scholarly journals Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6477
Author(s):  
Túlio Fernandes de Almeida ◽  
Edgard Morya ◽  
Abner Cardoso Rodrigues ◽  
André Felipe Oliveira de Azevedo Dantas
Keyword(s):  
Open Source ◽  
Measurement System ◽  
Joint Angle ◽  
Low Cost ◽  
Ground Truth ◽  
Percentage Error ◽  
Angle Estimation ◽  
Adult Individual ◽  
Average Percentage ◽  
Real Scenario

The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60∘. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28∘, and during evaluation in a real scenario it achieved an average ROM of 44.62∘. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios.

SHOULDER AND ELBOW JOINT ANGLE ESTIMATION FOR UPPER LIMB REHABILITATION TASKS USING LOW-COST INERTIAL AND OPTICAL SENSORS

2017 ◽  
Vol 17 (02) ◽  
pp. 1750031 ◽  
Author(s):  
ALIREZA ALIZADEGAN ◽  
SAEED BEHZADIPOUR
Keyword(s):  
Kalman Filter ◽  
Upper Limb ◽  
Optical Sensors ◽  
Joint Angle ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Angle Estimation ◽  
Upper Limb Rehabilitation ◽  
Body Acceleration ◽  
Limb Rehabilitation

This paper proposes a new method to improve accuracy and real-time performance of inertial joint angle estimation for upper limb rehabilitation applications by modeling body acceleration and adding low-cost markerless optical position sensors. A method based on a combination of the 3D rigid body kinematic equations and Denavit-Hartenberg (DH) convention is used to model body acceleration. Using this model, body acceleration measurements of the accelerometer are utilized to increase linearization order and compensate for body acceleration perturbations. To correct for the sensor-to-segment misalignment of the inertial sensors, position measurements of a low-cost markerless position sensor are used. Joint angles are estimated by Extended Kalman Filter (EKF) and compared with Unscented Kalman Filter (UKF) in terms of performance. Simulations are performed to quantify the existing error and potential improvements achievable by the proposed method. Experiments on a human test subject performing an upper limb rehabilitation task is used to validate the simulation results in realistic conditions.

Open-Source Antenna Pattern Validation using FEKO

2021 ◽  
Vol 35 (11) ◽  
pp. 1360-1361
Author(s):  
Christian Hearn
Keyword(s):  
Open Source ◽  
Measurement System ◽  
Position Control ◽  
Low Cost ◽  
Antenna Pattern ◽  
Undergraduate Level ◽  
Antenna Theory ◽  
Software Defined Radios ◽  
Arduino Microcontroller ◽  
Pattern Validation

An open-source antenna pattern measurement system comprised of software-defined radios (SDRs), standard PVC tubing, and 3-D printer components measures the radiation patterns of student-built prototype antennas. Position control is realized using an Arduino microcontroller. Measured principal plane gain patterns for two antenna prototypes are compared to (FEKO) simulated results. The low-cost, open-source nature of the measurement system is ideal for undergraduate-level investigation of antenna theory and measurement.

scholarly journals Re-classification of archival Ovarian Carcinoma diagnostics using immunohistologic digital quantification and algorithmic prognosis

2020 ◽  
Author(s):  
Camelia D. Vrabie ◽  
Mihnea I. Gangal ◽  
Marius D Gangal
Keyword(s):  
Ovarian Carcinoma ◽  
Open Source ◽  
Diagnostic Criteria ◽  
Low Cost ◽  
Research Question ◽  
Ground Truth ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Digital Analysis

Twenty years of research improved the classification of ovarian carcinoma, making the diagnostic relevant from a scientific and clinical perspective. Our research question was to find out if old studies are still pertinent under new diagnostic criteria and how we can use machine learning techniques for re-classification purposes. The same main investigator re-classified 60 cases of ovarian carcinoma after 15 years, using 2014 WHO diagnostic criteria. Selected pathology data only (macro, micro information and immunohistochemistry images coming from a seven-stain panel) were provided for digital analysis. Biomarker images were digitalized and quantified using open source software and a validated methodology. 1080 attributes were classified using a random forest (open source) algorithm, using a supervised learning technique (the training dataset used 180 attributes). Human results were considered ground truth for the digital analysis. The human analysis maintained the initial histopathologic diagnostic in 61.5% of cases. The digital prediction shows 80% accuracy and 73% precision when compared with human reclassified data. Based on results, we concluded that recycling of old studies is possible. Limitation of the study are the low number of cases analyzed, the total absence of clinical, treatment and prognostic data and a possible human criteria selection bias. Even if technical difficulties related to biomarker selection and histological analysis exist, digital investigation of existing, large archival registries is feasible, reliable and it can be done at a low cost.

