The role of medical test results in the diagnosis and treatment of a patient’s disease cannot be denied. Doctors and medical staff rely on these results to develop a treatment plan that meets the requirements of the patient’s health status (i.e., physical condition) and disease type. In developing countries (i.e., Vietnam), we note that test results are recorded in paper versions and stored by patients. Thus, several solutions have been introduced for electronic medical records to compensate for medical test results management. However, in Vietnam, these approaches face many obstacles such as centralized processing (e.g., storage, analysis); non-transparency issues; scalability; availability; and so on. In this paper, we exploit the benefits of blockchain, smart contracts, and NFT technologies to solve the above disadvantages. Therefore, our work contributes to five aspects. (a) Collecting procedures for handling and storing test results of patients at hospitals in Ho Chi Minh City and Mekong Delta (i.e., Can Tho city) (b) Proposing a mechanism for sharing test results based on blockchain technology, smart contract, and NFT applied; (c) Presenting an NFT tool-based certification generation model; (d) implementing the proposed model based on smart contracts (i.e., proof-of-concept); and (e) deploying proof-of-concept on four EVM- and NFT-supported platforms to find the most suitable one.
There have been studies on Lightweight Power Wheelchairs (LPW) that compensate for the disadvantages of large and heavy powered wheelchairs for the activities of the people with physical disabilities in the community, but there is a lack of research to understand usability. Accordingly, we compared the usability of LPWs developed in Korea for 5 wheelchair participants and caregivers to find out their effectiveness, efficiency, and satisfaction. As a result of the usability testing, there was a difference between the two LPWs in effectiveness and satisfaction. LPW1 was analyzed as grade B/’Good’ usability LPW2 was analyzed as grade A/’Best Imaginable’ usability. The usability of the heavier LPW2 was highly appreciated. This can be interpreted that users feel a sense of stability in the LPW with a certain amount of weight. In future studies, it is considered necessary to study the appropriate weight of LPW that users can feel stable.
Measuring the range of motion (ROM) is one of the important tasks in medical or healthcare sectors. However, person-to-person measurement is time-consuming and requires resources. In this paper, we propose an approach to estimate ROM using machine learning algorithm equipped with computer vision technology based on data-driven experiments. We describe the setup to gather experimental dataset to learn the angle of human joints in 2D space. From the extensive experiments and multi-linear regression learning approach, our method can reduce estimation error by 11.1
Assistive technology devices (ATD) and services (ATS) are generally applied to assist the people with disabilities and the elderly who have limited independent performance in daily life. The Korean government is also providing support through various projects, but the evidence for its effectiveness is lacking, and its application is also limited. Therefore, it is necessary to objectively measure the value of ATD and present evidence. This study analyzed studies that measured the economic and non-economic values of ATD and ATS through a systematic literature review, and looked at the overall economic analysis process. The database was compiled using KCI, NDSL, Web of Science and Scopus. Nineteen studies were selected according to a set procedure for literature screening, and the quality level of studies was analyzed through the CHEERS checklist 2022. Most studies have analyzed hearing aids, and most studies have used cost-effectiveness techniques. Measurement instruments were most commonly used to measure health and quality of life, accounting for approximately 60
Frailty is a clinical syndrome associated with ageing that characterizes an intermediate state between robust health and loss of autonomy. To preserve the abilities of older adults and prevent dependency, it is important to identify and evaluate their frailty. This approach is part of a dependency prevention strategy, based on a thorough understanding of their medical, social, and living environment. This understanding is usually acquired through significant data collection using standardized evaluation surveys. The obtained data is then analyzed to provide personalized recommendations for the beneficiaries’ lifestyles. Our article presents the concept of frailty and a personalized recommendation system aimed at helping citizens prevent frailty. This system uses an innovative self-assessment approach designed for older adults, without necessarily involving healthcare professionals.
This study systematically reviewed research that applied dual-task interventions using VR technology for balance and cognitive tasks among older adults. Ten databases were searched following the PRISMA guideline, and 18 studies were selected based on their evidence levels and risk of bias. The selected studies consisted of 10 RCTs, 6 non-randomized controlled trials, and one each of case and qualitative studies. The types of balance tasks included standing and sitting postures, and all studies utilized cognitive tasks that required concentration. A total of 30 physical assessment tools and 42 cognitive assessment tools were identified. The results showed that virtual reality interventions improved balance and cognitive abilities among older adults and had a positive effect on fall prevention. These findings suggest that VR technology can be an effective tool for improving the physical and cognitive health of older adults.
