Context aware inactivity recognition for visual fall detection

Jansen, Bart; Deklerck, Rudi

doi:10.1109/pcthealth.2006.361657

Cited by 85 publications

(28 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…6 The proposed strategy may consider additional tasks and responsibilities like reliable and quick human fall detection (Jansen and Deklerck, 2007). Furthermore, the ability to detect, track, and identify wheelchair and pets may also be required.…”

Section: Requirement Specificationsmentioning

confidence: 99%

Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors

Ibrahim

Wong

Moore

et al. 2017

IJCSE

View full text Add to dashboard Cite

Abstract:Research concern has been directed toward automatic identification and tracking of people within the home environment to support smart home services such as care services for elderly and disadvantaged people to enable and prolong their independent living. Although various approaches have been proposed to deal with this problem, solutions still remain untackled for various reasons (e.g., user acceptance). The aim of this paper is to develop and implement an advanced approach that is capable to accurately detect, identify, and track people within appropriate smart home environment to be used as infrastructure for various application domains. A novel multimodal approach for non-tagged human identification and tracking within smart home environment is proposed. The proposed approach combines pyroelectric infrared (PIR) sensors and floor pressure through unique designed integration strategy aiming to merge the advantages of the two sensor types and overcome their limitations.Keywords: smart home; multimodal approach; pyroelectric infrared; PIR; sensor and smart floor.Reference to this paper should be made as follows: Al-Naimi, I., Wong, C.B., Moore, P. and Chen, X. (2017) 'Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors', Int.

show abstract

Section: Requirement Specificationsmentioning

confidence: 99%

Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors

Ibrahim

Wong

Moore

et al. 2017

IJCSE

View full text Add to dashboard Cite

show abstract

“…All these environments may benefit from the use of CHROMUBE. Some examples of environments with heterogeneous sensors are cameras [22,16], wearable sensors [20], unobstrusive sensors (i.e. presence, open door, pressure sensors) [14,25], sensors integrated in mobile phones or PDAs [29,40], etc.…”

Section: Related Workmentioning

confidence: 99%

Generation of human computational models with machine learning

Campuzano

García-Valverde

Botía

et al. 2015

Information Sciences

View full text Add to dashboard Cite

Services in smart environments pursue to increase the quality of people's lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world test-ing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodol-ogy guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton's structure and the prob-ability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models.

show abstract

“…Concerning the second concept class of off-body video monitoring, while limb recognition has demonstrated a high degree of accuracy [7,36,37,47,49,50,54], despite a somewhat lack of experimental validation specifically for athome fall detection applications [47]. Aside from computing power considerations surrounding video processing which would require off-body equipment [49], limb recognition using video feeds has been dismissed time after time due to privacy issues surrounding the continuous video recording of the patient [55,56]; studies based on such a system have been rejected even by focus groups due to being too intrusive.…”

Section: Off-body Fall Detection Schemesmentioning

confidence: 99%

Wireless Body Area Network Node Localization Using Small-Scale Spatial Information

González-Valenzuela

Leung

2013

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Deploying wireless body area networks (WBANs) in the long-term at-home monitoring of a patient's physiological and bio-kinetic conditions has become increasingly prevalent. However, such WBANs do not typically incorporate mechanisms to detect and correct for the possibility of accidentally switching up wearable wireless sensor nodes (W 2 SNs), where a node assigned to one limb is placed on another, and vice-versa, leading to possible incorrect prognoses from interpreting the data.In this thesis, we present a new scheme to automatically identify and To quantify and validate the accuracy, consistency and reliability of this localization scheme, a statistical analysis on a set of commercially-available air pressure sensors and an experimental prototype WBAN is conducted to examine the scheme's performance and limitations. This study has verified that this approach is indeed capable of distinguishing between positions indicative of expected separation between different limbs of the patient's body.Based on a 60cm separation between nodes, the statistical analysis consistently exceeded 95% accuracy within the confidence interval (CI), demonstrating great promise for incorporation into commercial WBANs.We also present and experimentally demonstrate an enhancement aiming

show abstract

Context aware inactivity recognition for visual fall detection

Cited by 85 publications

References 8 publications

Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors

Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors

Generation of human computational models with machine learning

Wireless Body Area Network Node Localization Using Small-Scale Spatial Information

Contact Info

Product

Resources

About