Biocompatibility and antibacterial properties of zirconium nitride coating on titanium abutments: An in vitro study

Improving soft tissue attachment and reducing bacterial colonization on titanium abutments are key factors for the long-term maintenance of healthy soft and hard peri-implant tissues. This in vitro study was conducted to compare the biocompatibility and antibacterial activity of four different surfaces: uncoated Ti6Al4V, anodized, and coated with titanium nitride or zirconium nitride. Surface topography was investigated with a high-resolution system for measuring surface finishes. Human gingival fibroblast (HGF) adhesion and proliferation were examined using MTT assay, Scanning Electron Microscopy (SEM) imaging, immunofluorescence analysis and real-time PCR for selected target genes. The hemolysis and AMES tests were performed to assess the chemical compounds' blood compatibility and mutagenic potential, respectively. Antibacterial activity was tested against five bacterial strains isolated from the oral cavity (Streptococcus salivarius, S. sanguinis, S. mutans, S. sobrinus, S. oralis), and the percentage of dead bacteria was calculated. Roughness measurements confirmed a substantial similarity between the surfaces and their compatibility with clinical applications. MTT assay, SEM analysis and immunofluorescence staining showed adhesion and proliferation of HGFs cultured on all the examined surfaces. PCR confirmed that HGFs produced extracellular matrix components efficiently on all the surfaces. No hemolytic activity was detected, and the AMES test confirmed the surfaces' clinical safety. For all tested bacterial strains, biofilms grown on the zirconium nitride surface showed a higher percentage of dead bacteria than on the other disks. The titanium nitride surface inactivated bacterial biofilms, too, but to a lesser extent

Adaptive Silhouette Extraction In Dynamic Environments Using Fuzzy Logic

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. In a previous paper, we addressed some of the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varying background model. A problem with our system is that by adapting the background model, objects moved by a human are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the human silhouette, such as in a quick arm movement. In this paper, we develop a fuzzy logic inference system to detach the silhouette of a moving object from the human body. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.The authors are grateful for the support from NSF ITR grant IIS-0428420 and the U.S. Administration on Aging, under grant 90AM3013

Real-time Assessment and Visual Feedback for Patient Rehabilitation Using Inertial Sensors

Rehabilitation exercises needs have been continuously increasing and have been projected to increase in future as well based on its demand for aging population, recovering from surgery, injury and illness and the living and working lifestyle of the people. This research aims to tackle one of the most critical issues faced by the exercise administers-Adherence or Non-Adherence to Home Exercise problems especially has been a significant issue resulting in extensive research on the psychological analysis of people involved. In this research, a solution is provided to increase the adherence of such programs through an automated real-time assessment with constant visual feedback providing a game like an environment and recording the same for analysis purposes. Inertial sensors like Accelerometer and Gyroscope has been used to implement a rule-based framework for human activity recognition for measuring the ankle joint angle. This system is also secure as it contains only the recordings of the data and the avatar that could be live fed or recorded for the treatment analysis purposes which could save time and cost. The results obtained after testing on four healthy human subjects shows that with proper implementation of rule parameters, good quality and quantity of the exercises can be assessed in real time

Human Motion Analysis with Wearable Inertial Sensors

High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson’s disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user’s itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system