Single Feedback Model of Human Goal-Directed Movement

2008 ◽  
Author(s):  
Oh-Sang Kwon ◽  
Jeffrey N. Shelton ◽  
George T.-C. Chiu
Keyword(s):  
Early Stage ◽  
Human Movement ◽  
Minimum Variance ◽  
Human Motion ◽  
Movement Trajectory ◽  
Directed Movement ◽  
Psychophysical Data ◽  
Proposed Model ◽  
Aimed Movement ◽  
Speed Accuracy

Two prominent models frequently used to explain targeted human movement are the stochastic optimized-submovement model and the minimum variance model. Both successfully explain the speed-accuracy tradeoff known as Fitts’ law, but neither is complete. The former cannot predict movement trajectory between the endpoints, while the latter is not congruent with the multiple movement segments often observed in human motion. In this paper, a new model is proposed in which an aimed movement consists of two submovements and a single feedback instant, with the trajectory of each submovement being individually optimized. Simulations using the proposed model show that the optimal transition between two submovements occurs at an early stage of the movement, and produces a sharp peak in the acceleration profile. This result is consistent with psychophysical data. Also observed in numerical simulation is the bell-shaped positional variance curve that is in agreement with psychophysical data.

scholarly journals A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wilfredo Angulo ◽  
José M. Ramírez ◽  
Dany De Cecchis ◽  
Juan Primera ◽  
Henry Pacheco ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Risk Perceptions ◽  
Transmission Rate ◽  
Early Stage ◽  
Individual Risk ◽  
Seir Model ◽  
Proposed Model ◽  
Probable Site ◽  
Adjusted Model ◽  
The Impact

AbstractCOVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronavirus using a modified transmission rate SEIR model that takes into account the impact of government actions and the perception of risk by individuals in reaction to the proportion of fatal cases. The parameters related to these effects were fitted to the number of infected cases in the 33 provinces of China. The data for Hubei Province, the probable site of origin of the current pandemic, were considered as a particular case for the simulation and showed that the theoretical model reproduces the behavior of the data, thus indicating the importance of combining government actions and individual risk perceptions when the proportion of fatal cases is greater than $$4\%$$ 4 % . The results show that the adjusted model reproduces the behavior of the data quite well for some provinces, suggesting that the spread of the disease differs when different actions are evaluated. The proposed model could help to predict outbreaks of viruses with a biological and molecular structure similar to that of SARS-CoV-2.

Multi Human Movement Trajectory Extraction by Thermal Sensor

2014 ◽  
pp. 35-49
Author(s):  
Masato Kuki ◽  
Hiroshi Nakajima ◽  
Naoki Tsuchiya ◽  
Junichi Tanaka ◽  
Yutaka Hata
Keyword(s):  
Human Movement ◽  
Thermal Sensor ◽  
Movement Trajectory

scholarly journals A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients

Life ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1092
Author(s):  
Sikandar Ali ◽  
Ali Hussain ◽  
Satyabrata Aich ◽  
Moo Suk Park ◽  
Man Pyo Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Lung Disease ◽  
Lung Diseases ◽  
Early Stage ◽  
Confusion Matrix ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Proposed Model

Idiopathic pulmonary fibrosis, which is one of the lung diseases, is quite rare but fatal in nature. The disease is progressive, and detection of severity takes a long time as well as being quite tedious. With the advent of intelligent machine learning techniques, and also the effectiveness of these techniques, it was possible to detect many lung diseases. So, in this paper, we have proposed a model that could be able to detect the severity of IPF at the early stage so that fatal situations can be controlled. For the development of this model, we used the IPF dataset of the Korean interstitial lung disease cohort data. First, we preprocessed the data while applying different preprocessing techniques and selected 26 highly relevant features from a total of 502 features for 2424 subjects. Second, we split the data into 80% training and 20% testing sets and applied oversampling on the training dataset. Third, we trained three state-of-the-art machine learning models and combined the results to develop a new soft voting ensemble-based model for the prediction of severity of IPF disease in patients with this chronic lung disease. Hyperparameter tuning was also performed to get the optimal performance of the model. Fourth, the performance of the proposed model was evaluated by calculating the accuracy, AUC, confusion matrix, precision, recall, and F1-score. Lastly, our proposed soft voting ensemble-based model achieved the accuracy of 0.7100, precision 0.6400, recall 0.7100, and F1-scores 0.6600. This proposed model will help the doctors, IPF patients, and physicians to diagnose the severity of the IPF disease in its early stages and assist them to take proactive measures to overcome this disease by enabling the doctors to take necessary decisions pertaining to the treatment of IPF disease.

