Real-Time Human Motion Behavior Detection via CNN Using mmWave Radar

2019 ◽  
Vol 3 (2) ◽  
pp. 1-4 ◽  
Author(s):  
Renyuan Zhang ◽  
Siyang Cao
Keyword(s):  
Real Time ◽  
Human Motion ◽  
Behavior Detection ◽  
Motion Behavior

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

A real-time trajectory modification algorithm

Robotica ◽  
2001 ◽  
Vol 19 (4) ◽  
pp. 395-405 ◽  
Author(s):  
Vadim Rogozin ◽  
Yael Edan ◽  
Tamar Flash
Keyword(s):  
Real Time ◽  
Target Location ◽  
Time Algorithm ◽  
Initial Position ◽  
Human Motion ◽  
Movement Direction ◽  
Switch Time ◽  
Velocity Constraints ◽  
The Mathematical Model ◽  
Motion Behavior

This paper presents a real-time algorithm for modifying the trajectory of a manipulator approaching a moving target. The algorithm is based on the superposition scheme; a model developed based on human motion behavior. The algorithm generates a smooth trajectory toward the new target by calculating the vectorial sum between the first trajectory (initial position and first target) and second trajectory (between first and second target location). The algorithm searches for the switch hme that will result in a minimum time trajectory. The idea of the algorithm is to define some domain where the optimal switching time can be found, reduce this domain as much as possible to decrease the number of the points that must be checked and try every remaining candidate in this domain to find numerically the best (optimal) switch time. The algorithm was implemented on an Adept-one robotic system taking into account velocity constraints. The actual velocity profile was found to be less smooth than specified by the mathematical model. When the switch occurs at the middle of the trajectory when the speed is close to its maximum, the change in the movement direction is performed more gently.

Vision-Based Real-time Human Malicious Behavior Detection

2021 ◽  
Author(s):  
Hajra Binte Naeem ◽  
Muhammad Haroon Yousaf ◽  
Farhan Hassan Khan ◽  
Amanullah Yasin
Keyword(s):  
Real Time ◽  
Behavior Detection ◽  
Malicious Behavior

scholarly journals Human motion behavior while interacting with an industrial robot

Work ◽  
2012 ◽  
Vol 41 ◽  
pp. 1699-1707 ◽  
Author(s):  
Dino Bortot ◽  
Hao Ding ◽  
Alexandros Antonopolous ◽  
Klaus Bengler
Keyword(s):  
Industrial Robot ◽  
Human Motion ◽  
Motion Behavior

Improved Skeleton Tracking by Duplex Kinects: A Practical Approach for Real-Time Applications

2013 ◽  
Vol 13 (4) ◽  
Author(s):  
Kwok-Yun Yeung ◽  
Tsz-Ho Kwok ◽  
Charlie C. L. Wang
Keyword(s):  
Real Time ◽  
Technical Difficulty ◽  
Human Motion ◽  
Depth Information ◽  
Length Variation ◽  
Single View ◽  
Motion Extraction ◽  
Hard Constraints ◽  
Real Time Applications ◽  
Frame Motion

Recent development of per-frame motion extraction method can generate the skeleton of human motion in real-time with the help of RGB-D cameras such as Kinect. This leads to an economic device to provide human motion as input for real-time applications. As generated by a single-view image plus depth information, the extracted skeleton usually has problems of unwanted vibration, bone-length variation, self-occlusion, etc. This paper presents an approach to overcome these problems by synthesizing the skeletons generated by duplex Kinects, which capture the human motion in different views. The major technical difficulty of this synthesis comes from the inconsistency of two skeletons. Our algorithm is formulated under the constrained optimization framework by using the bone-lengths as hard constraints and the tradeoff between inconsistent joint positions as soft constraints. Schemes are developed to detect and re-position the problematic joints generated by per-frame method from duplex Kinects. As a result, we develop an easy, cheap and fast approach that can improve the skeleton of human motion at an average speed of 5 ms per frame.

scholarly journals CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Zhanjun Hao ◽  
Yu Duan ◽  
Xiaochao Dang ◽  
Tong Zhang
Keyword(s):  
Real Time ◽  
Human Motion ◽  
Detection Accuracy ◽  
Behavior Recognition ◽  
Motion Recognition ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Motion Data ◽  
Indoor Scenes ◽  
Human Motion Recognition

WiFi indoor personnel behavior recognition has become the core technology of wireless network perception. However, the existing human behavior recognition methods have great challenges in terms of detection accuracy, intrusion, and complexity of operations. In this paper, we firstly analyze and summarize the existing human motion recognition schemes, and due to the existence of the problems in them, we propose a noninvasive, highly robust complex human motion recognition scheme based on Channel State Information (CSI), that is, CSI-HC, and the traditional Chinese martial art XingYiQuan is verified as a complex motion background. CSI-HC is divided into two phases: offline and online. In the offline phase, the human motion data are collected on the commercial Atheros NIC and a powerful denoising method is constructed by using the Butterworth low-pass filter and wavelet function to filter the outliers in the motion data. Then, through Restricted Boltzmann Machine (RBM) training and classification, we establish offline fingerprint information. In the online phase, SoftMax regression is used to correct the RBM classification to process the motion data collected in real time and the processed real-time data are matched with the offline fingerprint information. On this basis, the recognition of a complex human motion is realized. Finally, through repeated experiments in three classical indoor scenes, the parameter setting and user diversity affecting the accuracy of motion recognition are analyzed and the robustness of CSI-HC is detected. In addition, the performance of the proposed method is compared with that of the existing motion recognition methods. The experimental results show that the average motion recognition rate of CSI-HC in three classic indoor scenes reaches 85.4%, in terms of motion complexity and indoor recognition accuracy. Compared with other algorithms, it has higher stability and robustness.

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.

Sign in / Sign up

Export Citation Format

Share Document