scholarly journals Enabling Technology for Remote Prosthetic Alignment Tuning

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 659-664
Author(s):  
David A Boone ◽  
Sarah R Chang
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Coordinate Measuring Machine ◽  
Software Application ◽  
Prosthetic Socket ◽  
Prosthetic Alignment ◽  
Prosthesis Alignment ◽  
Alignment Tool ◽  
Measuring Machine

ABSTRACT Introduction This research has resulted in a system of sensors and software for effectively adjusting prosthetic alignment with digital numeric control. We called this suite of technologies the Prosthesis Smart Alignment Tool (ProSAT) system. Materials and Methods The ProSAT system has three components: a prosthesis-embedded sensor, an alignment tool, and an Internet-connected alignment expert system application that utilizes machine learning to analyze prosthetic alignment. All components communicate via Bluetooth. Together, they provide for numerically controlled prosthesis alignment adjustment. The ProSAT components help diagnose and guide the correction of very subtle, difficult-to-see imbalances in dynamic gait. The sensor has been cross-validated against kinetic measurement in a gait laboratory, and bench testing was performed to validate the performance of the tool while adjusting a prosthetic socket based on machine learning analyses from the software application. Results The three-dimensional alignment of the prosthetic socket was measured pre- and postadjustment from two fiducial points marked on the anterior surface of the prosthetic socket. A coordinate measuring machine was used to derive an alignment angular offset from vertical for both conditions: pre- and postalignment conditions. Of interest is the difference in the angles between conditions. The ProSAT tool is only controlling the relative change made to the alignment, not an absolute position or orientation. Target alignments were calculated by the machine learning algorithm in the ProSAT software, based on input of kinetic data samples representing the precondition and where a real prosthetic misalignment condition was known a priori. Detected misalignments were converted by the software to a corrective adjustment in the prosthesis alignment being tested. We demonstrated that a user could successfully and quickly achieve target postalignment change within an average of 0.1°. Conclusions The accuracy of a prototype ProSAT system has been validated for controlled alignment changes by a prosthetist. Refinement of the ergonomic form and technical function of the hardware and clinical usability of the mobile software application are currently being completed with benchtop experiments in advance of further human subject testing of alignment efficiency, accuracy, and user experience.

Evaluation of Parts Produced by a Novel Additive Manufacturing Process

2013 ◽  
Vol 315 ◽  
pp. 63-67 ◽  
Author(s):  
Muhammad Fahad ◽  
Neil Hopkinson
Keyword(s):  
Additive Manufacturing ◽  
Rapid Prototyping ◽  
Manufacturing Process ◽  
High Speed ◽  
Three Dimensional ◽  
Coordinate Measuring Machine ◽  
Layer By Layer ◽  
Nylon 12 ◽  
Measuring Machine ◽  
End Use

Rapid prototyping refers to building three dimensional parts in a tool-less, layer by layer manner using the CAD geometry of the part. Additive Manufacturing (AM) is the name given to the application of rapid prototyping technologies to produce functional, end use items. Since AM is relatively new area of manufacturing processes, various processes are being developed and analyzed for their performance (mainly speed and accuracy). This paper deals with the design of a new benchmark part to analyze the flatness of parts produced on High Speed Sintering (HSS) which is a novel Additive Manufacturing process and is currently being developed at Loughborough University. The designed benchmark part comprised of various features such as cubes, holes, cylinders, spheres and cones on a flat base and the build material used for these parts was nylon 12 powder. Flatness and curvature of the base of these parts were measured using a coordinate measuring machine (CMM) and the results are discussed in relation to the operating parameters of the process.The result show changes in the flatness of part with the depth of part in the bed which is attributed to the thermal gradient within the build envelope during build.

