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(searched for: doi:10.1109/access.2020.3021943)
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Published: 11 November 2022
by MDPI
Journal: Sensors
Sensors, Volume 22; https://doi.org/10.3390/s22228716

Abstract:
Modern wheelchairs, with advanced and robotic technologies, could not reach the life of millions of disabled people due to their high costs, technical limitations, and safety issues. This paper proposes a gesture-controlled smart wheelchair system with an IoT-enabled fall detection mechanism to overcome these problems. It can recognize gestures using Convolutional Neural Network (CNN) model along with computer vision algorithms and can control the wheelchair automatically by utilizing these gestures. It maintains the safety of the users by performing fall detection with IoT-based emergency messaging systems. The development cost of the overall system is cheap and is lesser than USD 300. Hence, it is expected that the proposed smart wheelchair should be affordable, safe, and helpful to physically disordered people in their independent mobility.
Published: 2 November 2022
by MDPI
Journal: Computation
Abstract:
Currently, telemedicine has gained more strength and its use allows establishing areas that acceptably guarantee patient care, either at the level of control or event monitors. One of the systems that adapt to the objectives of telemedicine are fall detection systems, for which artificial vision or artificial intelligence algorithms are used. This work proposes the design and development of a fall detection model with the use of artificial intelligence, the model can classify various positions of people and identify when there is a fall. A Kinect 2.0 camera is used for monitoring, this device can sense an area and guarantees the quality of the images. The measurement of position values allows to generate the skeletonization of the person and the classification of the different types of movements and the activation of alarms allow us to consider this model as an ideal and reliable assistant for the integrity of the elderly. This approach analyzes images in real time and the results showed that our proposed position-based approach detects human falls reaching 80% accuracy with a simple architecture compared to other state-of-the-art methods.
Published: 30 July 2022
Multimedia Tools and Applications pp 1-25; https://doi.org/10.1007/s11042-022-13451-5

Abstract:
With the increased digitalisation of our society, new and emerging forms of data present new values and opportunities for improved data driven multimedia services, or even new solutions for managing future global pandemics (i.e., Disease X). This article conducts a literature review and bibliometric analysis of existing research records on new and emerging forms of multimedia data. The literature review engages with qualitative search of the most prominent journal and conference publications on this topic. The bibliometric analysis engages with statistical software (i.e. R) analysis of Web of Science data records. The results are somewhat unexpected. Despite the special relationship between the US and the UK, there is not much evidence of collaboration in research on this topic. Similarly, despite the negative media publicity on the current relationship between the US and China (and the US sanctions on China), the research on this topic seems to be growing strong. However, it would be interesting to repeat this exercise after a few years and compare the results. It is possible that the effect of the current US sanctions on China has not taken its full effect yet.
, Martin Tomášek
Published: 13 July 2022
Abstract:
This work proposes a smart system that could be useful in the delivery of elderly care services. Elderly care is a set of services that are provided to senior citizens to help them have a more comfortable and independent life which would not be possible without these services. This proposed system is unique in that it combines the detection algorithm with the automatic update of the dataset. It also uses a heuristic mechanism to reduce false detections. This is on the premise that the AI effort is good, but it could be made better with the inclusion of heuristics. Fall detection accuracy is initially solved by the first classifier, then another classifier evaluates the result with inferences before evoking an alarm. It checks the location of the subject to use in its inferences. Hence the smart house design consists of two machine learning systems. One system performs human activity classification while the other performs fall occurrence detection. Of the eight different classification methods utilized, XGBoost was most accurate with an average of 97.65% during training. A customized dataset is then generated with newly labeled data hence improving system performance.
Published: 23 March 2022
by MDPI
Journal: Healthcare
Abstract:
A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis is ideal for researchers to understand the research trend and future directions. Influential articles, institutes, countries, authors, and commonly used keywords were analyzed to grasp a comprehensive view on our topic, alerts in CDSS. Articles published between 2011 and 2021 were extracted from the Web of Science database. There were 728 articles included for bibliometric analysis, among which 24 papers were selected for content analysis. Our analysis shows that the research direction has shifted from patient safety to system utility, implying the importance of alert usability to be clinically impactful. Finally, we conclude with future research directions such as the optimization of alert mechanisms and comprehensiveness to enhance alert appropriateness and to reduce alert fatigue.
, Ashish Kumar, Ashish Kumar Chakraverti, Ravindra Raman Cholla
Published: 22 March 2022
The publisher has not yet granted permission to display this abstract.
Hasan Imam Bijoy, Masud Rabbani, Ishrak Islam Zarif, Mahbubur Rahman, Rakibul Hasan, Tridip Bhowmik
Published: 1 January 2022
Abstract:
The word "Pandemic" means a horrible situation for the whole world. In December 2019, they were crucially killing breathing symptoms coronavirus (SARS-CoV-2), a novel coronavirus that was first flourishing in Wuhan, China. This survey exposes Bangladeshi people to their thinking, mind, reaction about the corona, guessing about COVID-19, how their mental situation, their economic condition, and how they see the impact on the environment of COVID-19 during the lockdown. More than 15 occupations people supported us in the completion of the work. We set a consent option for participants that they agree or disagree with to fill up the survey form, and we were capable of collecting 938 responses (921 yes and 17 no) through the google form, which filled-up with their assessment. We surveyed to measure and analyze the situation during the lockdown period. According to our survey responses, 19% of people take the COVID-19 as God’s wrath, 35.14% permanently and 43.24% temporarily lose their job of the private job holder, 76.8% suffer financially from corona, 55.60% of students or young guys think a coronavirus will never attack them, and they have lots of issues with coronavirus. We found some curious acknowledgment from the audience because they feel COVID-19 attacks only bad people, someone who had a severe fever, and they think COVID-19 attacked him, and they (17.5%) also think the virus is inactive by the water of the rain. They have a favorable view of the coronavirus impact on the environment. This survey also instituted a reference for further pandemic situations. It will also help in taking advanced preparation for pandemic as well as beware people.
Elangovan Ramanujam,
International Journal of Ambient Computing and Intelligence, Volume 13, pp 1-22; https://doi.org/10.4018/ijaci.304727

