Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks

Arifoǧlu, Damla; Bouchachia, Abdelhamid

doi:10.1016/j.procs.2017.06.121

Cited by 147 publications

(86 citation statements)

References 20 publications

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“…Bouchachia et al [24] proposed a Recurrent Neural Network (RNN) model to address the problem of activity recognition and abnormal behavior detection for elderly people with dementia. Their proposed method suffered from the lack of data in the context of dementia.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

Zerkouk

Chikhaoui

2020

Sensors

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The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal behaviors observed during activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder-CNN-LSTM for identifying and accurately predicting the abnormal behaviors of elderly people. The temporal information and spatial sequences collected over time are used to generate models, which can be fitted to the training data and the fitted model can be used to make a prediction. We present an experimental evaluation of these models performance in identifying and predicting elderly persons abnormal behaviors in smart homes, via extensive testing on two public data sets, taking into account different models architectures and tuning the hyperparameters for each model. The performance evaluation is focused on accuracy measure.However, dependent persons are often exposed to problems of various types that may cause them to perform activities of daily living incorrectly. Detecting abnormal behaviors is thus of great importance for dependent people, in order to ensure that activities are performed correctly without errors [5]. This will also ensure their safety and well-being.Detecting an anomaly in a person's activities of daily living (ADL) is usually done by detecting nonconformities with their usual ADL patterns. Various authors have used classical machine learning algorithms to achieve this [6,7]. Tele-health care requires systems with high accuracy, low computational time and minimal user intervention because data are becoming larger and more complex [8]. Deep learning architectures provide a way to automatically extract useful and meaningful spatial and temporal features from raw data without the need for data labeling, which is time consuming, complex and error prone [9,10]. This makes deep learning models easily generalizable to different contexts. LSTM is a powerful deep learning model for sequence prediction and anomaly detection in sequential data [11]. The LSTM model is able to extract temporal features with long-term relationships. CNN has powerful spatial feature extraction capabilities and the ability to detect abnormality, which is considered as a classification problem. The advantage in the use of CNN with LSTM is to combine their capabilities in terms of spatial and temporal features extraction. Autoencoder learns a compressed input representation, which is recommended for the high dimensional data collected from smart home. Therefore, the motivation to select our deep learning models is due to their high performance accuracy in terms of abnormal behavior detection in different research areas [12,13].This propert...

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Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

Zerkouk

Chikhaoui

2020

Sensors

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“…In-home automatic assessment of cognitive decline has been the subject of many machine learning approaches such as Support Vector Machines (SVMs) and Naïve Bayes (NB) [6], Restricted Boltzmann Machines (RBMs) [7], Hidden Markov Models (HMMs) [8], Random Forests [9] and Recurrent Neural Networks (RNNs) [10]. In [10], the authors exploit RNNs to detect abnormal behavior of dementia sufferers in a daily living scenario. The abnormal behavior are flagged based on their classification confidence values.…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning based cognitive status assessment studies rely on activity recognition techniques. These methods first learn what is normal from training data and then flag the abnormal activity based on classification confidence values [6]- [10]. They require training data to be manually annotated, which is extremely hard and a time-consuming task.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Behaviour Detection for Dementia Sufferers via Transfer Learning and Recursive Auto-Encoders

Arifoǧlu

Bouchachia

2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Cognitive impairment is one of the crucial problems elderly people face. Tracking their daily life activities and detecting early indicators of cognitive decline would be necessary for further diagnosis. Depending on the decline magnitude, monitoring may need to be done over long periods of time to detect abnormal behaviour. In the absence of training data, it would be helpful to learn the normal behaviour and daily life patterns of a (cognitively) healthy person and use them as a basis for tracking other patients. In this paper, we propose to investigate Recursive Auto-Encoders (RAE)-based transfer learning to cope with the problem of scarcity of data in the context of abnormal behaviour detection. We present a method for generating synthetic data to reflect on some behavior of people with dementia. An RAE model is trained on data of a healthy person in a source household. Then, the resulting RAE is used to detect abnormal behavior in a target house. To evaluate the proposed approach, we compare the results with the-state-ofthe-art supervised methods. The results indicate that transfer learning is promising when there is lack of training data.

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“…There is a host of literature relating to HAR [18], there are indoor monitoring studies with AI, e.g. [19], and studies of wandering trajectories, e.g. [20].…”

Section: Unconventional Deep Learningmentioning

confidence: 99%

Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia

Williams

Ware

Müller

2019

Lecture Notes in Computer Science

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Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks

Cited by 147 publications

References 20 publications

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models

Abnormal Behaviour Detection for Dementia Sufferers via Transfer Learning and Recursive Auto-Encoders

Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia

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