| 1. |
- Chu, Thi My Chinh, et al.
(författare)
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On the Perception of Frame Stalls in Remote VR for Task and Task-Free Subjective Tests
- 2023
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Ingår i: 2023 15th International Conference on Quality of Multimedia Experience, QoMEX 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350311730 ; , s. 201-204
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Konferensbidrag (refereegranskat)abstract
- The performance of remotely rendered Virtual Reality (VR) is sensitive to temporal disturbances in communication channels. An earlier Quality of Experience (QoE) study of temporal impacts in the form of frame stalls has revealed difficulties with subjective disturbance ratings while performing a task in an interactive 6-degrees-of-freedom (DOF) VR environment. This study follows up on above observation by comparing QoE ratings in the presence and absence of a task. The exploratory findings show that the task-free subjective tests yield lower ratings compared to the subjective tests with task. This indicates that the participants became more sensitive to temporal impairments in the absence of a task. Also, the positive impact of reprojection on the QoE ratings decreased in the task-free environment. The simulator sickness results for individual symptoms were on similar low levels in both settings. The total score (TS) of sickness severity was higher after than before the subjective tests with task while the difference between the TS before and after the task-free subjective tests was insignificant, © 2023 IEEE.
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| 2. |
- Dallora Moraes, Ana Luiza, et al.
(författare)
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A decision tree multifactorial approach for predicting dementia in a 10 years’ time
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: Dementia is a complex neurological disorder, to which little is known about its mechanisms and no therapeutic treatment was identified, to date, to revert or alleviate its symptoms. It affects the older adults population causing a progressive cognitive decline that can become severe enough to impair the individuals' independence and functioning. In this scenario, the prognosis research, directed to identify modifiable risk factors in order to delay or prevent its development, in a big enough time frame is substantially important.Objective: This study investigates a decision tree multifactorial approach for the prognosis of dementia of individuals, not diagnosed with this disorder at baseline, and their development (or not) of dementia in a time frame of 10 years. Methods: This study retrieved data from the Swedish National Study on Aging and Care, which consisted of 726 subjects (313 males and 413 females), of which 91 presented a diagnosis of dementia at the 10-year study mark. A K-nearest neighbors multiple imputation method was employed to handle the missing data. A wrapper feature selection was employed to select the best features in a set of 75 variables, which considered factors related to demographic, social, lifestyle, medical history, biochemical test, physical examination, psychological assessment and diverse health instruments relevant to dementia evaluation. Lastly, a cost-sensitive decision tree approach was employed in order to build predictive models in an stratified nested cross-validation experimental setup.Results: The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Our findings showed that most of the variables selected by the tree are related to modifiable risk factors, of which physical strength was an important factor across all ages of the sample. Also, there was a lack of variables related to the health instruments routinely used for the dementia diagnosis that might not be sensitive enough to predict dementia in a 10 years’ time.Conclusions: The proposed model identified diverse modifiable factors, in a 10 years’ time from diagnosis, that could be investigated for possible interventions in order to delay or prevent the dementia onset.
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| 3. |
- Dallora Moraes, Ana Luiza, et al.
(författare)
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Bone age assessment with various machine learning techniques : A systematic literature review and meta-analysis
- 2019
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 14:7
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Forskningsöversikt (refereegranskat)abstract
- Background The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. Objective The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. Method A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. Results 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. Conclusions There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. Copyright: © 2019 Dallora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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| 4. |
- Georgsson, Mattias
(författare)
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Quantifying usability : an evaluation of a diabetes mHealth system on effectiveness, efficiency, and satisfaction metrics with associated user characteristics
- 2016
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press. - 1067-5027 .- 1527-974X. ; 23:1, s. 5-11
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Tidskriftsartikel (refereegranskat)abstract
- Objective Mobile health (mHealth) systems are becoming more common for chronic disease management, but usability studies are still needed on patients' perspectives and mHealth interaction performance. This deficiency is addressed by our quantitative usability study of a mHealth diabetes system evaluating patients' task performance, satisfaction, and the relationship of these measures to user characteristics. Materials and Methods We used metrics in the International Organization for Standardization (ISO) 9241-11 standard. After standardized training, 10 patients performed representative tasks and were assessed on individual task success, errors, efficiency (time on task), satisfaction (System Usability Scale [SUS]) and user characteristics. Results Tasks of exporting and correcting values proved the most difficult, had the most errors, the lowest task success rates, and consumed the longest times on task. The average SUS satisfaction score was 80.5, indicating good but not excellent system usability. Data trends showed males were more successful in task completion, and younger participants had higher performance scores. Educational level did not influence performance, but a more recent diabetes diagnosis did. Patients with more experience in information technology (IT) also had higher performance rates. Discussion Difficult task performance indicated areas for redesign. Our methods can assist others in identifying areas in need of improvement. Data about user background and IT skills also showed how user characteristics influence performance and can provide future considerations for targeted mHealth designs. Conclusion Using the ISO 9241-11 usability standard, the SUS instrument for satisfaction and measuring user characteristics provided objective measures of patients' experienced usability. These could serve as an exemplar for standardized, quantitative methods for usability studies on mHealth systems.
