Human intention detection and activity support system for ubiquitous autonomy

Noguchi, Katsunori; Somwong, P.; Matsubara, Takashi; Nakauchi, Yasushi

doi:10.1109/cira.2003.1222300

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…This situation necessitates the sensing of his/her intent to modify the nominal trajectory accordingly. Work on intention sensing is reported by many researchers including Colgate et al [88], Noguchi et al [89], and Wasson et al [90]. Another possibility is to install sensors in the environment to make the space around the robot intelligent.…”

Section: Future Research Directionsmentioning

confidence: 96%

Safe physical human robot interaction-past, present and future

2008

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When a robot physically interacts with a human user, the requirements should be drastically changed. The most important requirement is the safety of the human user in the sense that robot should not harm the human in any situation. During the last few years, research has been focused on various aspects of safe physical human robot interaction. This paper provides a review of the work on safe physical interaction of robotic systems sharing their workspace with human users (especially elderly people). Three distinct areas of research are identified: interaction safety assessment, interaction safety through design, and interaction safety through planning and control. The paper then highlights the current challenges and available technologies and points out future research directions for realization of a safe and dependable robotic system for human users.

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Section: Future Research Directionsmentioning

confidence: 96%

Safe physical human robot interaction-past, present and future

2008

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“…Schrempf et al [21] presented a different approach to creating a Bayesian network for estimating intentions without a learning framework. Two other studies [22,23] also used HMM to recognize human behavior via an online probabilistic algorithm, while others [24,25] designed an experimental room for detection of human intention. They used an ID4-based leaning system to recognize the human intention, complete with sensors for the estimation of human position and sounds in the environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating and reshaping human intention via human--robot interaction

Durdu¹,

Erkmen²,

Erkmen³

2016

Turk J Elec Eng & Comp Sci

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Human-robot interaction (HRI) is studied in two important research areas, intention estimation and intention reshaping. Although there are many studies in the literature that define human intention, new research examines the reshaping of human intentions by using robots in HRI. In this paper, 2 different robot movements are tested in a real environment in order to reshape current human intention. The hidden Markov model (HMM) is used to estimate human intention in our intelligent robotic system. The algorithmic design of the system comprises 2 parts: the first part tracks the moving objects in the environment, and the second part estimates human intention and reshapes the estimated current human intention by using intelligent robots. In the first part, a feature vector consisting of the headings of the human posture and the locations of the humans and robots is created by using video processing techniques. The second part is related to estimating the current intention of a human participant via HMM models and to reshaping the current intention into another intention. The system is tested in a real experimental environment including humans and robots, and the results in the recorded videos are given at the end of the paper.

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“…Another technique that can be used to determine human activities is a decision tree [21]. With this method, there are several possible activities that can occur in one given place.…”

Section: Bayesian Networkmentioning

confidence: 99%

A Survey on Context-Aware Sensing for Body Sensor Networks

Korel

Koo²

2010

WSN

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Context awareness in Body Sensor Networks (BSNs) has the significance of associating physiological user activity and the environment to the sensed signals of the user. The context information derived from a BSN can be used in pervasive healthcare monitoring for relating importance to events and specifically for accurate episode detection. In this paper, we address the issue of context-aware sensing in BSNs, and survey different techniques for deducing context awareness

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Human intention detection and activity support system for ubiquitous autonomy

Cited by 10 publications

References 4 publications

Safe physical human robot interaction-past, present and future

Safe physical human robot interaction-past, present and future

Estimating and reshaping human intention via human--robot interaction

A Survey on Context-Aware Sensing for Body Sensor Networks

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