Evaluation of Actual Geometric Tolerances Using Coordinate Measuring Machine Data

1989 ◽  
Author(s):  
W. H. ElMaraghy ◽  
Z. Wu ◽  
H. A. ElMaraghy
Keyword(s):  
Three Dimensional ◽  
Measurement Data ◽  
Simulated Data ◽  
Coordinate Measuring Machine ◽  
Optimization Techniques ◽  
Test Cases ◽  
Tolerance Zone ◽  
Geometric Tolerances ◽  
Measuring Machine

Abstract This paper focuses on the development of a procedure and algorithms for the systematic comparison of geometric variations of measured features with their specified geometric tolerances. To automate the inspection of mechanical parts, it is necessary to analyze the measurement data captured by coordinate measuring machines (CMM) in order to detect out-of-tolerance conditions. A procedure for determining the geometric tolerances from the measured three dimensional coordinates on the surface of a cylindrical feature is presented. This procedure follows the definitions of the geometric tolerances used in the current Standards, and is capable of determining the value of each geometric tolerance from the composite 3-D data. The developed algorithms adopt the minimum tolerance zone criterion. Nonlinear numerical optimization techniques are used to fit the data to the minimum tolerance zone. Two test cases are given in the paper which demonstrate the successful determination of geometric tolerances from given simulated data.

Contactless Li-Ion Battery Voltage Detection by Using Walabot and Machine Learning

2019 ◽  
Author(s):  
Yanan Wang ◽  
Haoyu Niu ◽  
Tiebiao Zhao ◽  
Xiaozhong Liao ◽  
Lei Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Principal Component ◽  
Stochastic Gradient Descent ◽  
Li Ion Battery ◽  
Linear Discriminant ◽  
Li Ion

Abstract This paper has proposed a contactless voltage classification method for Lithium-ion batteries (LIBs). With a three-dimensional radio-frequency based sensor called Walabot, voltage data of LIBs can be collected in a contactless way. Then three machine learning algorithm, that is, principal component analysis (PCA), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) classifiers, have been employed for data processing. Experiments and comparison have been conducted to verify the proposed method. The colormaps of results and prediction accuracy show that LDA may be most suitable for LIBs voltage classification.

scholarly journals Trueness and Precision of Three-Dimensional Digitizing Intraoral Devices

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hussam Mutwalli ◽  
Michael Braian ◽  
Deyar Mahmood ◽  
Christel Larsson
Keyword(s):  
Three Dimensional ◽  
Coordinate Measuring Machine ◽  
In Vitro Study ◽  
Vitro Study ◽  
Distance Measurements ◽  
True Value ◽  
Measuring Machine ◽  
One Way Anova ◽  
Intraoral Devices

Aim. To measure the trueness and precision under repeatable conditions for different intraoral scanners (IOSs) when scanning fully edentulous arch with multiple implants. Materials and Methods. Three IOSs and one industrial scanner were used to scan one edentulous master cast containing five implant scan bodies and three spheres. The cast was scanned thirty times with each scanner device. All scans were analyzed in the inspect software, and three-dimensional locations of the implants and the interarch distance between the spheres were measured. The values were compared to measurements made with one coordinate measuring machine (true value). One-way ANOVA was used to calculate the differences between IOSs and in comparison with the true value. Results. Significant differences were found between all IOSs. For the implant measurements, Trios 3 had the lowest trueness (≤114 μm), followed by Trios 3 mono (≤63 μm) and Itero element (≤−41 μm). Trios had the lowest precision (≤135 μm), followed by Itero element (≤101 μm) and Trios 3 mono (≤100 μm). With regard to the interarch distance measurements, Trios 3 had the lowest trueness (≤68 μm), followed by Trios 3 mono (≤45 μm) and Itero element (≤40 μm). Trios 3 had the lowest precision (≤206 μm), followed by Itero element (≤124 μm) and Trios 3 mono (≤111 μm). Conclusion. The results from this in vitro study suggest that precision is low for the tested IOS devices when scanning fully edentulous arches with multiple implants.

scholarly journals 3D DIGITIZING TECHNOLOGY IN PRODUCT REVERSE DESIGN

2011 ◽  
Author(s):  
Qingjin Peng ◽  
Hector Sanchez
Keyword(s):  
Shape Recovery ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Coordinate Measuring Machine ◽  
Cost Effective ◽  
3D Shape ◽  
3D Data ◽  
Cost Effective Approach ◽  
Measuring Machine ◽  
And Performance

The reverse design develops new products based on the improvement of existing products. The shape recovery of three-dimensional (3D) objects is the basis of the product reverse design. 3D digitization technology is an important tool for the 3D shape recovery. This paper analyses the current 3D data acquisition technology. The accuracy and performance of the 3D laser scanner is evaluated. A cost-effective approach is proposed to recover 3D shape of objects using a structured-light technique. Details of the proposed method are described. Application examples are presented. The accuracy is evaluated using a coordinate measuring machine.

