Human Intention Detection and Activity Support System for Ubiquitous Sensor Room

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

doi:10.20965/jrm.2004.p0545

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Many such systems are based on technology related to Intelligent Environments [3][4][5][6]. In these environments, the observation of human activity or behavior is a key in supporting residents, and there have been many trials that uses accelerometers, vibration sensors, magnetic switches, electric current sensors, or infrared detectors [7][8][9][10][11]. In our research, we assume a system using some pyroelectric sensors that detect an elderly resident's motion ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Life Pattern Estimation of the Elderly Based on Accumulated Activity Data and its Application to Anomaly Detection

Mori¹,

Ishino²,

Noguchi³

et al. 2012

J. Robot. Mechatron.

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A life pattern estimation method and its application to anomaly detection of a single elderly are proposed. Our observation system deploys some pyroelectric sensors in an elderly’s house and monitors and measures activities 24 hours a day to grasp residents’ life patterns. Activity data is successively forwarded to the nurse operation center and displayed to nurses at the center. The system reports status related to anomalies together with the basic activities of elderly residents to the nurses, who decide whether recent accumulated data expresses an anomaly or not based on suggestions from the system. In the system, residents whose lifestyle features resemble each other are categorized into the same group. Anomalies that occurred in the past are shared in the group and utilized in an anomaly detection algorithm. This algorithm is based on an “anomaly score.” The score is figured out by utilizing the activeness of the house’s elderly resident. This activeness is approximately proportional to the frequency of sensor response within one minute. The anomaly score is calculated from the difference between activeness in the present and in the past averaged over the long term. The score is thus positive if activeness in the present is greater than the average in the past, and the score is negative if the value in the present is less than average. If the score exceeds a certain threshold, it means that an anomaly event has occurred. An activity estimation algorithm is also developed that estimates the basic activities of residents such as getting up in the morning, or going out. The estimation is also shown to nurses with the anomaly score of residents. Nurses can understand the condition of elderly residents’ health by combining the information and planning the most appropriate way to respond.

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Section: Introductionmentioning

confidence: 99%

Life Pattern Estimation of the Elderly Based on Accumulated Activity Data and its Application to Anomaly Detection

Mori¹,

Ishino²,

Noguchi³

et al. 2012

J. Robot. Mechatron.

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“…Strategic compliance, which has been inspired by the concepts of intelligent environments and structuralizing of robot's working environments [9,10], should be a concept not only to help enhance the functions of robots but also improve the quality of services provided by robots by streamlining working environments and schemes for robots. 1 Such flexibilities in strategy can only take effect when it is combined with mechanical flexibilities of robots, and making mechanical compliance is one of the major technologies we address in this research.…”

Section: Introductionmentioning

confidence: 99%

Home-Use Object Transfer/Storage Robot System with Compliant Strategy and Mechanism (Commodities Management and its Extended Application of Daily Life Support for the Elderly)

Fukui¹,

Mori²,

Sato³

2011

J. Robot. Mechatron.

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This research describes the strategy and mechanism design methodology to realize a robot system that transfers and stores daily use objects in our living space. Unlike industrial applications, there are three specific problems in the home application: (1) variation of living environment, (2) diversity of daily use objects, (3) dispersion of human activity. First, we presented a concept of strategic compliance as a basic solution for these problems and extracted three fundamental functions (regulation, assist/enforce, and navigation) for the strategy. Second, we aimed to realize a robust robot motion by introducing adequate mechanisms which are suitable for the strategy. The mechanisms are; (1) passive mechanical compliance and (2) object constraint methodology with “caging.” As an actual prototype system, a home-use logistical support robot system implemented with those strategies and mechanisms is constructed. By experiments, validity of the presented methodology was confirmed. In addition to the domestic logistical application, we considered applying the proposed system to the elderly people support, and developed prototypes of supporting instruments; lavatory and refrigerator containers. The basic design of the instruments is also introduced.

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“…To recognize various activities accurately and reliably, an appropriate activity model has to be deployed to relate low-level features to high-level concepts. Most existing work focuses on recognizing activities of a single user [1,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. However, in a living space (Le., typically a home environment), there are usually multiple inhabitants.…”

Section: Introductionmentioning

confidence: 99%

Mining Emerging Patterns for recognizing activities of multiple users in pervasive computing

Wang

et al. 2009

Proceedings of the 6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Understanding and recognizing human activities from sensor readings is an important task in pervasive computing. Existing work on activity recognition mainly focuses on recognizing activities for a single user in a smart home environment. However, in real life, there are often multiple inhabitants live in such an environment. Recognizing activities of not only a single user, but also multiple users is essential to the development of practical context-aware applications in pervasive computing. In this paper, we investigate the fundamental problem of recognizing activities for multiple users from sensor readings in a home environment, and propose a novel pattern mining approach to recognize both single-user and multi-user activities in a unified solution. We exploit Emerging Patterna type of knowledge pattern that describes significant changes between classes of data -for constructing our activity models, and propose an Emerging Pattern based Multi-user Activity Recognizer (epMAR) to recognize both single-user and multiuser activities. We conduct our empirical studies by collecting real-world activity traces done by two volunteers over a period of two weeks in a smart home environment, and analyze the performance in detail with respect to various activity cases in a multi-user scenario. Our experimental results demonstrate that our epMAR recognizer achieves an average accuracy of 89.72% for all the activity cases.

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Human Intention Detection and Activity Support System for Ubiquitous Sensor Room

Cited by 8 publications

References 7 publications

Life Pattern Estimation of the Elderly Based on Accumulated Activity Data and its Application to Anomaly Detection

Life Pattern Estimation of the Elderly Based on Accumulated Activity Data and its Application to Anomaly Detection

Home-Use Object Transfer/Storage Robot System with Compliant Strategy and Mechanism (Commodities Management and its Extended Application of Daily Life Support for the Elderly)

Mining Emerging Patterns for recognizing activities of multiple users in pervasive computing

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