scholarly journals Movement Onset Detection and Target Estimation for Robot-Aided Arm Training

2015 ◽  
Vol 63 (4) ◽  
Author(s):  
Robert Riener ◽  
Domen Novak
Keyword(s):  
Eye Tracking ◽  
Real Time ◽  
Time Use ◽  
Healthy Subjects ◽  
Robot Control ◽  
Contextual Information ◽  
Estimation Algorithm ◽  
Movement Onset ◽  
Joint Torques ◽  
Arm Rehabilitation

AbstractThis paper presents a motion intention estimation algorithm that is based on the recordings of joint torques, joint positions, electromyography, eye tracking and contextual information. It is intended to be used to support a virtual-reality-based robotic arm rehabilitation training. The algorithm first detects the onset of a reaching motion using joint torques and electromyography. It then predicts the motion target using a combination of eye tracking and context, and activates robotic assistance toward the target. The algorithm was first validated offline with 12 healthy subjects, then in a real-time robot control setting with 3 healthy subjects. In offline crossvalidation, onset was detected using torques and electromyography 116 ms prior to detectable changes in joint positions. Furthermore, it was possible to successfully predict a majority of motion targets, with the accuracy increasing over the course of the motion. Results were slightly worse in online validation, but nonetheless show great potential for real-time use with stroke patients.

scholarly journals Real-Time Estimation of Glenohumeral Joint Rotation Center With Cable-Driven Arm Exoskeleton (CAREX)—A Cable-Based Arm Exoskeleton

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Ying Mao ◽  
Xin Jin ◽  
Sunil K. Agrawal
Keyword(s):  
Real Time ◽  
Glenohumeral Joint ◽  
Kinematic Model ◽  
Estimation Method ◽  
Time Estimation ◽  
Estimation Algorithm ◽  
Joint Rotation ◽  
Rotation Center ◽  
Arm Rehabilitation ◽  
Novel Approach

In the past few years, the authors have proposed several prototypes of a Cable-driven upper ARm EXoskeleton (CAREX) for arm rehabilitation. One of the assumptions of CAREX was that the glenohumeral joint rotation center (GH-c) remains stationary in the inertial frame during motion, which leads to inaccuracy in the kinematic model and may hamper training performance. In this paper, we propose a novel approach to estimate GH-c using measurements of shoulder joint angles and cable lengths. This helps in locating the GH-c center appropriately within the kinematic model. As a result, more accurate kinematic model can be used to improve the training of human users. An estimation algorithm is presented to compute the GH-c in real-time. The algorithm was implemented on the latest prototype of CAREX. Simulations and preliminary experimental results are presented to validate the proposed GH-c estimation method.

Real-Time Estimation of Glenohumeral Joint Rotation Center With CAREX: A Cable-Based Arm Exoskeleton

2012 ◽  
Author(s):  
Ying Mao ◽  
Xin Jin ◽  
Sunil K. Agrawal
Keyword(s):  
Real Time ◽  
Healthy Subjects ◽  
Degrees Of Freedom ◽  
Glenohumeral Joint ◽  
Kinematic Model ◽  
Time Estimation ◽  
Estimation Algorithm ◽  
Experimental Results ◽  
Joint Rotation ◽  
Rotation Center

In the past few years, the authors have proposed several prototypes of a Cable-driven upper ARm EXoskeleton (CAREX) for arm rehabilitation. The key advantages of CAREX over conventional exoskeletons are: (i) It is nearly an order of magnitude lighter. (ii) It does not have conventional links and joints, hence does not require joint axes alignment and segment lengths adjustment. (iii) It does not limit the natural degrees-of-freedom of the upper limb. (iv) The structure of the exoskeleton is novel as the cables are routed from the proximal to the distal segments of the arm. Preliminary experimental results with CAREX on a robotic arm and on healthy subjects have demonstrated the effectiveness of the exoskeleton within “assist-as-needed” training paradigm. In this paper, we propose a novel approach to estimate the glenohumeral joint rotation center (GH-c) using measurements of shoulder joint angles and cable lengths. This helps in locating the glenohumeral joint rotation center appropriately within the kinematic model. As a result, more accurate kinematic model can be used to improve the training of human users. An estimation algorithm is presented to compute the GH-c in real-time. The algorithm was implemented on the latest prototype of CAREX which controls four degrees-of-freedom of the shoulder and elbow. Preliminary experiments were performed on two healthy subjects under two different scenarios: (i) GH-c was assumed to be a fixed point and (ii) GH-c was estimated using the proposed algorithm. Experimental results are presented to compare the two scenarios.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Real-time face & eye tracking and blink detection using event cameras