Edge computing, a distributed computing architecture within the knowledge-defined network (KDN), faces challenges due to the significant disparities and data heterogeneity among its nodes, hindering their interaction. Ontology, a solution within the Semantic Web, is well-suited for addressing data heterogeneity and matching ontologies effectively. However, ontology matching presents difficulties due to non-linear mathematical issues. To overcome these challenges, the generative adversarial network (GAN), an unsupervised learning method, has emerged as a promising tool. GAN consists of two models with distinct objectives trained against eachother to achieve optimal outcomes. This paper introduces SA-GAN, an algorithm that combines GAN with simulation-based annealing to enhance its effectiveness. SA-GAN utilizes a stagnation counter to expedite the convergence speed of GAN. Through experiments conducted on a renowned ontology benchmark, the paper demonstrates that SA-GAN, along with other ontology matching algorithms, can identify the best alignments. Consequently, SA-GAN facilitates the construction of bridges in edge computing, improving its overall effectiveness.
In the past decade, a lot of challenges to access, assess, and to acquire the needed technological opportunities to teach computers what naturally comes from the human brain and to understand how we naturally react when we rely on technology. The ability to document human thoughts, reactions and behavior to computers has led to the coming of NLP, AI, Dl, & ML. Aim to understand the influence of IoT on humans with the use of DL to achieve content correctness and accuracy with virtual technology. Studies show that the way we think, react, and do the things we think “Internet of thoughts” reflect our personality. The way we think determines the way we react and the way we do things are based on how we think. Technology advancement has reinforced a lot of changes in humans which makes humans vulnerable to personal content exposure misappropriation due to the continuously changing nature of humanity and language. The study uses NLP, DL and behavior-oriented drive and influential function and results show that IoT based on VR influences human psychology “Internet of Thoughts”.
Activities of daily living (ADLs) are basic self-care tasks that are necessary for independent living. It is also used to assess aging adults’ functional ability. As people age, they experience impaired awareness, which affects their reasoning, cognitive ability, and consequently impedes their ability to manage or supervise ADLs successfully. Impaired awareness has a potential economic impact on aging adults. For example, recent studies have shown that aging adults with impaired awareness use more energy in homes due to deviation from normal execution of ADLs. This often results in incompletely executed ADLs which are left unattended or unsupervised. Unsupervised ADLs involving appliances with high wattage ratings (e.g., TV, air conditioner) can lead to an increase in the cost of home energy services used by aging adults who desire to age-in-place. Thus, in this paper, we propose a situation-aware framework that leverages a smart home resident's context information with respect to unattended ADLs in mitigating increased energy consumptions in smart home environments.
This work presents a real-time system for tracking multiple object in the context of meal preparation when using the Cognitive Orthosis for CoOKing (COOK). This system is called SafeCOOK. It aims to provide more capabilities to detect some dangerous situations that the current system does not consider. For example, it can locate a utensil or other kitchen object that has been left on the cooking surface of the stove while a meal is being prepared. This system uses a hybrid method based on YOLO and KCF to detect, track and drop cooking utensils as they enter and leave the cooking area, and is capable of monitoring an entire cooktop in real-time with a single camera. The software has been implemented on an embedded platform in the smart stove and has been added to it. The system produces good segmentation and tracking results at a frame rate of 1 to 4 frames per second, as demonstrated in extensive experiments using video sequences under different conditions.
This study obtained key performance items related to phototherapy and the standards of light-emitting diode (LED) phototherapy devices through the published literature to secure LED phototherapy device safety in South Korea. Based on these items and IEC 62471-1 for the photobiological safety of lamps and lamp systems, a performance test method and wavelength parameters, which are the main performance indicators of the LED phototherapy device, were derived. We conducted a performance test derived from this study using samples with the same performance as the sample that is being developed. The samples comprised a laser and IR LED, red LED, and blue LED. All samples were within ±5%, of the standard threshold. The 455 nm blue LED and 625 nm red LED were close to the set value with a difference of 1 to 2 nm; their wavelengths’ accuracy was within ±0.32%, ±0%, and ±0.219%, depending on the samples’ wavelengths, which was the same or similar to the error range of ±0%. As for laser and IR LED, the difference from the set value was as large as 2 to 7 nm, and their error ranges were close to ±1%, as shown by ±0.615%, ±0.828%, or ±0.591%, which were larger than the values for red and blue LEDs.
