles in carbonyl iron particle (CIP)-based magnetorheological fluids (MRFs). Sr-CoFe2O4 nanoparticles were fabricated at different particle sizes using co-precipitation at calcination temperatures of 300 and 400 °C. Field emission scanning electron microscopy (FESEM) was used to evaluate the morphology of the Sr-CoFe2O4 nanoparticles, which were found to be spherical. The average grain sizes were 71-91 nm and 118-157 nm for nanoparticles that had been calcinated at 300 and 400 °C, respectively. As such, higher calcination temperatures were found to produce larger-sized Sr-CoFe2O4 nanoparticles. To investigate the rheological effects that Sr-CoFe2O4 nanoparticles have on CIP-based MRF, three MRF samples were prepared: (1) CIP-based MRF without nanoparticle additives (CIP-based MRF), (2) CIP-based MRF with Sr-CoFe2O4 nanoparticles calcinated at 300 °C (MRF CIP+Sr-CoFe2O4-T300), and (3) CIP-based MRF with Sr-CoFe2O4 nanoparticles calcinated at 400 °C (MRF CIP+Sr-CoFe2O4-T400). The rheological properties of these MRF samples were then observed at room temperature using a rheometer with a parallel plate at a gap of 1 mm. Dispersion stability tests were also performed to determine the sedimentation ratio of the three CIP-based MRF samples.
Parkinson's Disease (PD) is a neurodegenerative disorder that is often accompanied by slowness of movement (bradykinesia) or gradual reduction in the frequency and amplitude of repetitive movement (hypokinesia). There is currently no cure for PD, but early detection and treatment can slow down its progression and lead to better treatment outcomes. Vision-based approaches have been proposed for the early detection of PD using gait. Gait can be captured using appearance-based or model-based approaches. Although appearance-based gait contains comprehensive features, it is easily affected by factors such as dressing. On the other hand, model-based gait is robust against changes in dressing and external contours, but it is often too sparse to contain sufficient information. Therefore, we propose a fusion of appearance-based and model-based gait features for PD prediction. First, we extracted keypoint coordinates from gait captured in videos and modeled these keypoints as a point cloud. The silhouette images are also segmented from the videos to obtain an overall appearance representation of the subject. We then perform a binary classification of gait as normal or Parkinsonian using a novel fusion of the gait point cloud and silhouette features, obtaining AUC up to 0.87 and F1-Scores up to 0.82 (precision: 0.85, recall: 0.80).