SHAPING ENVIRONMENTS CONDUCIVE TO EMOTIONAL WELL-BEING FOR ELDERLY PEOPLE WITH INCIPIENT COGNITIVE IMPAIRMENT

10.6036/10200 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 447-447
Author(s):  
Arturo Peralta Martin-Palomino ◽  
ANTONIO FERNANDEZ CABALLERO ◽  
JOSE MIGUEL LATORRE POSTIGO
Keyword(s):  
Cognitive Impairment ◽  
Environmental Factors ◽  
Elderly People ◽  
Great Influence ◽  
Well Being ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Gradual Development ◽  
Ambient Music ◽  
Selection Of

Undoubtedly, the environment and the set of environmental factors that surround us can have a great influence on the perceived sense of well-being. However, there are few studies that analyze, jointly, the influence of a selection of factors for the conformation of environments that favor well-being, especially oriented to elderly people with cognitive impairment. It is in this context that the present study arises, with the aim of determining the influence of a set of ten factors (ambient temperature, lighting, ambient music, etc.) on the emotional well-being of elderly people with early symptoms of cognitive impairment, which is particularly important in today's increasingly aging society, where the gradual development of cognitive impairment is inevitable. For this purpose, the advances achieved in previous research carried out by the authors are summarized, and a method based on the execution of a set of experiments is described, using collections of images to assess the emotional interpretation of each individual under the influence of different combination of simulated conditions (selected external factors) in their environment, processing and analyzing the data obtained by means of machine learning techniques. The realization of these experiments and the application of the proposed analysis method will allow obtaining knowledge that can be used for the design of specific living environments for elderly people with cognitive impairment where the sense of emotional well-being and the preservation of their mental faculties are favored. Key words: Emotional interpretation, environmental factors, Soft Computing, clustering, advanced age.

scholarly journals Trends in Heart-Rate Variability Signal Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Syem Ishaque ◽  
Naimul Khan ◽  
Sri Krishnan
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Well Being ◽  
Good Health ◽  
Machine Learning Techniques ◽  
Video Gaming ◽  
Clinical Events ◽  
Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

scholarly journals Symptom clusters among cancer survivors: what can machine learning techniques tell us?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Koen I. Neijenhuijs ◽  
Carel F. W. Peeters ◽  
Henk van Weert ◽  
Pim Cuijpers ◽  
Irma Verdonck-de Leeuw
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Cancer Survivors ◽  
Well Being ◽  
Physical Symptoms ◽  
Symptom Clusters ◽  
Machine Learning Techniques ◽  
Risk Scores ◽  
Learning Techniques ◽  
Patient Reported

Abstract Purpose Knowledge regarding symptom clusters may inform targeted interventions. The current study investigated symptom clusters among cancer survivors, using machine learning techniques on a large data set. Methods Data consisted of self-reports of cancer survivors who used a fully automated online application ‘Oncokompas’ that supports them in their self-management. This is done by 1) monitoring their symptoms through patient reported outcome measures (PROMs); and 2) providing a personalized overview of supportive care options tailored to their scores, aiming to reduce symptom burden and improve health-related quality of life. In the present study, data on 26 generic symptoms (physical and psychosocial) were used. Results of the PROM of each symptom are presented to the user as a no well-being risk, moderate well-being risk, or high well-being risk score. Data of 1032 cancer survivors were analysed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on high risk scores and moderate-to-high risk scores separately. Results When analyzing the high risk scores, seven clusters were extracted: one main cluster which contained most frequently occurring physical and psychosocial symptoms, and six subclusters with different combinations of these symptoms. When analyzing moderate-to-high risk scores, three clusters were extracted: two main clusters were identified, which separated physical symptoms (and their consequences) and psycho-social symptoms, and one subcluster with only body weight issues. Conclusion There appears to be an inherent difference on the co-occurrence of symptoms dependent on symptom severity. Among survivors with high risk scores, the data showed a clustering of more connections between physical and psycho-social symptoms in separate subclusters. Among survivors with moderate-to-high risk scores, we observed less connections in the clustering between physical and psycho-social symptoms.

scholarly journals Grain Yield Estimation in Cultivated Land Using Machine Learning Techniques

2021 ◽  
Vol 36 (2) ◽  
pp. 70-75
Author(s):  
Dr.K. Venkata Nagendra ◽  
Dr.B. Prasad ◽  
K.T.P.S. Kumar ◽  
K.S. Raghuram ◽  
Dr.K. Somasundaram
Keyword(s):  
Machine Learning ◽  
Market Price ◽  
Specific Area ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Accurate Information ◽  
Agricultural Crop ◽  
Agricultural Field ◽  
Learning Techniques ◽  
Selection Of

Agriculture contributes approximately 28 percent of India's GDP, and agriculture employs approximately 65 percent of the country's labor force. India is the world's second-largest agricultural crop producer. Agriculture is not only an important part of the expanding economy, but it is also necessary for our survival. The technological contribution could assist the farmer in increasing his yield. The selection of each crop is critical in the planning of agricultural production. The selection of crops will be influenced by a variety of factors, including market price, production rate, and the policies of the various government departments. Numerous changes are required in the agricultural field in order to improve the overall performance of our Indian economy. By using machine learning techniques that are easily applied to the farming sector we can improve agriculture. Along with all of the advancements in farming machinery and technology, the availability of useful and accurate information about a variety of topics plays an important role in the success of the industry. It is a difficult task to predict agricultural output since it depends on a number of variables, such as irrigation, ultraviolet (UV), insect killers, stimulants & the quantity of land enclosed in that specific area. It is proposed in this article that two distinct Machine Learning (ML) methods be used to evaluate the yields of the crops. The two algorithms, SVR and Linear Regression, have been well suited to validate the variable parameters of the continuous variable estimate with 185 acquired data points.