It is difficult for inpatient rehabilitation patients to continue to perform rehabilitation exercises in the community after leaving the hospital. This is because various exercise programs, which are not medically proven, do not reflect the specificity of the individual and are performed collectively due to administrative and financial convenience. The purpose of this study is to evaluate and compare the effects of exercise programs using the Smart Elephant total body exercise device and walking on mental and physical outcomes with real-time monitoring to develop a customized rehabilitation exercise program optimized for people with disabilities. To conduct this study, five non-disabled people living in the community were selected to participate in the exercise programs of Intervention A (walking), Intervention B (walking and cycling), and Intervention C (cycling) for 9 weeks to determine the effects on physical function measures, psychosocial, mental and quality of life health outcomes, participants’ feedback and satisfaction surveys, and changes in Electromyography (EMG), Electrodermal Activity (EDA), Temperature (TEMP), and oxygen saturation (SpO2) during the intervention. It is believed that it can be used as a basis for customized rehabilitation exercise that provides a validated rehabilitation exercise service model for people with disabilities in the community.
The end of the covid-19 epidemic has revealed many weaknesses in the health system and the medical waste treatment process. In particular, the ineffective treatment of medical waste has also contributed to the explosion in the number of infections in some countries (i.e., India, Brazil, and Vietnam). Several studies have found that even developed countries (i.e., with better infrastructure and health services than the world average) have to face emergencies during this time. epidemic. Therefore, the amount of medical waste dumped into the environment is extremely terrible. The waste generated suddenly during this period includes protective gear, masks, and vaccines that burden the waste treatment process. There are several approaches to exploiting Blockchain technologies to solve the problem of direct contact between the stages: medical staff - transportation staff - waste disposal staff to minimize unintended spread. However, to thoroughly solve the current waste classification and treatment processes, a more reward/punishment solution is needed. Specifically, we propose a model to assess the compliance/violation level of waste sorting and treatment in medical centers and isolation areas based on current popular technologies: blockchain, smart contracts, and NFTs.
Machine learning (ML) algorithms have become popular in recent years and have found increasing utility in the field of medical imaging, specifically in positron emission tomography (PET) imaging. The interest in ML in PET imaging for the study of neurodegenerative diseases stems from the potential of these techniques to analyze and predict the physiological parameters of biomarkers such as the total volume of distribution (V _t ) in the organ or a structure of the organ to be explored. In this paper, we investigated whether the V _t of [ ^18 F]-FEPPA radiotracer, an indicator of neuroinflammation, could be estimated directly in a non-invasive way, given the activity of the radiotracer in brain tissue. The study used several regression models to predict the [ ^18 F]-FEPPA V _t in different brain regions where 31 regions of interest were defined for each of 24 patients with Parkinson disease and 20 healthy subjects, and were used to train four tree-based regression models. The predicted and reference values were compared by Bland-Altman analysis and regression model’s performance was evaluated by the mean absolute error (MAE). The best result was obtained by the XGBoost model with a MAE of 2.6. Bland-Altman analysis results indicate that predicted V _t are in average very close to the reference with a bias of 0.23 2.82. Significant main effect of genotype on [ ^18 F]-FEPPA in both caudate and putamen have been preserved by predicted Vt values (p < 0.05). The results of paired t-test indicate that the difference between predicted and reference V _t is not statistically significant in 6 out of 8 groups. The proposed algorithms provide a non-invasive and efficient tool to predict [ ^18 F]-FEPPA V _t values, a hallmark of neuroinflammation that is believed to be a potential trigger for Parkinson’s disease development.
Advancements on the Internet of Things (IoT) have enabled the development of advanced monitoring systems that can track human behavior and vital signs in real-time, which can have a real impact in the way healthcare is provided. This paper presents a system implementation to monitor and analyze a subject's behavior changes over time using IoT, with the objective of detecting the impact of an inhibitor drug on the subject's activity levels. In this research we present a case study by which we showed it is possible to follow the effect of an anticholinergic drug by means of an unobtrusive IoT system. We have monitored the physical activity of a subject in his residence for seven consecutive months to study the effect of the inhibiting drug doses introduced at three known specific timestamps. Following, we compared our detection results for the subject’s physical activity change timestamps with the medical staff medication doses timestamps. Our results show that we can detect the physical activity change at close timestamps compared to those indicated by the medical staff.
