A New Method to Evaluate Pressure Distribution Using a 3D-Printed C2-C3 Cervical Spine Model with an Embedded Sensor Array.

Cervical degenerative disc diseases such as myelopathy and radiculopathy often require conventional treatments like artificial cervical disc replacement or anterior cervical discectomy and fusion (ACDF). When designing a medical device, like the stand-alone cage, there are many design inputs to consider. However, the precise biomechanics of the force between the vertebrae and implanted devices under certain conditions require further investigation. In this study, a new method was developed to evaluate the pressure between the vertebrae and implanted devices by embedding a sensor array into a 3D-printed C2-C3 cervical spine. The 3D-printed cervical spine model was subjected to a range of axial loads while under flexion, extension, bending and compression conditions. Cables were used for the application of a preload and a robotic arm was used to recreate the natural spine motions (flexion, extension, and bending). To verify and predict the total pressure between the vertebrae and the implanted devices, a 3D finite element (FE) numerical mathematical model was developed. A preload was represented by applying 22 N of force on each of the anterior tubercles for the C2 vertebra. The results of this study suggest that the sensor is useful in identifying static pressure. The pressure with the robot arm was verified from the FE results under all conditions. This study indicates that the sensor array has promising potential to reduce the trial and error with implants for various surgical procedures, including multi-level artificial cervical disk replacement and ACDF, which may help clinicians to reduce pain, suffering, and costly follow-up procedures.

Feeling the beat: a smart hand exoskeleton for learning to play musical instruments.

Individuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by 'feeling' the difference between correct and incorrect versions of the same song. The exoskeleton features piezoresistive sensor arrays with 16 taxels integrated into each fingertip. The hand exoskeleton was created as a single unit, with polyvinyl acid (PVA) used as a stent and later dissolved to construct the internal pressure chambers for the five individually actuated digits. Ten variations of a song were produced, one that was correct and nine containing rhythmic errors. To classify these song variations, Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) algorithms were trained with data from the 80 taxels combined from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the exoskeleton independently and while the exoskeleton was worn by a person. Results demonstrated that the ANN algorithm had the highest classification accuracy of 97.13% ± 2.00% with the human subject and 94.60% ± 1.26% without. These findings highlight the potential of the smart exoskeleton to aid disabled individuals in relearning dexterous tasks like playing musical instruments.

A Review of Finite Element Modeling for Anterior Cervical Discectomy and Fusion.

The cervical spine poses many complex challenges that require complex solutions. Anterior cervical discectomy and fusion (ACDF) has been one such technique often employed to address such issues. In order to address the problems with ACDF and assess the modifications that have been made to the technique over time, finite element analyses (FEA) have proven to be an effective tool. The variations of cervical spine FEA models that have been produced over the past couple of decades, particularly more recent representations of more complex geometries, have not yet been identified and characterized in any literature. Our objective was to present material property models and cervical spine models for various simulation purposes. The outlining and refinement of the FEA process will yield more reliable outcomes and provide a stable basis for the modeling protocols of the cervical spine.

Biomechanical Study of Cervical Endplate Removal on Subsidence and Migration in Multilevel Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: This study compares four cervical endplate removal procedures, validated by finite element models. PURPOSE: To characterize the effect of biomechanical strength and increased contact area on the maximum von Mises stress, migration, and subsidence between the cancellous bone, endplate, and implanted cage. OVERVIEW OF LITERATURE: Anterior cervical discectomy and fusion (ACDF) has been widely used for treating patients with degenerative spondylosis. However, no direct correlations have been drawn that incorporate the impact of the contact area between the cage and the vertebra/endplate. METHODS: Model 1 (M1) was an intact C2C6 model with a 0.5 mm endplate. In model 2 (M2), a cage was implanted after removal of the C4-C5 and C5-C6 discs with preservation of the osseous endplate. In model 3 (M3), 1 mm of the osseous endplate was removed at the upper endplate. Model 4 (M4) resembles M3, except that 3 mm of the osseous endplate was removed. RESULTS: The range of motion (ROM) at C2C6 in the M2-M4 models was reduced by at least 9º compared to the M1 model. The von Mises stress results in the C2C3 and C3C4 interbody discs were significantly smaller in the M1 model and slightly increased in the M2-M3 and M3-M4 models. Migration and subsidence decreased from the M2-M3 model, whereas further endplate removal increased the migration and subsidence as shown in the transition from M3 to M4. CONCLUSIONS: The M3 model had the least subsidence and migration. The ROM was higher in the M3 model than the M2 and M4 models. Endplate preparation created small stress differences in the healthy intervertebral discs above the ACDF site. A 1 mm embedding depth created the best balance of mechanical strength and contact area, resulting in the most favorable stability of the construct.

Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition.

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.