2021 ◽  
Author(s):  
Cian Ryan ◽  
Brian O’Sullivan ◽  
Amr Elrasad ◽  
Aisling Cahill ◽  
Joe Lemley ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Real Time ◽  
Blink Detection

High-precision roll attitude estimation of decoupled canards relative to the projectile body using bipolar hall-effect sensors

2021 ◽  
pp. 1-9
Author(s):  
Tingting Yin ◽  
Zhong Yang ◽  
Youlong Wu ◽  
Fangxiu Jia
Keyword(s):  
Hall Effect ◽  
Real Time ◽  
High Precision ◽  
Solution Method ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Fuzzy Algorithms ◽  
Positioning Method ◽  
Hall Effect Sensors

The high-precision roll attitude estimation of the decoupled canards relative to the projectile body based on the bipolar hall-effect sensors is proposed. Firstly, the basis engineering positioning method based on the edge detection is introduced. Secondly, the simplified dynamic relative roll model is established where the feature parameters are identified by fuzzy algorithms, while the high-precision real-time relative roll attitude estimation algorithm is proposed. Finally, the trajectory simulations and grounded experiments have been conducted to evaluate the advantages of the proposed method. The positioning error is compared with the engineering solution method, and it is proved that the proposed estimation method has the advantages of the high accuracy and good real-time performance.

scholarly journals Full-Observable Three-Phase State Estimation Algorithm Applied to Electric Distribution Grids

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1327 ◽  
Author(s):  
Thiago Soares ◽  
Ubiratan Bezerra ◽  
Maria Tostes
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Distribution Systems ◽  
Phase State ◽  
Reactive Power ◽  
Estimation Algorithm ◽  
Load Flow ◽  
Electrical Network ◽  
The Real ◽  
Three Phase

This paper proposes the development of a three-phase state estimation algorithm, which ensures complete observability for the electric network and a low investment cost for application in typical electric power distribution systems, which usually exhibit low levels of supervision facilities and measurement redundancy. Using the customers´ energy bills to calculate average demands, a three-phase load flow algorithm is run to generate pseudo-measurements of voltage magnitudes, active and reactive power injections, as well as current injections which are used to ensure the electrical network is full-observable, even with measurements available at only one point, the substation-feeder coupling point. The estimation process begins with a load flow solution for the customers´ average demand and uses an adjustment mechanism to track the real-time operating state to calculate the pseudo-measurements successively. Besides estimating the real-time operation state the proposed methodology also generates nontechnical losses estimation for each operation state. The effectiveness of the state estimation procedure is demonstrated by simulation results obtained for the IEEE 13-bus test network and for a real urban feeder.

Real-time RT-PCR analysis of housekeeping genes in human skeletal muscle following acute exercise

2004 ◽  
Vol 18 (2) ◽  
pp. 226-231 ◽  
Author(s):  
Douglas J. Mahoney ◽  
Kate Carey ◽  
Ming-Hua Fu ◽  
Rodney Snow ◽  
David Cameron-Smith ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Real Time ◽  
Healthy Subjects ◽  
Acute Exercise ◽  
Housekeeping Genes ◽  
Housekeeping Gene ◽  
Cycle Ergometry ◽  
Pcr Analysis ◽  
28S Rrna ◽  
Rt Pcr

Studies examining gene expression with RT-PCR typically normalize their mRNA data to a constitutively expressed housekeeping gene. The validity of a particular housekeeping gene must be determined for each experimental intervention. We examined the expression of various housekeeping genes following an acute bout of endurance (END) or resistance (RES) exercise. Twenty-four healthy subjects performed either a interval-type cycle ergometry workout to exhaustion (∼75 min; END) or 300 single-leg eccentric contractions (RES). Muscle biopsies were taken before exercise and 3 h and 48 h following exercise. Real-time RT-PCR was performed on β-actin, cyclophilin (CYC), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and β2-microglobulin (β2M). In a second study, 10 healthy subjects performed 90 min of cycle ergometry at ∼65% of V̇o2 max, and we examined a fifth housekeeping gene, 28S rRNA, and reexamined β2M, from muscle biopsy samples taken immediately postexercise. We showed that CYC increased 48 h following both END and RES exercise (3- and 5-fold, respectively; P < 0.01), and 28S rRNA increased immediately following END exercise (2-fold; P = 0.02). β-Actin trended toward an increase following END exercise (1.85-fold collapsed across time; P = 0.13), and GAPDH trended toward a small yet robust increase at 3 h following RES exercise (1.4-fold; P = 0.067). In contrast, β2M was not altered at any time point postexercise. We conclude that β2M and β-actin are the most stably expressed housekeeping genes in skeletal muscle following RES exercise, whereas β2M and GAPDH are the most stably expressed following END exercise.

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