Intelligent video surveillance for monitoring fall detection of elderly in home environments

Foroughi, Homa; Aski, Baharak Shakeri; Pourreza, Hamid Reza

doi:10.1109/iccitechn.2008.4803020

Cited by 183 publications

(117 citation statements)

References 10 publications

Supporting

Mentioning

117

Contrasting

Order By: Relevance

“…A T [37], [100], [62], [74], [56], [130], [2], [45], [60], [47], [31] Kin [90]; Cam [29], [54], [75], [41]; grey Cam [49] Evidence found in the literature suggest that for the recognition of all the activities presented in the above table, apart from Using Elevator, Walk Through Doors and Leaving the House, a single tri-axial accelerometer is sufficient. The main advantage of such wearable technology is the fact that it is widely available and relatively cheap.…”

Section: Locomotion Transitions and Posturementioning

confidence: 99%

See 1 more Smart Citation

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

View full text Add to dashboard Cite

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...

show abstract

Section: Locomotion Transitions and Posturementioning

confidence: 99%

“…A T [74], [100], [110], [62], [130], [2], [45], [60], [47], [131], [31], [70] Kin [8], [90]; Cam [29], [54], [75], [41]; grey Cam [49]; Cam+ 2xA T [111] Lying(0)…”

Section: Locomotion Transitions and Posturementioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

View full text Add to dashboard Cite

show abstract

“…For example in paper [4] a video surveillance system is proposed, in paper [5] a ring sensor is introduced, and similarly another type of wear sensor "alarm button on the wrist" be used to help elderly in emergency. But because of privacy issues, visual tracking is not attractive and because of the elderly have their own psychological and physiological problems, such as memory disorder (forget to wear sensor some day or forget where the sensor is), action obstacles (cannot operate an alarm device in time or in extreme situation -unconsciousness -even cannot press an alarm button on the wrist).…”

Section: Contribution To Sustainability and Technological Innovationmentioning

confidence: 99%

“…An intelligent, dynamically facility introduced in paper [3], which helps the elderly user to browse the internet. In paper [4] a video surveillance system is proposed. It aimed to fall detection of the elderly.…”

Section: Introductionmentioning

confidence: 99%

Daily Activity Model for Ambient Assisted Living

Yin

Bruckner

2011

Technological Innovation for Sustainability

View full text Add to dashboard Cite

Abstract. We propose a novel way for ambient assisted living: a system that with motion detector to observe the daily activities of the elderly, build the daily activity model of the user. In case of unusual activities the system send alarm signal to caregiver. The problems with this approach to build such a model: firstly, the activities of the user are random and dynamic distributed, that means the related data is dynamically and with huge count. Secondly, the difficulty and computational burden to get character parameters of hidden Markov model with many "states". To deal with the first problem we take advantage of an easy filter algorithm and translate the huge dynamical data to "state" data. Secondly according the limited output of distinct observation symbols per state, we reduced the work to research the observation symbol probability distribution. Furthermore the forward algorithm used to calculate the probability of observed sequence according the build model.

show abstract

“…Video-based system for older people surveillance is growing as a research field (particularly frailty detection) [9][10][11][12][13] , as it can provide data about a people interaction with their environment (e.g., time spent in zones and interaction with objects of interest). Applications are generally associated with detection of daily living activities (e.g., eating, dressing, walking), or the detection of (potentially) dangerous situations (e.g., older people falls).…”

Section: Introductionmentioning

confidence: 99%

Alzheimers Patient Activity Assessment Using Different Sensors

Crispim-Junior¹,

Joumier²,

Hsu³

et al. 2012

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

PurposeThe number of people requiring care, including Alzheimer patients, will grow while the number of people able to provide care will decrease. We focus on the development of medical, information and communication technologies for improved diagnosis and evaluation of dementia progression in early-stage Alzheimer disease (AD) patients. Method We compared several sensors (video and accelerometer) to assess elderly performance on instrumental activities of daily living (IADL) and gait tests in the clinical protocol developed and executed by the Memory Center of the Nice Hospital and the Department of Neurology at National Cheng Kung University Hospital, Taiwan. This clinical protocol defines a set of daily living tasks (e.g., preparing coffee, watching TV), and physical tests (e.g. a balance test), that could be realistic achieved in the designed observation room, and at the same time provide objective information about dementia symptoms. Previous works analyzed only accelerometer sensors for elderly gait analysis for dementia symptoms differentiation, while video-sensors were dedicated to ADL-detection. The comparison of several sensors could provide new evidence about patients' activities. The proposed systems used a constraint-based ontology to model and detect events based on the sensors data output. 2D-video stream data is converted to 3D-geometric information that is combined with a priori semantic information about the clinical scenario. The ontology language is declarative and intuitive (as it uses natural terms), allowing medical experts to easily define and modify the IADL and gait events models (using spatial, temporal, video-tracking and accelerometers data to describe events). The sensor system has been tested with 44 participants (healthy=21, AD=23). A stride detection algorithm was developed by the Taiwan team for the automatically acquisition of gait information using a triaxial accelerometer embedded in a wearable device. It acquires data about the participant locomotion (e.g., walking time, stride length, stride frequency). It was tested with 33 participants (healthy=17, AD=16), on a 40-meter walking test. Results & Discussion This monitoring system detected the full set of activities of the first part of the clinical protocol (e.g., balance test, repeated sequence of sitting-standing positions) with a detection rate of 96.9% to 100% (true positive rate).

show abstract

Intelligent video surveillance for monitoring fall detection of elderly in home environments

Cited by 183 publications

References 10 publications

Classification and suitability of sensing technologies for activity recognition

Classification and suitability of sensing technologies for activity recognition

Daily Activity Model for Ambient Assisted Living

Alzheimers Patient Activity Assessment Using Different Sensors

Contact Info

Product

Resources

About

Intelligent video surveillance for monitoring fall detection of elderly in home environments

Cited by 183 publications

References 10 publications

Classification and suitability of sensing technologies for activity recognition

Classification and suitability of sensing technologies for activity recognition

Daily Activity Model for Ambient Assisted Living

Alzheimers Patient Activity Assessment Using Different Sensors

Contact Info

Product

Resources

About

Alzheimers Patient Activity Assessment Using Different Sensors