The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer's disease, frailty, Parkinson's, and cardiovascular disease, but also a ...general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.
Abstract The localization of neuronal generators during an ERP study, using a high-density electroencephalogram (HD-EEG) equipment was made on three Evoked Related Potential (ERP) components, i.e., ...the Mismatch Negativity (MMN), the P300 and the N400. Furthermore, the ERP characteristics, their field distribution and the area of their maximum field intensity were extracted and compared between young and elderly, as well as between females and males. A two tone oddball experiment was conducted, involving 27 young adults and 18 elderly, healthy and right handed, and HD-EEG data were acquired. These data were then subjected to auditory ERPs extraction and thorough statistical analysis. The derived experimental results revealed significant age-related differences to both the latencies and the amplitudes of the MMN and the P300 and the topographic distribution of the HD-EEG amplitudes. Additionally, a shift in the maximum intensities from frontal to temporal lobe with aging appeared in the case of the P300, whereas no effect was observed for the MMN component. No statistical significant differences ( p >0.05) due to age was found in N400 characteristics. Finally, gender-related differences were significant in the response time of the subjects, finding males response faster. The level and the location of the maximum intensity of sources also differed between genders, especially in young subjects. These findings justify the enhanced potential of HD-EEG data to accurately reflect the age and gender dependencies at the three components of simple auditory ERPs and pave the way for the investigation of neurodegenerative pathologies, such as the Alzheimer’s disease.
This paper deals with content-based large-scale image retrieval using the state-of-the-art framework of VLAD and Product Quantization proposed by Jegou as a starting point. Demonstrating an excellent ...accuracy-efficiency trade-off, this framework has attracted increased attention from the community and numerous extensions have been proposed. In this work, we make an in-depth analysis of the framework that aims at increasing our understanding of its different processing steps and boosting its overall performance. Our analysis involves the evaluation of numerous extensions (both existing and novel) as well as the study of the effects of several unexplored parameters. We specifically focus on: a) employing more efficient and discriminative local features; b) improving the quality of the aggregated representation; and c) optimizing the indexing scheme. Our thorough experimental evaluation provides new insights into extensions that consistently contribute, and others that do not, to performance improvement, and sheds light onto the effects of previously unexplored parameters of the framework. As a result, we develop an enhanced framework that significantly outperforms the previous best reported accuracy results on standard benchmarks and is more efficient.
Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react ...expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.
People with severe motor impairment face many challenges in communication and control of the environment, whilst survivors from neurological disorders have increased demand for advanced, adaptive and ...personalized rehabilitation. The last decades many studies have underlined the importance of brain-computer interfaces (BCIs) with great contributions ranging from communication restoration to motor rehabilitation. In this work we review BCI research that focuses on noninvasive, electroencephalography (EEG)-based BCI systems for people with motor impairment as far as communication and rehabilitation aspects are concerned. More specifically we overview milestone approaches that are primarily intended to help severely paralyzed and/or locked-in state patients by using three different BCI modalities, i.e., slow cortical potentials, sensorimotor rhythms and P300 potentials as operational mechanisms. In addition, we review BCI systems with special emphasis on restoration of motor function for patients with spinal cord injury and chronic stroke. Finally, we summarize how EEG-based BCI systems have contributed to communication and rehabilitation of motor impaired people, stress out advantages and limitations and discuss the challenges that these systems should address in the future.
Combining multimodal concept streams from heterogeneous sensors is a problem superficially explored for activity recognition. Most studies explore simple sensors in nearly perfect conditions, where ...temporal synchronization is guaranteed. Sophisticated fusion schemes adopt problem-specific graphical representations of events that are generally deeply linked with their training data and focused on a single sensor. This paper proposes a hybrid framework between knowledge-driven and probabilistic-driven methods for event representation and recognition. It separates semantic modeling from raw sensor data by using an intermediate semantic representation, namely concepts. It introduces an algorithm for sensor alignment that uses concept similarity as a surrogate for the inaccurate temporal information of real life scenarios. Finally, it proposes the combined use of an ontology language, to overcome the rigidity of previous approaches at model definition, and a probabilistic interpretation for ontological models, which equips the framework with a mechanism to handle noisy and ambiguous concept observations, an ability that most knowledge-driven methods lack. We evaluate our contributions in multimodal recordings of elderly people carrying out IADLs. Results demonstrated that the proposed framework outperforms baseline methods both in event recognition performance and in delimiting the temporal boundaries of event instances.
Human activity recognition (HAR) has made significant progress in recent years, with growing applications in various domains, and the emergence of wearable and ambient sensors has provided new ...opportunities in the field ....
Abstract Precise preclinical detection of dementia for effective treatment and stage monitoring is of great importance. Miscellaneous types of biomarkers, e.g., biochemical, genetic, neuroimaging, ...and physiological, have been proposed to diagnose Alzheimer's disease (AD), the usual suspect behind manifested cognitive decline, and mild cognitive impairment (MCI), a neuropathology prior to AD that does not affect cognitive functions. Event related potential (ERP) methods constitute a non-invasive, inexpensive means of analysis and have been proposed as sensitive biomarkers of cognitive impairment; besides, various ERP components are strongly linked with working memory, attention, sensory processing and motor responses. In this study, an auditory oddball task is employed, to acquire high density electroencephalograhy recordings from healthy elderly controls, MCI and AD patients. The mismatch negativity (MMN) and P300 ERP components are then extracted and their relationship with neurodegeneration is examined. Then, the neural activation at these components is reconstructed using the 3D vector field tomography (3D-VFT) inverse solution. The results reveal a decline of both ERPs amplitude, and a statistically significant prolongation of their latency as cognitive impairment advances. For the MMN, higher brain activation is usually localized in the inferior frontal and superior temporal gyrus in the controls. However, in AD, parietal sites exhibit strong activity. Stronger P300 generators are mostly found in the frontal lobe for the controls, but in AD they often shift to the temporal lobe. Reduction in inferior frontal source strength and the switch of the maximum intensity area to parietal and superior temporal sites suggest that these areas, especially the former, are of particular significance when neurodegenerative disorders are investigated. The modulation of MMN and P300 can serve to produce biomarkers of dementia and its progression, and brain imaging can further contribute to the diagnostic efficiency of ERPs.
Internet-of-Things systems are increasingly being installed in buildings to transform them into smart ones and to assist in the transition to a greener future. A common feature of smart buildings, ...whether commercial or residential, is environmental sensing that provides information about temperature, dust, and the general air quality of indoor spaces, assisting in achieving energy efficiency. Environmental sensors though, especially when combined, can also be used to detect occupancy in a space and to increase security and safety. The most popular methods for the combination of environmental sensor measurements are concatenation and neural networks that can conduct fusion in different levels. This work presents an evaluation of the performance of multiple late fusion methods in detecting occupancy from environmental sensors installed in a building during its construction and provides a comparison of the late fusion approaches with early fusion followed by ensemble classifiers. A novel weighted fusion method, suitable for imbalanced samples, is also tested. The data collected from the environmental sensors are provided as a public dataset.
This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event ...detection techniques have focused on either images or text, but combining the two can improve performance. The authors present lessons learned from the Flood-related multimedia task in MediaEval2020, provide a dataset for reproducibility, and propose a new multimodal fusion method that uses Graph Neural Networks to combine image, text, and time information. Their method outperforms state-of-the-art approaches and can handle low-sample labelled data.
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