The presented platform architecture and deployed implementation in real-life clinical and home care settings on four Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) study sites, integrates the novel working tools for improved disease management with the initial releases of the AI models for disease monitoring. The described robust industry-standard scalable platform is to be a referent example of the integration approach based on loose coupling APIs and industry open standard human-readable and language-independent interface specifications, and its successful baseline implementation for further upcoming releases of additional and more advanced AI models and supporting pipelines (such as for ALS and MS progression prediction, patient stratification, and ambiental exposure modelling) in the following development.
The purpose of this study was to investigate the effects of the Korean Smart Home Modification Program (KSHMP) on the activities of daily living and health-related quality of life of people with physical disabilities. The study used a single-group pre-post design with 10 people with physical disabilities living at home. During the intervention period, the KSHMP was implemented, which included occupational profile, smart home installation, setup, training, task-based feedback, and monitoring. Post-intervention changes in activities of daily living and health-related quality of life were assessed with the Canadian Occupational Performance Measure (COPM) and EuroQual-5 Dimensions (EQ-5D). After the KSHMP, all 10 subjects improved their activities of daily living and quality of life. In addition, the occupational performance of all subjects was maintained. These results show that a customized smart home has a positive impact on improving the activities of daily living and quality of life of people with retardation and is an efficient alternative.
This study aimed to quantitatively analyze pressure parameters in different high-risk areas depending on the position. We reviewed the clinical records of trials of 20 healthy adults on a multi-actuated bed accompanied with pressure sensor mat. We collected average, maximal, minimal pressure, and area in the supine and bilateral side-tilt positions. Also, we analyzed the difference between each at-risk area, depending on positions. In the supine position, pressure parameters of the head, shoulders, sacrum, coccyx, and heels showed significant differences, except between the right and left heels. In the right side-tilt position, all pressure measurements of the ear, shoulder, elbow, hip, knee, and lateral ankle were significantly different. In the left side-tilt position, most of the pressure parameters of the ear, shoulder, elbow, hip, knee, and lateral ankle were significantly different, except between the elbow and ankle. We found that frequent position changing is more important than achieving optimum positioning.
Remote patient monitoring (RPM) has been gaining popularity recently. However, health data acquisition is a significant challenge associated with patient monitoring. In continuous RPM, health data acquisition may miss health data during transmission. Missing data compromises the quality and reliability of patient risk assessment. Several studies suggested techniques for analyzing missing data; however, many are unsuitable for RPM. These techniques neglect the variability of missing data and provide biased results with imputation. Therefore, a holistic approach must consider the correlation and variability of the various vitals and avoid biased imputation. This paper proposes a coherent computation pattern-matching technique to identify and predict missing data patterns. The performance of the proposed approach is evaluated using data collected from a field trial. Results show that the technique can effectively identify and predict missing patterns.
Arm support is a typical assistive technology device to assist shoulder and elbow movements in those with reduced upper extremity muscle strength due to neurological lesions. Recently, the assisting method, range, and manipulation method of arm support are also changing with the development of technology. Accordingly, an assessment system is required for appropriate matching and measuring the effectiveness in the clinical field. This study examines the direction of the assessment process by analyzing studies that measured the effectiveness of arm support through a systematic literature review. The databases were collected using Embase, CINAHL Plus with Full text, Web of Science, and Scopus. 19 studies were selected according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow chart. Most studies have been conducted on people with neuromuscular disorders. Assessment area included performance, function, usability, satisfaction, and psychosocial impact. And more than half of the studies measured performance and function. There were various assessment methods to measure the effectiveness including assessment tools, kinematics, physical examination, questionnaire, observation, and EMG, with assessment tools accounting for more than half of the studies. Most studies have set up assessment environments based on tasks related to ADLs and IADLs to measure the effectiveness. Currently, various methods such as assessment tools and kinematics were applied to measure the effectiveness of arm support. The assessment tool was used the most among them. However, assistive technology-based assessment tools are extremely limited. Therefore, it is required to develop an assessment tool centered on assistive technology based on performance related to ADL and IADL in the future.
