Deep learning with long short-term memory networks for classification of dementia related travel patterns

Non-pharmacological management of wandering in persons with dementia: an integrative review

BACKGROUND

Wandering behaviour affects a significant portion of dementia patients, ranging from 15% to 80%, presenting a serious safety concern and adding to caregivers' burden. Recent studies emphasise the effectiveness of non-pharmacological interventions over pharmacological ones due to their minimal side effects. Consequently, in current literature there has been a surge of interest in exploring non-pharmacological methods for managing wandering.

PURPOSE

This integrative literature review aims to deepen comprehension of wandering behaviour, presents recent studies on non-pharmacological approaches, and inspires further research in this field.

METHODS

Electronic data collection spanned from 2019 to 2024, sourcing 20 relevant articles from PubMed and Scopus databases using search terms such as 'dementia', 'Alzheimer's disease', 'wandering', and 'management'. A thematic analysis methodology was employed to identify non-pharmacological treatment themes for managing wandering. This approach involves scrutinising and synthesising themes within the dataset. Qualitative data analysis focused on significant phrases and keywords, grouping them to derive relevant themes.

RESULTS

Recent literature extensively explores non-pharmacological methods for managing wandering. These include understanding behaviours, identifying and targeting high-risk groups, facilitating safe wandering, addressing environmental factors, promoting exercise and activity, and offering caregiver support.

CONCLUSIONS

This study significantly advances understanding of wandering behaviour and highlights recent research on non-pharmacological interventions. The findings suggest the potential for providing safe and effective treatment to wandering dementia patients, thereby alleviating stress for both patients and caregivers.

SafeMove: monitoring seniors with mild cognitive impairments using deep learning and location prediction

Due to society aging, age-related issues such as mild cognitive impairments (MCI) and dementia are attracting the attention of health professionals, scientists and governments. Seniors suffering from such impairments notice a slight deterioration in their cognitive abilities, which may lead to memory loss and behavioural disorders. In consequence, such seniors refrain from doing their everyday outdoor activities. Technology, e.g. smartphones, wearables and artificial intelligence, can provide seniors and their relatives with a variety of monitoring tools. In a nutshell, locations are analysed and, under specific situations, alarms are raised so that caregivers urgently informed. In this context, the discovery and prediction of trajectories and behaviours play a key role in deploying effective monitoring solutions. In this paper, we present a real-time smartphone-based monitoring system, called SafeMove, to discover and predict elderly people behaviours by analyzing outdoor trajectories. This is achieved by firstly analysing the elder’s mobility data previously collected using the proposed model called SpaceTime-Convolutional Neural Network (ST-CNN) in order to predict the most popular locations he/she might visit in the next time. Based on the predicted locations, the elder can be monitored in bounded region. Time and space-related variables, such as the distance traversed, the direction of the movements and the time spent, are analyzed in our abnormal behaviour detection (ABD) model that takes advantage of recurrent neural networks (RNNs). The effectiveness and the efficiency of our system for predicting the next location and detection the abnormal behaviors are evaluated using different datasets comprising real-world GPS trajectories.

An Attention Mechanism Oriented Hybrid CNN-RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

The booming computational thinking and deep learning make it possible to construct agile, efficient, and robust deep learning-driven decision-making support engine for the operation of container terminal handling systems (CTHSs). Within the conceptual framework of computational logistics, an attention mechanism oriented hybrid convolutional neural network and recurrent neural network deep learning architecture (AMO-HCR-DLA) is proposed technically to predict the container terminal liner handling conditions that mainly include liner handling time (LHT) and total working time of quay crane farm (TWT-QCF) for a calling liner. Consequently, the container terminal oriented logistics generalized computation (CTO-LGC) automation and intelligence are established tentatively by AMO-HCR-DLA. A typical regional container terminal hub of China is selected to design, implement, execute, and evaluate the AMO-HCR-DLA with the actual production data. In the case of severe vibration of LHT and TWT-QCF, while forecasting the handling conditions of 210 ships based on the CTO-LGC running log of four years, the forecasting error of LHT within one hour is more than 97% and that of TWT-QCF within six hours accounts for 89.405%. When predicting the operating conditions of 300 liners by the log of five years, the forecasting deviation of LHT within one hour is more than striking 99% and that of TWT-QCF within six hours reaches up to 94.010% as well. All are far superior to the predicting outcomes by the classical algorithms of machine learning and deep learning. Hence, the AMO-HCR-DLA shows excellent performance for the prediction of CTHS with the low and stable computational consuming. It also demonstrates the feasibility, credibility, and realizability of the computing architecture and design paradigm of AMO-HCR-DLA preliminarily.