Abstract:
Falls are the major cause of injuries and death of elders who live alone at home. Various research works have provided the best solution to the fall detection approach during day vision. However, fall occurs more at the night due to many factors such as low or zero lighting conditions, intake of medication/ drugs, frequent urination due to nocturia disease, and slippery restroom. Based on the required factors, an autonomous monitoring system based on night condition has been proposed through retro-reflective stickers pasted on their upper cloth and infrared cameras installed in the living environment of elders. The developed system uses features such as changes in orientation angle and distance between the retro-reflective stickers to identify the human shape and its characteristics for fall identification. Experimental analysis has also been performed on various events of fall and non-fall activities during the night exclusively in the living environment of the elder, and the system achieves an accuracy of 96.2% and fall detection rate of 92.9%.
, /sup> Emeline Michel, /sup> Cédric Annweiler, /sup> Guillaume Sacco
Published: 1 January 2022
Clinical interventions in aging, pp 35-53; https://doi.org/10.2147/cia.s329668

Abstract:
Abstract: Systems using passive infrared sensors with a low resolution were recently proposed to answer the dilemma effectiveness–ethical considerations for human fall detection by Information and Communication Technologies (ICTs) in older adults. How effective is this type of system? We performed a systematic review to identify studies that investigated the metrological qualities of passive infrared sensors with a maximum resolution of 16× 16 pixels to identify falls. The search was conducted on PubMed, ScienceDirect, SpringerLink, IEEE Xplore Digital Library, and MDPI until November 26– 28, 2020. We focused on studies testing only these types of sensor. Thirteen articles were “conference papers”, five were “original articles” and one was a found in arXiv.org (an open access repository of scientific research). Since four authors “duplicated” their study in two different journals, our review finally analyzed 15 studies. The studies were very heterogeneous with regard to experimental procedures and detection methods, which made it difficult to draw formal conclusions. All studies tested their systems in controlled conditions, mostly in empty rooms. Except for two studies, the overall performance reported for the detection of falls exceeded 85– 90% of accuracy, precision, sensitivity or specificity. Systems using two or more sensors and particular detection methods (eg, 3D CNN, CNN with 10-fold cross-validation, LSTM with CNN, LSTM and Voting algorithms) seemed to give the highest levels of performance (> 90%). Future studies should test more this type of system in real-life conditions.
Published: 19 August 2021
by MDPI
Journal: Biosensors
Biosensors, Volume 11; https://doi.org/10.3390/bios11080284

Abstract:
In recent years, the popularity of wearable devices has fostered the investigation of automatic fall detection systems based on the analysis of the signals captured by transportable inertial sensors. Due to the complexity and variety of human movements, the detection algorithms that offer the best performance when discriminating falls from conventional Activities of Daily Living (ADLs) are those built on machine learning and deep learning mechanisms. In this regard, supervised machine learning binary classification methods have been massively employed by the related literature. However, the learning phase of these algorithms requires mobility patterns caused by falls, which are very difficult to obtain in realistic application scenarios. An interesting alternative is offered by One-Class Classifiers (OCCs), which can be exclusively trained and configured with movement traces of a single type (ADLs). In this paper, a systematic study of the performance of various typical OCCs (for diverse sets of input features and hyperparameters) is performed when applied to nine public repositories of falls and ADLs. The results show the potentials of these classifiers, which are capable of achieving performance metrics very similar to those of supervised algorithms (with values for the specificity and the sensitivity higher than 95%). However, the study warns of the need to have a wide variety of types of ADLs when training OCCs, since activities with a high degree of mobility can significantly increase the frequency of false alarms (ADLs identified as falls) if not considered in the data subsets used for training.
H. M. Mohan, , ,
Published: 3 August 2021
Advances in Human-Computer Interaction, Volume 2021, pp 1-19; https://doi.org/10.1155/2021/6483003

Abstract:
The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.
Published: 29 July 2021
by MDPI
Journal: Sensors
Sensors, Volume 21; https://doi.org/10.3390/s21155134

Abstract:
Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.
Man-Chung Yuen, Sin-Chun Ng,
Published: 1 February 2021
Journal of Physics: Conference Series, Volume 1828; https://doi.org/10.1088/1742-6596/1828/1/012111

Abstract:
Passive management contributes a more stable return than an active management strategy over the long term. Index-tracking is one of the passive investment strategies that attempt to replicate market indexes to reproduce the performance. Sparse index-tracking considers a subset of market index stocks to minimize the difference between the market index and the replicated index. In this paper, two metaheuristics are applied to solve this problem. The sparse index-tracking problem formed by the objective function of the empirical tracking error with the penalty values that result in an NP-hard problem. The penalty value is used to restrict the numbers of the considered stocks. To show the performance of the metaheuristics, various penalty values are investigated, and they produce approximation solutions to the index-tracking problem. Among them, particle swarm optimization shows better or statistically similar performance to GA in solving the sparse index-tracking problem.
Published: 21 October 2020
by MDPI
Journal: Sensors
Sensors, Volume 20; https://doi.org/10.3390/s20205948

Abstract:
Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.
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