Data Collection and Model Construction Methods for Reverse Engineering

2010 ◽  
Vol 102-104 ◽  
pp. 189-193
Author(s):  
Ling Yun Jiang ◽  
Zhi Biao Wang
Keyword(s):  
Data Collection ◽  
Point Cloud ◽  
Three Dimensional ◽  
Coordinate Measuring Machine ◽  
Digital Measurement ◽  
Original Design ◽  
Regular Feature ◽  
Technical Problems ◽  
Construction Methods ◽  
Measuring Machine

The process of creating a CAD model from an object is mainly made up of two steps: the data collection through digital measurement and the construction of parameterized and revisable model. This paper discusses the measuring process and technical problems of the Coordinate Measuring Machine (CMM) and non-contact sensor. Through comparative analysis, we determine the application scope of those approaches in measuring different dimensions of the same objects considering the time efficiency and tolerance requirement. This paper divide the objects into two categories: freeform feature objects and regular feature objects. As for the freeform feature objects, people could fit wrap-around B-spline surfaces to construct the model. Regular feature objects for mass produce contain machined surfaces which should be precisely measured and modeled. The model of regular feature object should be constructed by three-dimensional modeling software, so that it is parametric and revisable for changing and improving the original design. Sizes and position of important surfaces of the model are acquired from CMM, and those of non-important features are fitted though point cloud processing. Some profile can’t be measured directly from CMM but should be precise, so this paper proposed two methods to construct profile line and analyze error by comparing it with point cloud.

AUTONOMOUS BEHAVIORS OF GRAPHICAL AVATARS BASED ON MACHINE LEARNING

2012 ◽  
Vol 26 (02) ◽  
pp. 1251002
Author(s):  
YUESHENG HE ◽  
YUAN YAN TANG
Keyword(s):  
Machine Learning ◽  
Hierarchical Structure ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Research Area ◽  
Machine Learning Techniques ◽  
3D Animation ◽  
Graphical Environment ◽  
Novel Approach ◽  
The Difference

Graphical avatars have gained popularity in many application domains such as three-dimensional (3D) animation movies and animated simulations for product design. However, the methods to edit avatars' behaviors in the 3D graphical environment remained to be a challenging research topic. Since the hand-crafted methods are time-consuming and inefficient, the automatic actions of the avatars are required. To achieve the autonomous behaviors of the avatars, artificial intelligence should be used in this research area. In this paper, we present a novel approach to construct a system of automatic avatars in the 3D graphical environments based on the machine learning techniques. Specific framework is created for controlling the behaviors of avatars, such as classifying the difference among the environments and using hierarchical structure to describe these actions. Because of the requirement of simulating the interactions between avatars and environments after the classification of the environment, Reinforcement Learning is used to compute the policy to control the avatar intelligently in the 3D environment for the solution of the problem of different situations. Thus, our approach has solved problems such as where the levels of the missions will be defined and how the learning algorithm will be used to control the avatars. In this paper, our method to achieve these goals will be presented. The main contributions of this paper are presenting a hierarchical structure to control avatars automatically, developing a method for avatars to recognize environment and presenting an approach for making the policy of avatars' actions intelligently.

scholarly journals UniAssist- Implementation of Machine Learning Based Higher Education University Recommendation System

2020 ◽  
pp. 262-266
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Work Experience ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Relevant Information ◽  
Mobile App ◽  
Admission Process ◽  
Software Application ◽  
Tuition Fee

UniAssist project is implemented to help students who have completed their Bachelorette degree and are looking forward to study abroad to pursue their higher education such as Masters. Machine Learning would help identify appropriate Universities for such students and suggest them accordingly. UniAssist would help such individuals by recommending those Universities according to their preference of course, country and considering their grades, work experience and qualifications. There is a need for students hoping to pursue higher education outside India to get to know about proper universities. Data collected is then converted into relevant information that is currently not easily available such as courses offered by their dream universities, the avg. tuition fee and even the avg. expense of living near the chosen university on single mobile app based software platform. This is the first phase of the admission process for every student. The machine-learning algorithm used is Collaborative filtering memory-based approach using KNN calculated using cosine similarity. A mobile-based software application is implemented in order to help and guide students for their higher education.

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