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| 5. |
- Georgsson, Mattias
(författare)
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Using activity theory as a framework for the usability evaluation process and task determination in mhealth self-management systems for diabetes
- 2018
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Ingår i: PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON WEARABLE MICRO AND NANO TECHNOLOGIES FOR PERSONALIZED HEALTH (PHEALTH 2018). - : IOS Press. - 9781614998679 ; , s. 158-163
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Konferensbidrag (refereegranskat)abstract
- mHealth systems can be used for patients in their diabetes selfmanagement, but usability evaluations are often needed to determine how to make them more useful for the diabetes patient user in the monitoring and managing of their disease. Activity Theory (AT) was developed within Russian psychology to define the work and activity process in an activity system. AT was here considered to also be a particularly suitable framework for inspiration in usability evaluation both for the whole evaluation process and also for the usability task determination in this process for diabetes patient users. In the following paper, examples are provided from four usability studies using both user-based and expert usability methods in evaluation showing how AT was applied to guide the thoughts in evaluating the usability of two mHealth self-management systems for diabetes. Experiences and insights are provided from this process. © 2018 The authors and IOS Press. All rights reserved.
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| 6. |
- Idrisoglu, Alper, et al.
(författare)
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Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders : Systematic Literature Review
- 2023
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Ingår i: Journal of Medical Internet Research. - : JMIR Publications. - 1438-8871. ; 25
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Forskningsöversikt (refereegranskat)abstract
- BACKGROUND: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. OBJECTIVE: This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. METHODS: This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. RESULTS: In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. CONCLUSIONS: This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.
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| 7. |
- Isaksson, Gunilla, et al.
(författare)
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To regain participation in occupations through human encounters : narratives from women with spinal cord injury
- 2007
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Ingår i: Disability and Rehabilitation. - : Informa UK Limited. - 0963-8288 .- 1464-5165. ; 29:22, s. 1679-1688
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Tidskriftsartikel (refereegranskat)abstract
- Purpose. To gain an understanding of how women with spinal cord injury (SCI) experienced human encounters in occupations and how these influenced their participation. Method. The data were collected through two or three in-depth interviews with 13 women (age 25 - 61 years) with SCI. Data analysis was carried out by using a paradigmatic analysis of narrative data, followed by an interpretation based on a narrative theory. Results. The results showed a complexity where the women's experiences and acting in human encounters changed over time. In these human encounters the women struggled with conflicts, supported other persons that were insecure and revaluated their apprehension about persons in their social network. These multidimensional human encounters thereby enabled them to regain participation in occupations. Conclusions. This shows that human encounters are important for persons with disabilities so they can restructure their occupational identity and their needs for participation in occupations. The study also showed that the use of narratives as a tool within rehabilitation could lead to an increased understanding of the subjective changes that occur over time for a person with a disability
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| 8. |
- Isaksson, Gunilla, et al.
