Validation of a Biomechanical Injury and Disease Assessment Platform Applying an Inertial-Based Biosensor and Axis Vector Computation.

Inertial kinetics and kinematics have substantial influences on human biomechanical function. A new algorithm for Inertial Measurement Unit (IMU)-based motion tracking is presented in this work. The primary aims of this paper are to combine recent developments in improved biosensor technology with mainstream motion-tracking hardware to measure the overall performance of human movement based on joint axis-angle representations of limb rotation. This work describes an alternative approach to representing three-dimensional rotations using a normalized vector around which an identified joint angle defines the overall rotation, rather than a traditional Euler angle approach. Furthermore, IMUs allow for the direct measurement of joint angular velocities, offering the opportunity to increase the accuracy of instantaneous axis of rotation estimations. Although the axis-angle representation requires vector quotient algebra (quaternions) to define rotation, this approach may be preferred for many graphics, vision, and virtual reality software applications. The analytical method was validated with laboratory data gathered from an infant dummy leg's flexion and extension knee movements and applied to a living subject's upper limb movement. The results showed that the novel approach could reasonably handle a simple case and provide a detailed analysis of axis-angle migration. The described algorithm could play a notable role in the biomechanical analysis of human joints and offers a harbinger of IMU-based biosensors that may detect pathological patterns of joint disease and injury.

Ultrasonic wave propagation assessment of native cartilage explants and hydrogel scaffolds for tissue engineering.

Non-destructive techniques characterising the mechanical properties of cells, tissues, and biomaterials provide baseline metrics for tissue engineering design. Ultrasonic wave propagation and attenuation has previously demonstrated the dynamics of extracellular matrix synthesis in chondrocyte-seeded hydrogel constructs. In this paper, we describe an ultrasonic method to analyse two of the construct elements used to engineer articular cartilage in real-time, native cartilage explants and an agarose biomaterial. Results indicated a similarity in wave propagation velocity ranges for both longitudinal (1500-1745 m/s) and transverse (350-950 m/s) waveforms. Future work will apply an acoustoelastic analysis to distinguish between the fluid and solid properties including the cell and matrix biokinetics as a validation of previous mathematical models.

Biokinetic Mechanisms Linked With Musculoskeletal Health Disparities: Stochastic Models Applying Tikhonov's Theorem to Biomolecule Homeostasis.

Multiscale technology and advanced mathematical models have been developed to control and characterize physicochemical interactions, respectively, enhancing cellular and molecular engineering progress. Ongoing tissue engineering development studies have provided experimental input for biokinetic models examining the influence of static or dynamic mechanical stimuli (Saha, A. K., and Kohles, S. S., 2010, "A Distinct Catabolic to Anabolic Threshold Due to Single-Cell Nanomechanical Stimulation in a Cartilage Biokinetics Model," J. Nanotechnol. Eng. Med., 1(3) p. 031005; 2010, "Periodic Nanomechanical Stimulation in a Biokinetics Model Identifying Anabolic and Catabolic Pathways Associated With Cartilage Matrix Homeostasis," J. Nanotechnol. Eng. Med., 1(4), p. 041001). In the current study, molecular regulatory thresholds associated with specific disease disparities are further examined through applications of stochastic mechanical stimuli. The results indicate that chondrocyte bioregulation initiates the catabolic pathway as a secondary response to control anabolic processes. In addition, high magnitude loading produced as a result of stochastic input creates a destabilized balance in homeostasis. This latter modeled result may be reflective of an injurious state or disease progression. These mathematical constructs provide a framework for single-cell mechanotransduction and may characterize transitions between healthy and disease states.

Direct assessment of profilometric roughness variability from typical implant surface types.

PURPOSE: Protocols for quantifying the surface roughness of implants are varied and dependent upon the roughness parameter produced by the particular measurement device. The objective of this study was to examine the accuracy and precision of typical roughness characterization instruments used in the dental implant industry. MATERIALS AND METHODS: The average roughness (Ra) was measured using 2 common surface characterization instruments: an interferometer and a stylus profilometer. Titanium disks were prepared to represent 4 typical dental implant surfaces: machined, acid-etched, hydroxyapatite-coated, and titanium plasma-sprayed. Repeated measurements from multiple sites on each surface were undertaken to establish statistical inferences. Qualitative images of the surfaces were also acquired using a laser scanning confocal microscope. After surface measurements were conducted, the disks were diametrically cut and cross-sectional profiles were examined using a scanning electron microscope (SEM) as a comparative measure of surface topography. An analysis of variance was applied to isolate the effects of the measurement site, measurement sequence, surface treatment, and instrument type on Ra values. RESULTS: The results indicated that surface treatment (P = .0001) and instrument (P = .0001) strongly influenced Ra data. By design, measurement site (diametrical: P = .9859; area: P = .9824) and measurement sequence (P = .9990) did not influence roughness. In the assessment of individual instrument accuracy, the interferometer was the most accurate in predicting SEM-based roughness (P = .6688) compared with the stylus (P = .0839). As a measure of aggregate precision over all measurements, the most repeatable instrument was the stylus (coefficient of variation [CV] = 0.108), followed by the interferometer (CV = 0.125) and SEM (CV = 0.273). DISCUSSION: These results indicate dependencies in accuracy and precision related to the surface characterization technique. CONCLUSION: Instrument variability may obscure functional correlations between implant surface topography and osseointegration.