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Mitsuichi Hiratsuka

dblp:23/5407 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Health and well-being technologies · 77% Wearable and physiological sensing · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing › motion sensing
motion monitoring
0.012000
Detection of Human Mistakes and Misperception for Human Perceptive Augmentation: Behavior Monitoring Using Hybrid Hidden Markov Models · ICRA 2000

Methods — techniques the papers use, named apart from their topics

stochastic process modeling · 0.0hybrid hidden markov model · 0.0
YearPublicationVenuePosition
2000 Detection of Human Mistakes and Misperception for Human Perceptive Augmentation: Behavior Monitoring Using Hybrid Hidden Markov Models
abstract
A method of detecting human mistakes and misperception for assisting humans in operating complex systems is presented. The method is developed in the context of operating iPASS (Integrative Physical Assists and Seamless Services) system which provides a patient diverse physical aids without changing equipment. The system can serve as a bed, a walker, a stand-up and seating assistance, as well as a wheelchair. iPASS needs special care for its operations because human mistakes and misperception might lead to serious consequences such as injury and costly repair. In order to detect human mistakes and misperception in a human motion, it is important to monitor a human motion and to understand human intention. In this paper, processes of human perception and motion are treated as stochastic processes, and they are modeled by using hybrid hidden Markov models. Finally, an application of this method to stand-up assistance for iPASS is described.
Mitsuichi Hiratsuka, H. Harry Asada
ICRA1