Knee joint angle estimation during gait cycle using low-cost inertial sensors

2017 ◽  
Author(s):  
F. Sanchez-Guzman ◽  
J. F. Guerrero-Castellanos ◽  
G. Mino-Aguilar ◽  
R. C. Ambrosio-Lazaro ◽  
J. Linares-Flores
Keyword(s):  
Knee Joint ◽  
Gait Cycle ◽  
Joint Angle ◽  
Inertial Sensors ◽  
Low Cost ◽  
Angle Estimation ◽  
Knee Joint Angle

scholarly journals EARBOX: An open tool for high-throughput measurements of the spatial organization of maize ears and inference of novel traits

2021 ◽  
Author(s):  
Vincent Oury ◽  
Timothe Leroux ◽  
Olivier Turc ◽  
Romain Chapuis ◽  
Carine Palaffre ◽  
...  
Keyword(s):  
Open Source ◽  
High Throughput ◽  
Spatial Organization ◽  
Low Cost ◽  
Plant Genetic Resources ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Spatial Features ◽  
Novel Traits ◽  
Maize Ear

Background: Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. The spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results: We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organization, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions: The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorization. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

Rapid mapping accuracy assessment using low-cost UAV and open-source Structure from Motion

2020 ◽  
Vol 52 ◽  
pp. 55-61
Author(s):  
Ettore Potente ◽  
Cosimo Cagnazzo ◽  
Alessandro Deodati ◽  
Giuseppe Mastronuzzi
Keyword(s):  
Open Source ◽  
Structure From Motion ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Mapping Accuracy ◽  
Rapid Mapping ◽  
Source Structure

Actuation and dynamic mechanical characteristics of a core free flat dielectric electro-active polymer soft actuator

2020 ◽  
Vol 14 (4) ◽  
pp. 7396-7404
Author(s):  
Abdul Malek Abdul Wahab ◽  
Emiliano Rustighi ◽  
Zainudin A.
Keyword(s):  
Theoretical Model ◽  
Resonance Frequency ◽  
Dynamic Testing ◽  
Experimental Results ◽  
Percentage Error ◽  
Initial Tension ◽  
Average Percentage ◽  
Soft Actuator ◽  
Average Percentage Error ◽  
Mechanical Dynamics

Various complex shapes of dielectric electro-active polymer (DEAP) actuator have been promoted for several types of applications. In this study, the actuation and mechanical dynamics characteristics of a new core free flat DEAP soft actuator were investigated. This actuator was developed by Danfoss PolyPower. DC voltage of up to 2000 V was supplied for identifying the actuation characteristics of the actuator and compare with the existing formula. The operational frequency of the actuator was determined by dynamic testing. Then, the soft actuator has been modelled as a uniform bar rigidly fixed at one end and attached to mass at another end. Results from the theoretical model were compared with the experimental results. It was found that the deformation of the current actuator was quadratic proportional to the voltage supplied. It was found that experimental results and theory were not in good agreement for low and high voltage with average percentage error are 104% and 20.7%, respectively. The resonance frequency of the actuator was near 14 Hz. Mass of load added, inhomogeneity and initial tension significantly affected the resonance frequency of the soft actuator. The experimental results were consistent with the theoretical model at zero load. However, due to inhomogeneity, the frequency response function’s plot underlines a poor prediction where the theoretical calculation was far from experimental results as values of load increasing with the average percentage error 15.7%. Hence, it shows the proposed analytical procedure not suitable to provide accurate natural frequency for the DEAP soft actuator.

PyDOSS: An open-source Python-based communicable disease outbreak surveillance system designed and used in a COVID-19 community care facility (Preprint)

2020 ◽  
Author(s):  
Andrew Fang ◽  
Jonathan Kia-Sheng Phua ◽  
Terrence Chiew ◽  
Daniel De-Liang Loh ◽  
Lincoln Ming Han Liow ◽  
...  
Keyword(s):  
Open Source ◽  
Surveillance System ◽  
Community Care ◽  
Communicable Disease ◽  
Low Cost ◽  
Real Life ◽  
Disease Outbreak ◽  
Disease Spread ◽  
Healthcare Team ◽  
Increased Risk

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.

scholarly journals Adaptive Neuro-Fuzzy Inference System for Modelling the Effect of Slurry Impacts on PLA Material Processed by FDM

Polymers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 118
Author(s):  
Bahaa Saleh ◽  
Ibrahem Maher ◽  
Yasser Abdelrhman ◽  
Mahmoud Heshmat ◽  
Osama Abdelaal
Keyword(s):  
Weight Gain ◽  
Fused Deposition Modeling ◽  
Synergetic Effect ◽  
Solid Particles ◽  
Percentage Error ◽  
Inference System ◽  
Average Percentage ◽  
Fused Deposition ◽  
Effect Of Water ◽  
Neuro Fuzzy

In this research, the effect of water-silica slurry impacts on polylactic acid (PLA) processed by fused deposition modeling (FDM) is examined under different conditions with the assistance of an adaptive neuro-fuzzy interference system (ANFIS). Building orientation, layer thickness, and slurry impact angle are considered as the controllable variables. Weight gain resulting from water, net weight gain, and total weight gain are the predicting variables. Results uncover the accomplishment of the ANFIS model to appropriately appraise slurry erosion in correlation with comparing real data. Both experimental and ANFIS results are almost identical with average percentage error less than 5.45 × 10−6. We observed during the slurry impacts tests that all specimens showed an increase in their weights. This weight gain was finally interpreted to the synergetic effect of water absorption and the solid particles fragmentations immersed within the specimens due to the successive slurry impacts.

scholarly journals Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2944
Author(s):  
Benjamin James Ralph ◽  
Marcel Sorger ◽  
Benjamin Schödinger ◽  
Hans-Jörg Schmölzer ◽  
Karin Hartl ◽  
...  
Keyword(s):  
Open Source ◽  
User Interfaces ◽  
Industrial Revolution ◽  
Low Cost ◽  
Material Behavior ◽  
Sensor Data ◽  
Management Tool ◽  
Special Focus ◽  
Programming Environments ◽  
Metal Processing

Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.

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