Senior Subject Gait Analysis Using Self-supervised Method

There are many issues regarding the technology used for fall detection gait analysis in the geriatric center of senior patients. The fall detection system used should examine the privacy of the patients and the flexibility of the system. Several researchers have developed fall detection using wearable sensors due to their flexibility and nature of privacy. Most of those developed methods are supervised deep learning methods. However, data annotation is expensive because we use camera video recording and playback of each participant’s recorded video to label the data. Moreover, labelling using a camera recording limits the flexible and private nature of wearable sensor-based fall detection. This paper presents how to use unlabelled data to pre-train our models and use labelled data to fine-tune those pre-trained weights. We collected unlabelled and labelled data and applied self-supervised learning to detect falls. First, we performed pre-training on the unlabeled data using ResNet model. After that, fine-tune and train ResNet using the labelled dataset. The experiment in this study suggested that the best performance can be achieved by using pre-trained weights of unlabelled data from the accelerometer and gyroscope sensors. Furthermore, oversampling and modified loss functions are used to handle the dataset’s imbalance classes. With the ResNet pre-trained weights and re-training using the labelled data, the experiments achieved an F1-Score of 0.98

Surgical GPS Proof of Concept for Scoliosis Surgery

Scoliotic deformities may be addressed with either anterior or posterior approaches for scoliosis correction procedures. While typically quite invasive, the impact of these operations may be reduced through the use of computer-assisted surgery. A combination of physician-designated anatomical landmarks and surgical ontologies allows for real-time intraoperative guidance during computer-assisted surgical interventions. Predetermined landmarks are labeled on an identical patient model, which seeks to encompass vertebrae, intervertebral disks, ligaments, and other soft tissues. The inclusion of this anatomy permits the consideration of hypothetical forces that are previously not well characterized in a patient-specific manner. Updated ontologies then suggest procedural directions throughout the surgical corridor, observing the positioning of both the physician and the anatomical landmarks of interest at the present moment. Merging patient-specific models, physician-designated landmarks, and ontologies to produce real-time recommendations magnifies the successful outcome of scoliosis correction through enhanced pre-surgical planning, reduced invasiveness, and shorted recovery time

Special Issue on Medical Simulation

We would like to welcome you to this Special Issue on Medical Simulation, the first of its kind not only for SIMULATION: Transactions of The Society for Modeling and Simulation International, but for any technical journal. Our respective backgrounds are an indication of the technical and clinical breadth of medical simulation, as we approach the subject as primarily medical image analysis and biomechanics experts respectively, each with a variety of clinical interests spanning virtual reality (VR)–based neuro-, orthopedic and ear-nose-and-throat surgery. Moreover, we believe that the breadth of the papers that comprise this issue reflects an even broader perspective. After all, medical simulation can be seen as encompassing mannequin-based training, as well as nonsurgical areas such as pharmacological and physiological modeling, the latter of which is increasingly multi-scale and integrative

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has an imbalanced nature. Moreover, we designed a deep learning model that combines a convolution-based feature extractor and deep neural network blocks, the LSTM block, and the transformer encoder block, followed by a position-wise feedforward layer. We found that combining the input sequence with the convolution-learned features of different kernels tends to increase the performance of the fall-detection model. Last, we analyzed that the sensor signals collected by both accelerometer and gyroscope sensors can be leveraged to develop an effective classifier that can accurately detect falls, especially differentiating falls from near-falls. Furthermore, we also used data from sixteen different body parts and compared them to determine the better sensor position for fall-detection methods. We found that the shank is the optimal position for placing our sensors, with an F1 score of 0.97, and this could help other researchers collect high-quality fall datasets

Automatic Generation and Novel Validation of Patient-Specific, Anatomically Inclusive Scoliosis Models for Biomechanics-Informed Surgical Planning

Scoliosis is an abnormal spinal curvature of greater than 10 degrees. Severe scoliotic deformities are addressed with highly invasive procedures: anterior or posterior spinal fusion approaches. This invasiveness is due, in part, to the constraints of current surgical planning, which utilizes computed tomography (CT) scans unable to discern spinal ligaments that are dissected to make the spine sufficiently compliant for correction. If localization of ligaments and soft tissues were achieved pre-operatively, corrective procedures could become safer and more efficient by using finite element (FE) biomechanical simulations to determine decreased incidences of ligament releases. This research aims to achieve ligament localization within CT scans by deforming computer-aided design (CAD) meshes that encompass vertebrae, intervertebral discs, ligaments, and other soft tissues to emulate patient-specific anatomy. Models are generated through deformable surface algorithms that elastically fit CAD meshes onto segmentations of conspicuous structures. Surrounding soft tissues are locally warped to reconstruct contextually appropriate positions before the CAD mesh is tetrahedralized to support finite element studies. The methods presented use convolutional neural networks (CNNs) that segment vertebrae from CT images to improve initial deformation alignment. In instances of CNN failure, methodological robustness, given an accurate segmentation, is demonstrated through the use of spinal columns which have been molded into a Lenke classification. Dice coefficient and Hausdorff distance metrics demonstrate the accuracy of the deformable model generation. Synthetically generated images are used for additional validation of soft tissue positioning. Quantitative results are highly competitive and qualitative interpretations suggest a strong level of accuracy and appropriate deformation. Soft tissue ground truths, present in synthetic data, provide further confirmation of accurate mesh generation. Following the completion of the methodological pipeline, accurate, patient-specific, anatomically inclusive models are ready for use in FE studies.https://digitalcommons.odu.edu/gradposters2021_engineering/1005/thumbnail.jp