scholarly journals Neuro-musculoskeletal Upper Limb in-silico as virtual patient

2021 ◽  
Author(s):  
Mallampalli Kapardi ◽  
Madhav Pithapuram ◽  
Raghu Seshadri Iyengar ◽  
Mandayam Rangayyan Yashaswini ◽  
Avinash Kumar Singh ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Upper Limb ◽  
In Silico ◽  
Neural Control ◽  
Early Stage ◽  
Cervical Spinal Cord ◽  
Human Movement ◽  
Virtual Patient ◽  
Spatio Temporal ◽  
Movement Simulation

Virtual patients and physiologies allow experimentation, design, and early-stage clinical trials in-silico. Virtual patient technology for human movement systems that encompasses musculoskeleton and its neural control are few and far in between. In this work, we present one such neuro-musculoskeletal upper limb in-silico model. This upper limb is both modular in architecture and generates movement as an emergent phenomenon out of a multiscale co-simulation of spinal cord neural control and musculoskeletal dynamics. It is developed on the NEUROiD movement simulation platform that enables a co-simulation of popular neural simulator NEURON and the musculoskeletal simulator OpenSim. In this work, we describe the design and development of the upper limb in a modular fashion, while reusing existing models and modules. We further characterize and demonstrate the use of this model in generating a range of commonly observed movements by means of a spatio temporal stimulation pattern delivered to the cervical spinal cord. We believe this work enables a first and small step towards an in-silico paradigms for understanding upper limb movement, disease pathology, medication, and rehabilitation. Index Terms : co-simulation, in-silico, NEUROiD, neuromusculoskeletal, upper limb, Virtual patient.

scholarly journals Human care system for heart-rate and human-movement trajectory in home and its application to detect mental disease

2012 ◽  
Author(s):  
Yutaka Hata ◽  
Seigo Kanazawa ◽  
Maki Endo ◽  
Naoki Tsuchiya ◽  
Hiroshi Nakajima
Keyword(s):  
Heart Rate ◽  
Mental Disease ◽  
Human Movement ◽  
Movement Trajectory ◽  
Human Care ◽  
Care System

A Unified Use-Misuse Case Model for Capturing and Analysing Safety and Security Requirements

2011 ◽  
Vol 5 (4) ◽  
pp. 8-30
Author(s):  
O. T. Arogundade ◽  
A. T. Akinwale ◽  
Z. Jin ◽  
X. G. Yang
Keyword(s):  
Early Stage ◽  
System Development ◽  
Security Requirements ◽  
Health Care Information ◽  
Safety Requirements ◽  
Misuse Case ◽  
Proposed Model ◽  
Development Life Cycle ◽  
Care Information ◽  
The University

This paper proposes an enhanced use-misuse case model that allows both safety and security requirements to be captured during requirements elicitation. The proposed model extends the concept of misuse case by incorporating vulnerable use case and abuse case notations and relations that allows understanding and modeling different attackers and abusers behaviors during early stage of system development life cycle and finishes with a practical consistent combined model for engineering safety and security requirements.The model was successfully applied using health care information system gathered through the university of Kansas HISPC project. The authors were able to capture both security and safety requirements necessary for effective functioning of the system. In order to enhance the integration of the proposed model into risk analysis, the authors give both textual and detailed description of the model. The authors compare the proposed approach with other existing methods that identify and analyze safety and security requirements and discovered that it captures more security and safety threats.

