Activity recognition from interactions with objects using dynamic Bayesian network

Tomohito, Inomata; Naya, Futoshi; Kuwahara, Noriaki; Hattori, Fumio; Kiyokawa, Kiyoshi

doi:10.1145/1538864.1538871

Cited by 26 publications

(21 citation statements)

References 2 publications

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“…But we cannot ensure that the best results are still obtained from a BayesNet when the the number of classes is larger or the feature changes. Because the scoring function in a BayesNet is the minimal description length (MDL) [13]. A larger class will result in a larger error for the MDL.…”

Section: Analysis and Discussionmentioning

confidence: 99%

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Activity Recognition Based on an Accelerometer in a Smartphone Using an FFT-Based New Feature and Fusion Methods

Xue

Jin

2014

IEICE Trans. Inf. & Syst.

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SUMMARYWith the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time-or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

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Section: Analysis and Discussionmentioning

confidence: 99%

“…Second, we choose a classifier, which may result in great classification performance according to previous works [5], [13], to train the feature set fused with the rankings. Through a simple traversal of the different rankings, we obtain the optimized fusion ranking suitable for the specific classifier.…”

Section: ) Minimal Distance Optimization (Mdo)mentioning

confidence: 99%

Activity Recognition Based on an Accelerometer in a Smartphone Using an FFT-Based New Feature and Fusion Methods

Xue

Jin

2014

IEICE Trans. Inf. & Syst.

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“…Moreover, if there are many subjects in the monitored environment, it becomes hard to infer who the subject of the action was. The authors of [53] use Dynamic Bayesian Network (DBN) framework for AR from interactions with objects. In their setup, a nurse performs a drip injection procedure and the purpose of the system is to prevent the cause of medical accidents and incidents.…”

Section: Object-based Armentioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply di↵erent AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a di↵erent angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for homebased AR algorithms, it highlights the problem of technology-driven cycle of development in this area. IntroductionIn the last two decades sensors have become cheaper, smaller and widely available, residing at the edge of the Internet. Some such examples are wearable personal activity (PA) trackers (e.g. Fitbit, Nike+ FuelBand, etc.). However, the available commercial o↵-the-shelf (COTS) sensors are only capable of 'sensing' a small subset of user activities -mostly outdoor sport activities (type of activity, distance covered, time taken, etc.) and estimation of additional information such as energy expenditure (either kcal or self-crafted metrics e.g. Nike's fuel-points). However, a large part of our lives, and increasingly so in the advanced age, is spent in the home, yet very little is known about our activities and behaviour in there.We are surrounded by a multitude of sensing devices and Mark Weiser's vision of ubiquitous computing [120] is starting to materialise in the advances made in embedded networked systems currently addressed as the Internet of Things (IoT). The significant increase in devices streaming low-level information over the Web presents many new challenges. Whilst many researchers present this as a big data challenge, we believe that many of the environments and applications will require to justify the value and process relatively small data, making this a two-faceted problem requiring to consider the highly distributed, non-interoperable, small and relatively "lonely" data. E cient and accurate activity recognition (AR) algorithms are needed in order to make sense of this data and provide useful/actionable information and services in the human...

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“…The supervised activity recognition methods range from simple methods such as naive Bayes [2] based on sensor events independence assumption, to more recent and sophisticated methods such as conditional random fields [17] that model the sensor events as probabilistic sequences. Other notable supervised methods include decision trees [14], Markov models [13], and dynamic Bayes networks [12]. There are a number of problems with the supervised approach.…”

Section: Introductionmentioning

confidence: 99%

Mining and monitoring patterns of daily routines for assisted living in real world settings

Rashidi

Cook

2010

Proceedings of the 1st ACM International Health Informatics Symposium

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In this paper we demonstrate a fully automated approach for discovering and monitoring patterns of daily activities. Discovering patterns of daily activities and tracking them can provide unprecedented opportunities for health monitoring and assisted living applications, especially for elderly and people with memory deficits. In contrast to most previous systems that rely on either pre-selected activities or labeled data for tracking and monitoring, we use an automated approach for activity discovery and recognition. We present a mining method that is able to find natural activity patterns in real life data, as well as variations of such patterns. We will also show how the discovered patterns can be recognized and monitored by our recognition component. In addition, we provide a visualization component to help the care-givers to better understand the activity patterns and their variations. To validate our algorithms, we use the data collected in two smart apartments.

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Activity recognition from interactions with objects using dynamic Bayesian network

Cited by 26 publications

References 2 publications

Activity Recognition Based on an Accelerometer in a Smartphone Using an FFT-Based New Feature and Fusion Methods

Activity Recognition Based on an Accelerometer in a Smartphone Using an FFT-Based New Feature and Fusion Methods

Classification and suitability of sensing technologies for activity recognition

Mining and monitoring patterns of daily routines for assisted living in real world settings

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