Abnormal human activity recognition using SVM based approach

Palaniappan, Adithyan; Bhargavi, R.; Vaidehi, V.

doi:10.1109/icrtit.2012.6206829

Cited by 52 publications

(21 citation statements)

References 16 publications

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“…DT also is also a typical algorithm widely used in healthcare applications, especially for physical activity monitoring related disease diagnosis and treatments [108]. Likewise, SVM is capable to address the issue of either wearable sensors for precisely observing abnormal activities [121], or static postures detection for healthcare measurement [102], as well as in clinical outcome classification and prediction (e.g., disease diagnosis) [31]. In recent years, deep learning has gradually became a popular method in medical diagnosis and health state classifications due to its high efficiency and accuracy.…”

Section: ) Supervised Learning Methodsmentioning

confidence: 99%

Advanced internet of things for personalised healthcare systems: A survey

Yang

Min

et al. 2017

Pervasive and Mobile Computing

347

177

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Section: ) Supervised Learning Methodsmentioning

confidence: 99%

Advanced internet of things for personalised healthcare systems: A survey

Yang

Min

et al. 2017

Pervasive and Mobile Computing

347

177

View full text Add to dashboard Cite

show abstract

“…Palaniappan et al [22] were interested in detecting abnormal activities of individuals by ruling out all possible normal activities. They define abnormal activities as randomly occurring, unexpected events.…”

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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“…Palaniappan et al [15] interested in detecting abnormal activities of the individuals by ruling out all possible normal activities. Authors define abnormal activities as unexpected events that occur in random manner.…”

Section: Related Workmentioning

confidence: 99%

Long Short Term Memory Based Model for Abnormal Behavior Prediction in Elderly Persons

Zerkouk

Chikhaoui

2019

Lecture Notes in Computer Science

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Smart home refers to the independency and comfort that are ensured by remote monitoring and assistive services. Assisting an elderly person requires identifying and accurately predicting his/her normal and abnormal behaviors. Abnormal behaviors observed during the completion of 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 propose a method, based on long short-term memory recurrent neural networks (LSTM), to automatically predicting an elderly person's abnormal behaviors. Our method allows to model the temporal information expressed in the long sequences collected over time. Our study aims to evaluate the performance of LSTM on identifying and predicting elderly persons abnormal behaviors in smart homes. We experimentally demonstrated, through extensive experiments using a dataset, the suitability and performance of the proposed method in predicting abnormal behaviors with high accuracy. We also demonstrated the superiority of the proposed method compared to the existing state-of-the-art methods.

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Abnormal human activity recognition using SVM based approach

Cited by 52 publications

References 16 publications

Advanced internet of things for personalised healthcare systems: A survey

Advanced internet of things for personalised healthcare systems: A survey

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

Long Short Term Memory Based Model for Abnormal Behavior Prediction in Elderly Persons

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