Laminar-to-turbulent flame transition and cycle-to-cycle variations in large eddy simulation of spark-ignition engines

2020 ◽  
pp. 146808742096234
Author(s):  
Yunde Su ◽  
Derek Splitter ◽  
Seung Hyun Kim
Keyword(s):  
Large Eddy Simulation ◽  
Early Stage ◽  
Turbulent Flame ◽  
Transition Process ◽  
Spark Ignition ◽  
Transition Model ◽  
Flame Kernel ◽  
Eddy Simulation ◽  
Proposed Model ◽  
Large Eddy

This paper investigates the effect of laminar-to-turbulent flame transition modeling on the prediction of cycle-to-cycle variations (CCVs) in large eddy simulation (LES) of spark-ignition (SI) engines. A laminar-to-turbulent flame transition model that describes the non-equilibrium sub-filter flame speed evolution during an early stage of flame kernel growth is developed. In the present model, the flame transition is characterized by the flame kernel size at which the flame transition ends, defined here as the flame transition scale. The proposed model captures the effects that variations in a turbulent flow field have on the evolution of early-stage burning rates, through variations in the flame transition scale. The proposed flame transition model is combined with the front propagation formulation (FPF) method and a spark-ignition model to predict CCVs in a gasoline direct injection SI engine. It is found that multi-cycle LES with the proposed flame transition model reproduces experimentally-observed CCVs satisfactorily. When the transition model is not considered or when variations in the transition process are neglected, CCVs are significantly under-predicted for the case considered here. These results indicate the importance of modeling the laminar-to-turbulent flame transition and the effect of turbulence on the transition process, when predicting CCVs, under certain engine conditions. The LES results are also used to analyze sources for variations in the flame transition. It is found, for the present engine case, that the most important source is the cycle-to-cycle variation in the turbulence dissipation rate, which is used to measure the strength of turbulence in the proposed model, near a spark plug. The large-scale velocity field and the variations of the laminar flame speed due to the mixture composition and thermal stratification are also found to be important factors to contribute to the variations in the flame transition.

scholarly journals Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1801 ◽  
Author(s):  
Haitao Guo ◽  
Yunsick Sung
Keyword(s):  
Motion Capture ◽  
Short Term Memory ◽  
Arm Movements ◽  
Ground Truth ◽  
Human Movement ◽  
Human Motion ◽  
Soft Sensors ◽  
Upper Arm ◽  
Human Motion Capture ◽  
Gesture Control

The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.

Real time evaluation algorithm of human motion in tennis training robot

2020 ◽  
pp. 1-9
Author(s):  
Yingying Wang ◽  
Yongzhi Zhang
Keyword(s):  
Real Time ◽  
Rapid Development ◽  
Human Movement ◽  
Human Motion ◽  
Sports Activity ◽  
Training Requirements ◽  
Core Theory ◽  
Evaluation Algorithm ◽  
Stability And Accuracy ◽  
Time Evaluation

Tennis is a set of sports and entertainment and a sports activity, since 2014, tennis in China has been another rapid development. With the development of economy and technology, tennis training mode has been further optimized and reformed. At present, tennis training robot is the mainstream way to train athletes. However, there are some defects in the current tennis training robots, such as the low accuracy of human motion real-time evaluation, and the lack of stability. Therefore, this paper puts forward the related research on the real-time evaluation algorithm of human motion in tennis training robots, hoping to make up for the deficiency in this field. The research of this paper is mainly divided into four parts. The first part is to analyze the current situation of technology research in this field and put forward the idea of this paper by analyzing the shortcomings of the existing technology. The second part is the related basic theory research; this part deeply studies the core theory of tennis training and intelligent training robot, which provides a theoretical basis for the realization of the optimization scheme. The third part is the design and implementation of a real-time human motion evaluation optimization algorithm for tennis training robots. At the end of the paper, that is, the fourth part, through the way of field test and investigation, further proves the superiority of the improved real-time evaluation algorithm of human movement. The algorithm has good stability and accuracy and can meet the existing tennis training requirements.

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