(författare)
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Women's perception of changes in the social network after a spinal cord injury
- 2005
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Ingår i: Disability and Rehabilitation. - : Informa UK Limited. - 0963-8288 .- 1464-5165. ; 27:17, s. 1013-1021
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: To describe how women with a spinal cord injury (SCI) perceived changes in the social network, and how these changes affected their ability to participate in occupation. METHOD: Thirteen women, aged 25 to 61 years, with a SCI were interviewed twice. The interviews focused on their ability to participate in occupation, their relations with individuals within the social network, and changes in the social network following the SCI. The analysis was carried out using qualitative content analysis. RESULTS: The women described an emotional need for social support after the SCI to participate in occupation. This was a new experience that required time to adapt to. The women also described a need for practical social support from the social network members to manage meaningful occupation. After the SCI, the women had developed new habits through close cooperation with members in the social network. The women felt that they had become more responsible for the development of their relations. Many relations had improved after the SCI, while some had decreased. The women had also developed new relations with other persons with disabilities. CONCLUSIONS: The women perceived substantial changes in the social network following the SCI, which in several ways affected their ability to participate in occupation. To adapt to their new life situation, the women gradually developed different strategies. The results point out the need to identify persons in the social network that women with SCI develop relations with, and integrate them in the rehabilitation process.
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| 9. |
- Moraes, Ana Luiza Dallora, et al.
(författare)
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Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects : Machine Learning Multifactorial Approach
- 2020
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 8:9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Bone age assessment (BAA) is used in numerous pediatric clinical settings, as well as in legal settings when entities need an estimate of chronological age (CA) when valid documents are lacking. The latter case presents itself as critical since the law is harsher for adults and granted rights along with imputability changes drastically if the individual is a minor. Traditional BAA methods suffer from drawbacks such as exposure of minors to radiation, do not consider factors that might affect the bone age and they mostly focus on a single region. Given the critical scenarios in which BAA can affect the lives of young individuals it is important to focus on the drawbacks of the traditional methods and investigate the potential of estimating CA through BAA.Objective: This paper aims to investigate CA estimation through BAA in young individuals of 14 to 21 years with machine learning methods, addressing the drawbacks in the research using magnetic resonance imaging (MRI), assessment of multiple ROIs and other factors that may affect the bone age.Methods: MRI examinations of the radius, distal tibia, proximal tibia, distal femur and calcaneus were carried out on 465 males and 473 females subjects (14-21 years). Measures of weight and height were taken from the subjects and a questionnaire was given for additional information (self-assessed Tanner Scale, physical activity level, parents' origin, type of residence during upbringing). Two pediatric radiologists assessed, independently, the MRI images as to their stage of bone development (blinded to age, gender and each other). All the gathered information was used in training machine learning models for chronological age estimation and minor versus adults classification (threshold of 18 years). Different machine learning methods were investigated.Results: The minor versus adults classification produced accuracies of 90% and 84%, for male and female subjects, respectively, with high recalls for the classification of minors. The chronological age estimation for the eight age groups (14-21 years) achieved mean absolute errors of 0.95 years and 1.24 years for male and female subjects, respectively. However, for the latter lower error occurred only for the ages of 14 and 15.Conclusions: This paper proposed to investigate the CA estimation through BAA using machine learning methods in two ways: minor versus adults classification and CA estimation in eight age groups (14-21 years), while addressing the drawbacks in the research on BAA. The first achieved good results, however, for the second case BAA showed not precise enough for the classification.
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| 10. |
- Topaz, Maxim, et al.
(författare)
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Nurse Informaticians Report Low Satisfaction and Multi-level Concerns with Electronic Health Records : Results from an International Survey
- 2016
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Ingår i: AMIA Annual Symposium Proceedings. - 1942-597X. ; 2016, s. 2016-2025
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Tidskriftsartikel (refereegranskat)abstract
- This study presents a qualitative content analysis of nurses' satisfaction and issues with current electronic health record (EHR) systems, as reflected in one of the largest international surveys of nursing informatics. Study participants from 45 countries (n=469) ranked their satisfaction with the current state of nursing functionality in EHRs as relatively low. Two-thirds of the participants (n=283) provided disconcerting comments when explaining their low satisfaction rankings. More than one half of the comments identified issues at the system level (e.g., poor system usability; non-integrated systems and poor interoperability; lack of standards; and limited functionality/missing components), followed by user-task issues (e.g., failure of systems to meet nursing clinical needs; non nursing-specific systems) and environment issues (e.g., low prevalence of EHRs; lack of user training). The study results call for the attention of international stakeholders (educators, managers, policy makers) to improve the current issues with EHRs from a nursing perspective.
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