Real-time control of 3D virtual human motion using a depth-sensing camera for agricultural machinery training

Electromyography (EMG) signal contains a large amount of human motion information, which can be used to classify human actions. In this study, based on the detection of surface electromyography (sEMG) signal, three actions were designed, the sEMG signal was collected by the EMG acquisition system. Four feature values, including root-mean-square value, average absolute value (MAV), wavelength, and Zero crossing point, were extracted from the signal. Then these values were taken as the input of Back-Propagation neural network (BPNN) to recognize different actions, thereby realizing the real-time control of mechanical simulated arm. The experiment found that the training time of the BPNN method designed in this study was short, 11.36 s, and the average recognition accuracy rate reached 92.2%. In the real-time control experiment of mechanical simulated arm, the recognition accuracy of different actions reached more than 90%, and the running time was short. The experimental results verifies the effectiveness of the proposed method and make some contributions to the efficient control of the mechanical simulation arm.

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A real-time control model for intelligent virtual human

CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436) ◽

10.1109/ccece.2003.1226159 ◽

2004 ◽

Cited By ~ 1

Author(s):

Y. Liu ◽

D. Guo ◽

J. Sun ◽

Z. Wei

Keyword(s):

Real Time ◽

Control Model ◽

Virtual Human ◽

Real Time Control ◽

Time Control

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Real-Time Control of a Virtual Human Using Minimal Sensors

Presence Teleoperators & Virtual Environments ◽

10.1162/pres.1993.2.1.82 ◽

1993 ◽

Vol 2 (1) ◽

pp. 82-86 ◽

Cited By ~ 62

Author(s):

Norman I. Badler ◽

Michael J. Hollick ◽

John P. Granieri

Keyword(s):

Computer Graphics ◽

Real Time ◽

Human Body ◽

Inverse Kinematics ◽

Human Model ◽

Virtual Human ◽

Real Time Control ◽

Time Control ◽

Six Dof ◽

Full Body

We track, in real-time, the position and posture of a human body, using a minimal number of six DOF sensors to capture full body standing postures. We use four sensors to create a good approximation of a human operator's position and posture, and map it on to our articulated computer graphics human model. The unsensed joints are positioned by a fast inverse kinematics algorithm. Our goal is to realistically recreate human postures while minimally encumbering the operator.

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A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

Methods of Information in Medicine ◽

10.1055/s-0038-1634619 ◽

1995 ◽

Vol 34 (05) ◽

pp. 475-488

Author(s):

B. Seroussi ◽

J. F. Boisvieux ◽

V. Morice

Keyword(s):

Real Time ◽

Linear Time ◽

Treatment Plan ◽

Control Architecture ◽

Real Time Control ◽

Time Representation ◽

Time Control ◽

Continuous Support ◽

Definition Of ◽

Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

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