Behavior Hierarchy-Based Affordance Map for Recognition of Human Intention and Its Application to Human–Robot Interaction

Han, Ji-Hyeong; Lee, Seung-Jae; Kim, Jong-Hwan

doi:10.1109/thms.2016.2558539

Cited by 19 publications

(6 citation statements)

References 26 publications

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“…Yanhu et al [55] described the visual behavior recognition deeply based on the evolution of database. Han et al [56] used human-computer interaction to study human behavior and human intention recognition.…”

Section: Discussionmentioning

confidence: 99%

Human Motion Target Recognition Using Convolutional Neural Network and Global Constraint Block Matching

Liu

2020

IEEE Access

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The traditional human behavior recognition algorithm is easy to ignore the spatial constraint problem of feature blocks, which leads to poor recognition effect and low correct rate. Therefore, we proposed a human motion target recognition algorithm based on Convolution Neural Network (referred to as the ''CNN'') and global constraint block matching. First, key frames of the human motion video were extracted, second, the local feature and global feature of key frames were analyzed, and CNN was used to perform feature fusion, then, according to the result of the feature fusion, a feature block was formed and the closest matching feature block is obtained, using the definition of spatial constraint, we considered the spatial data of human motion in the vertical direction, calculates the spatial constraint weight, and further completes the matching. Finally, the score of matching block and the spatial constraint weight were calculated, and the human motion targets are recognized based on the cumulative score. The experimental results show that the proposed algorithm has a high key frame extraction accuracy of more than 90% and less time consumed in feature fusion, high matching accuracy of feature blocks of more than 80%, and high feature blocks, the F-measure of human behavior recognition is 0.95 on average, and the overall recognition performance is good.INDEX TERMS Convolutional neural network, global constraint block, feature block, spatial constraints, matching, human motion target, recognition.

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Section: Discussionmentioning

confidence: 99%

Human Motion Target Recognition Using Convolutional Neural Network and Global Constraint Block Matching

Liu

2020

IEEE Access

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“…Shirley and Evans [6] get the malicious judgment of software behavior according to the matching degree of user's operation intention and software behavior. In the field of artificial intelligence [18], the human intention identified by agents is that of formal representation. These studies provide a theoretical basis for our malware intention modeling.…”

Section: Related Workmentioning

confidence: 99%

Behavior Intention Derivation of Android Malware Using Ontology Inference

Jiao

Liu

Chen

et al. 2018

Journal of Electrical and Computer Engineering

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Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization) is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.

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“…In order to make pHRI more effective, we need robots to anticipate the human partner's intentions and act accordingly [7]. In this regard, one of the sensory channels that flourish in nowadays is haptics [8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Human Force Scaling via Admittance Control for Physical Human-Robot Interaction

Hamad

Aydın

Başdoğan

2021

IEEE Trans. Haptics

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The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts' reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.

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Behavior Hierarchy-Based Affordance Map for Recognition of Human Intention and Its Application to Human–Robot Interaction

Cited by 19 publications

References 26 publications

Human Motion Target Recognition Using Convolutional Neural Network and Global Constraint Block Matching

Human Motion Target Recognition Using Convolutional Neural Network and Global Constraint Block Matching

Behavior Intention Derivation of Android Malware Using Ontology Inference

Adaptive Human Force Scaling via Admittance Control for Physical Human-Robot Interaction

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