Watertight and 2-Manifold Surface Meshes Using Dual Contouring With Tetrahedral Decomposition of Grid Cubes

The Dual Contouring algorithm (DC) is a grid-based process used to generate surface meshes from volumetric data. The advantage of DC is that it can reproduce sharp features by inserting vertices anywhere inside the grid cube, as opposed to the Marching Cubes (MC) algorithm that can insert vertices only on the grid edges. However, DC is unable to guarantee 2-manifold and watertight meshes due to the fact that it produces only one vertex for each grid cube. We present a modified Dual Contouring algorithm that is capable of overcoming this limitation. Our method decomposes an ambiguous grid cube into a maximum of twelve tetrahedral cells; we introduce novel polygon generation rules that produce 2-manifold and watertight surface meshes. We have applied our proposed method on realistic data, and a comparison of the results of our proposed method with results from traditional DC shows the effectiveness of our method

Controlling the Error on Target Motion through Real-time Mesh Adaptation: Applications to Deep Brain Stimulation

We present an error-controlled mesh refinement procedure for needle insertion simulation and apply it to the simulation of electrode implantation for deep brain stimulation, including brain shift. Our approach enables to control the error in the computation of the displacement and stress fields around the needle tip and needle shaft by suitably refining the mesh, whilst maintaining a coarser mesh in other parts of the domain. We demonstrate through academic and practical examples that our approach increases the accuracy of the displacement and stress fields around the needle without increasing the computational expense. This enables real-time simulations. The proposed methodology has direct implications to increase the accuracy and control the computational expense of the simulation of percutaneous procedures such as biopsy, brachytherapy, regional anesthesia, or cryotherapy and can be essential to the development of robotic guidance.Comment: 21 pages, 14 figure

Robotically Steered Needles: A Survey of Neurosurgical Applications and Technical Innovations

This paper surveys both the clinical applications and main technical innovations related to steered needles, with an emphasis on neurosurgery. Technical innovations generally center on curvilinear robots that can adopt a complex path that circumvents critical structures and eloquent brain tissue. These advances include several needle-steering approaches, which consist of tip-based, lengthwise, base motion-driven, and tissue-centered steering strategies. This paper also describes foundational mathematical models for steering, where potential fields, nonholonomic bicycle-like models, spring models, and stochastic approaches are cited. In addition, practical path planning systems are also addressed, where we cite uncertainty modeling in path planning, intraoperative soft tissue shift estimation through imaging scans acquired during the procedure, and simulation-based prediction. Neurosurgical scenarios tend to emphasize straight needles so far, and span deep-brain stimulation (DBS), stereoelectroencephalography (SEEG), intracerebral drug delivery (IDD), stereotactic brain biopsy (SBB), stereotactic needle aspiration for hematoma, cysts and abscesses, and brachytherapy as well as thermal ablation of brain tumors and seizure-generating regions. We emphasize therapeutic considerations and complications that have been documented in conjunction with these applications

Deformable Multisurface Segmentation of the Spine for Orthopedic Surgery Planning and Simulation

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation

\u3ci\u3eCreating the Fleet Maker\u3c/i\u3e - Lessons Learned from the First Series of Workshops on Maker Concepts for Active Duty Personnel

The US Navy has supported research related to the 3D printing or Additive Manufacturing area for more than 20 years. More recently, efforts like the Print the Fleet initiative and Marine Makers are exploring ways to design and create solutions to future problems with the possibility of reducing maintenance costs, increasing equipment readiness, and improving combat effectiveness. The Creating the Fleet Maker project is an effort supported by the Navy and Marine Corps Science, Technology, Engineering and Mathematics Education, Outreach and Workforce Program of the Office of Naval Research. It examines the concept of making in order to develop skills for active duty personnel in 3D printing, computer aided design, and reverse engineering. As part of the Creating the Fleet Maker project, educational materials, and handson activities, based on STEM concepts, were developed for a 2-day workshop. During the first year of the project, a series of five workshops were delivered, with a total of 92 active activeduty sailors attending the workshops. This paper presents the lessons learned during the first series of workshops, including successes, challenges encountered, how these challenges were overcome, as well as areas for improvement as the project enters its second year. Results from the workshop assessments are very positive with the majority of sailors reporting an improvement in their knowledge of the concepts covered during the workshop, as well as in the skills for 3D printing, computer aided design, and reverse engineering. Furthermore, attendees reported interest in taking part in an extended version of the workshop or having it as part of their regular naval training