Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

Yu, Tong; Chen, Rong; Gao, Jian

doi:10.3390/e17031358

Cited by 19 publications

(14 citation statements)

References 34 publications

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“…In-home automatic assessment of cognitive decline has been the subject of many studies [16,17,6,7,18,19,20]. Many machine learning approaches such as SVMs and Naïve Bayes methods [21,22], Restricted Boltzmann Machines (RBMs) [19] and Markov Logic Networks [7,18,20], Hidden Markov Models (HMMs) [14], Random Forest methods [15], and Hidden Conditional Random Fields [23] have been exploited.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks

Arifoǧlu

Bouchachia

2019

Artificial Intelligence in Medicine

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In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people may get dependent on caregivers to complete daily life tasks. Detecting the early indicators of dementia before it gets worsen and warning the caregivers and medical doctors would be helpful for further diagnosis. In this paper, the problem of activity recognition and abnormal behaviour detection is investigated for elderly people with dementia. First of all, the paper presents a methodology for generating synthetic data reflecting on some behavioural difficulties of people with dementia given the difficulty of obtaining real-world data. Secondly, the paper explores Convolutional Neural Networks (CNNs) to model patterns in activity sequences and detect abnormal behaviour related to dementia. Activity recognition is considered as a sequence labelling problem, while abnormal behaviour is flagged based on the deviation from normal patterns. Moreover, the performance of CNNs is compared against the state-of-art methods such as Naïve Bayes (NB), Hidden Markov Models (HMMs), Hidden Semi-Markov Models (HSMM), Conditional Random Fields (CRFs). The results obtained indicate that CNNs are competitive with those state-of-art methods.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks

Arifoǧlu

Bouchachia

2019

Artificial Intelligence in Medicine

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“…A secure smart home can detect such a situation and alert the resident in order to ensure that the home is safe and to assist the resident if needed in completing a task. Tong et al [120] tackle this problem using hidden state conditional random fields (HCRF). A HCRF can be used to compare an observed activity with a database of activities performed normally.…”

Section: Detecting and Assessing Threatsmentioning

confidence: 99%

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Dahmen

Cook

Wang

et al. 2017

J Reliable Intell Environ

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Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings. Recent smart home applications are focused on activity recognition, health monitoring, and automation. In this paper, we take a look at another important role for smart homes: security. We first explore the numerous ways smart homes can and do provide protection for their residents. Next, we provide a comparative analysis of the alternative tools and research that has been developed for this purpose. We investigate not only existing commercial products that have been introduced but also discuss the numerous research that has been focused on detecting and identifying potential threats. Finally, we close with open challenges and ideas for future research that will keep individuals secure and healthy while in their own homes.

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“…The approaches to activity recognition that have been developed over the last ten years range from logic-based approaches to probabilistic machine learning approaches [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]. Although the reported successes are promising, determining the correct activity from sensor data alone is often impossible, since sensors can only provide very limited information and human behaviours are inherently complex.…”

Section: Introductionmentioning

confidence: 99%

Towards a Theory of Space for Activity Recognition in Smart Environments Based on Rough Sets

Guesgen

2015

2015 International Conference on Intelligent Environments

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This paper investigates how spatial information can be used to improve the process of recognising activities in smart homes. The spatial information is obtained through spatial markers, like motion sensors for instance. Since different markers may be involved in different occurrences of the same type of behaviour, there is a need for being able to express vagueness, which in our approach is achieved by using rough sets rather than standard sets to denote the space in which an activity occurs.

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Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

Cited by 19 publications

References 34 publications

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks

Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Towards a Theory of Space for Activity Recognition in Smart Environments Based on Rough Sets

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