Multi-Fidelity Global-Local Optimization of a Transonic Compressor Rotor

2019 ◽  
Author(s):  
Sudeepta Mondal ◽  
Michael M. Joly ◽  
Soumalya Sarkar
Keyword(s):  
Search Algorithm ◽  
Local Optimization ◽  
Machine Learning Techniques ◽  
High Fidelity ◽  
Aerodynamic Design ◽  
Local Approach ◽  
Transonic Compressor ◽  
Compressor Rotor ◽  
Learning Techniques ◽  
Selection Of

Abstract In aerodynamic design, accurate and robust surrogate models are important to accelerate computationally expensive CFD-based optimization. Machine learning techniques can also enable affordable exploration of high-dimensional design spaces with targeted selection of sparse high-fidelity data. In this paper, a multi-fidelity global-local approach is presented and applied to the surrogate-based design optimization of a highly-loaded transonic compressor rotor. The key idea is to train multi-fidelity surrogates with fewer high-fidelity RANS predictions and more rapid and inexpensive lower-fidelity RANS evaluations. The framework also introduces a global-local search algorithm that can spin-off multiple local optimization threads over narrow and targeted design spaces, concurrently to a constantly adapting global optimization thread. The approach is demonstrated with an optimization of the transonic NASA rotor 37, yielding significant increase in performance within a dozen of optimization iterations.

scholarly journals Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

2020 ◽  
Vol 17 (3) ◽  
pp. 193-206 ◽  
Author(s):  
Giampaolo Perna ◽  
Alessandra Alciati ◽  
Silvia Daccò ◽  
Massimiliano Grassi ◽  
Daniela Caldirola
Keyword(s):  
Clinical Practice ◽  
Antidepressant Treatment ◽  
Therapeutic Interventions ◽  
Machine Learning Techniques ◽  
Clinical Variables ◽  
Learning Techniques ◽  
Error Treatment ◽  
Computational Resources ◽  
Personalized Psychiatry ◽  
Selection Of

Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.

Predicting mental disorder from noisy questionnaires: an anomaly detection approach based on keyword extraction and machine learning techniques

2021 ◽  
pp. 1-13
Author(s):  
Qing Zhou ◽  
Xi Shi ◽  
Liang Ge
Keyword(s):  
Machine Learning ◽  
Mental Disorders ◽  
Anomaly Detection ◽  
Well Being ◽  
Machine Learning Techniques ◽  
Keyword Extraction ◽  
Learning Techniques ◽  
Detection Approach ◽  
Scaling Methods ◽  
Machine Learning Models

The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation of question significance. Experiments demonstrate the effectiveness of our proposed methods.

scholarly journals Sensor, Signal, and Imaging Informatics in 2017

2018 ◽  
Vol 27 (01) ◽  
pp. 110-113 ◽  
Author(s):  
William Hsu ◽  
Thomas Deserno ◽  
Charles Kahn ◽  
Keyword(s):  
Medical Informatics ◽  
Imaging Informatics ◽  
Machine Learning Techniques ◽  
Sensor Signal ◽  
Imaging Data ◽  
Scientific Publications ◽  
Learning Techniques ◽  
Final Selection ◽  
Selection Of

Objective: To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017. Methods: PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook. Results: The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information. Conclusion: The growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics.

scholarly journals Efficiently Coupling the I-DLV Grounder with ASP Solvers

2018 ◽  
Vol 20 (2) ◽  
pp. 205-224 ◽  
Author(s):  
FRANCESCO CALIMERI ◽  
CARMINE DODARO ◽  
DAVIDE FUSCÀ ◽  
SIMONA PERRI ◽  
JESSICA ZANGARI
Keyword(s):  
Machine Learning ◽  
Answer Set Programming ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Selection Of ◽  
Answer Set

We present ${{{{$\mathscr{I}$}-}\textsc{dlv}}+{{$\mathscr{MS}$}}}$, a new answer set programming (ASP) system that integrates an efficient grounder, namely ${{{$\mathscr{I}$}-}\textsc{dlv}}$, with an automatic selector that inductively chooses a solver: depending on some inherent features of the instantiation produced by ${{{$\mathscr{I}$}-}\textsc{dlv}}$, machine learning techniques guide the selection of the most appropriate solver. The system participated in the latest (7th) ASP competition, winning the regular track, category SP (i.e., one processor allowed).

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