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Long Short Term Memory Based Model for Abnormal Behavior Prediction in Elderly Persons
[chapter]
2019
Lecture Notes in Computer Science
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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 study aims to evaluate the performance of LSTM on identifying and predicting elderly persons abnormal behaviors in smart homes. ...
In this paper, we propose an LSTM model to identify and predict elderly people's abnormal behaviors. ...
doi:10.1007/978-3-030-32785-9_4
fatcat:yxo4fqw6bbadfgm7emq6ykyqd4
Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models
2020
Sensors
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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. ...
Figure 1 . 1 Long short term memory (LSTM) architecture development.
Figure 1 . 1 Long short term memory (LSTM) architecture development. ...
doi:10.3390/s20082359
pmid:32326349
fatcat:5t2vq5okszer7eyamx35ismzxe
Adaptive monitoring system for e-health smart homes
2018
Pervasive and Mobile Computing
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In order to evaluate our system, we use a Markovian model built for generating long term realistic scenarios. ...
They do not include optimized techniques which learn the patient's behavior for predicting future important changes. ...
We could combine several prediction models and evaluate their impact on the anticipation of risks and resources consumption. ...
doi:10.1016/j.pmcj.2017.11.001
fatcat:2onalchyizcclgnarqej5xlf6y
An AIoT-enabled Autonomous Dementia Monitoring System
[article]
2022
arXiv
pre-print
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Moreover, Long Short Term Memory (LSTM) is utilised to forecast the disease related activity trend of a patient. ...
An autonomous Artificial Internet of Things (AIoT) system for elderly dementia patients monitoring in a smart home is presented. ...
Among the various AI models for time-series prediction, Long Short-Term Memory (LSTM) [13] has been evaluated as being able to deliver a general high performance in different applications [14] [15 ...
arXiv:2207.00804v1
fatcat:cdiueaspwnfhfadpjt2lum5qma
An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
2020
Future Internet
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Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. ...
The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks ...
Sequence Prediction-Based Anomaly Detection We formulate abnormal behavior detection in a TCN-based multi-variate time-series prediction framework, where the prediction errors are used as anomaly scores ...
doi:10.3390/fi13010006
fatcat:7pjmnxz5gjebhn3t2o5aklqgca
Improving the Ambient Intelligence Living Using Deep Learning Classifier
2022
Computers Materials & Continua
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In this paper, we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks (BiLSTM) and convolutional neural network (CNN) classifier. ...
The convolutional neural network-bidirectional long-term and short-term memory (CNN-biLSTM) classifier with dimensional reduction isomap algorithm was then used to select ideal features. ...
The bidirectional long short-term memory (biLSTM) is similar to long short-term memory (LSTM) except for the recurrent block. ...
doi:10.32604/cmc.2022.027422
fatcat:b2hmpl5hlndpnk25zptkqympna
Addressing Mild Cognitive Impairment and Boosting Wellness for the Elderly through Personalized Remote Monitoring
2022
Healthcare
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Moreover, it includes multivariate AI-based predictive models that can detect the onset of MCI and its development towards dementia. ...
In this ageing worldwide context, early diagnosis and personalized assistance for MCI therefore become crucial. ...
Detection of abnormal behaviors on a short-term basis Patient's personal habits are not considered
Long-term analysis of activity data Model the patient's usual behavior from the activities performed ...
doi:10.3390/healthcare10071214
pmid:35885741
pmcid:PMC9325232
fatcat:6r55wkxfwbgjvo4zjaya4iwvsu
Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly
2019
Applied Sciences
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Elderly people frequently experience incidents of discomfort in their daily lives, including the deterioration of cognitive and memory abilities. ...
To provide auxiliary functions and ensure the safety of the elderly in daily living situations, we propose a projection-based augmented reality (PAR) system equipped with a deep-learning module. ...
(c) Daily Alarm The elderly's also experiences short-term and long-term memory problems, which result in forgetting important schedules or the state of the house. ...
doi:10.3390/app9183897
fatcat:xgg5xuh3nnglzcmctfdxxbga7a
Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition
2021
Sensors
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Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. ...
Due to the aging population, home care for the elderly has become very important. ...
Long Short-Term Memory (LSTM). ...
doi:10.3390/s21072520
pmid:33916549
fatcat:opznsq6jp5bnxkqygyjwcxy4fy
Diagnosis and treatment of depression and cognitive impairment in late life
2015
Annals of the New York Academy of Sciences
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Major depressive disorder in the elderly is accompanied by structural and functional abnormalities in the frontal lobes and their connections with limbic and striatal systems (see 10-11 for detailed reviews ...
Other executive functions, including planning and semantic organization, may account for observed deficits in select aspects of episodic memory and visuospatial abilities 16, 17 . ...
Half of the decks are disadvantageous (i.e, higher immediate rewards but long-term negative outcomes) and the other half is advantageous (i.e., lower immediate rewards but long-term positive outcomes). ...
doi:10.1111/nyas.12669
pmid:25655026
pmcid:PMC4447532
fatcat:c5y2jbv5yjcv3nhijvikh75tju
Activities Recognition, Anomaly Detection and Next Activity Prediction Based on Neural Networks in Smart Homes
2022
IEEE Access
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Finally, a cleaned sequence of consecutive activities is constructed and used by a long short-term memory (LSTM) algorithm to predict the next activity. ...
In this paper, we propose a unified deep learning model for monitoring elderly in execution of daily life activities such as eating, sleeping or taking medication. ...
Finally, the sequence is trained using long short-term memory (LSTM) algorithm and its model is used to predict the next activity. ...
doi:10.1109/access.2022.3157726
fatcat:26ez3nuvkjam5bzswhwmf62ikm
Integrated Sensing Devices for Disease Prevention and Health Alerts in Smart Homes
[chapter]
2022
Studies in Health Technology and Informatics
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This enables event detection and alerting for short-time as well as prediction and prevention for long-time monitoring. ...
We apply the bus-based scalable intelligent system to construct a hybrid topology for hierarchical multi-layer data fusion. ...
Taufeeque et al. applied long short-term memory (LSTM) networks for human pose estimation and support multi-camera systems as well as multiperson scenes. ...
doi:10.3233/shti220007
pmid:35593757
fatcat:mg3kwc4zrfcediyongmycath4i
Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review
2022
Computers
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In order to provide more personalized and advanced functions in smart home services, studies on deep learning applications are becoming more frequent, and deep learning is acknowledged as an efficient ...
In recent years, research on convolutional neural networks (CNN) and recurrent neural networks (RNN) in deep learning has been actively conducted. ...
Feed Forward Neural Network (FFNN) and Long Short-Term Memory (LSTM) were applied. Gochoo et al. [42] showed an unobtrusive CNN activity recognition model for older adults living alone. ...
doi:10.3390/computers11020026
fatcat:jrm6yn6m2nbalp42y2zqr2ruxm
Neuropsychological and neurobiological markers of the preclinical stage of Alzheimer's disease
2011
Psychology and Neuroscience
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posterior cingulate cortex, (5) the presence of the apolipoprotein E ε4 allele, and (6) verbal anterograde episodic long-term memory impairment and executive dysfunction. ...
Dementia, especially Alzheimer's disease, has a high prevalence in the elderly population. ...
of methodologies for the detection of AD based on neuropsychological assessment models. ...
doi:10.3922/j.psns.2011.2.010
fatcat:r6zt5aym2vbalpaqfnmrezvney
Nonwearable Sensor-Based In-Home Assessment of Subtle Daily Behavioral Changes as a Candidate Biomarker for Mild Cognitive Impairment
2021
Journal of Personalized Medicine
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Finally, the usefulness and problems of nonwearable sensor-based in-home assessment for early MCI and AD detection are discussed. ...
Next, an overview of previous studies on the assessment of behavioral changes in MCI and AD using nonwearable sensor-based in-home assessment is provided. ...
Acknowledgments: The authors thank all members of the Kumagai Institute of Health Policy for their research assistance.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jpm12010011
pmid:35055326
pmcid:PMC8781414
fatcat:4qnlybmob5av3gfpzgh3bojo7y
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