• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.51-62
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• 2022

This study suggests a design of predictive modeling for a hospital fall risk based on inpatients' posture. Inpatient's profile, medical history, and body measurement data along with basic information about a bed they use, were used to predict a fall risk and suggest an algorithm to determine the level of risk. Fall risk prediction is largely divided into two parts: a real-time fall risk evaluation and a qualitative fall risk exposure assessment, which is mostly based on the inpatient's profile. The former is carried out by recognizing an inpatient's posture in bed and extracting rule-based information to measure fall risk while the latter is conducted by medical staff who examines an inpatient's health status related to hospital fall risk and assesses the level of risk exposure. The inpatient fall risk is determined using a sigmoid function with recognized inpatient posture information, body measurement data and qualitative risk assessment results combined. The procedure and prediction model suggested in this study is expected to significantly contribute to tailored services for inpatients and help ensure hospital fall prevention and inpatient safety.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.27-37
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• 2020

Recently, big data is rapidly emerging as a core technology in the 4th industrial revolution. Further, the utilization and the demand of drones are continuously increasing with the development of the 4th industrial revolution. However, as the drones usage increases, the risk of drones falling increases. Drones always have a risk of being able to fall easily even with small problems due to its simple structure. In this paper, in order to predict the risk of drone fall and to prevent the fall, ESC (Electronic Speed Control) is attached integrally with the drone's driving motor and the acceleration sensor is stored to collect the vibration data in real time. By processing and monitoring the data in real time and analyzing the data through big data obtained in such a situation using a Fast Fourier Transform (FFT) algorithm, we proposed a prediction system that minimizes the risk of drone fall by analyzing big data collected from drones.

• Journal of Korean Clinical Nursing Research
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• v.26 no.2
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• pp.265-273
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• 2020

Purpose: The purpose of this study was to test the predictive validity of the Fall Assessment Scale-Korean version (FAS-K) and to find the most appropriate cutoff score to screen high-risk fall groups in adult patients in general hospitals in Korea. Methods: We performed a prospective evaluation study in medical and surgical ward patients at two major general hospitals in Seoul. Data were collected from Nov. 1, 2018 to Feb. 28, 2019, nurses performed 651 observation series. The researcher measured the fall risk assessment score by applying FAS-K, MFS (Morse Fall Scale), and JHFRAT (Johns Hopkins Hospital Fall Risk Assessment tool) to the patients twice a week between 10 am and 12 noon. Data were analyzed using Pearson's corelation coefficients, and the sensitivity, specificity, predictive value, and the area under the curve (AUC) of the three tools. Results: The FAS-K was positively correlated with the MFS (r=.70, p<.001) and the JHFRAT (r=.82, p<.001). According to the receiver operating characteristics (ROC) curve analysis of the FAS-K, sensitivity, specificity, and positive and negative prediction values were 85.3%, 49.4%, 8.5%, and 98.4%, respectively, when the FAS-K score was 4. Therefore, the cut-off score of the FAS-K to identify groups with high fall risk was 4. Conclusion: The FAS-K is a valid tool for measuring fall risk in adult inpatients. In addition, the FAS-K score, 4, can be used to identify high-risk fall groups and know specific points in time to provide active interventions to prevent falls.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2987-3002
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• 2023

In 2015, the number of senior citizens aged 65 and over in Korea was 6,662,400, accounting for 13.1% of the total population. Along with these social phenomena, risk information related to the elderly is increasing every year. In particular, a fall accident caused by a fall can cause serious injury to an elderly person, so special attention is required. Therefore, in this paper, we implemented a system that monitors fall accidents and informs them in real time to minimize damage caused by falls. To this end, beacon-based indoor location positioning was performed and biometric information based on an integrated module was collected using various sensors. In other words, a multi-functional sensor integration module was designed based on Arduino to collect and monitor user's temperature, heart rate, and motion data in real time. Finally, through the analysis and prediction of measurement signals from the integrated module, damage from fall accidents can be reduced and rapid emergency treatment is possible. Through this, it is possible to reduce the damage caused by a fall accident, and rapid emergency treatment will be possible. In addition, it is expected to lead a new paradigm of safety systems through expansion and application to socially vulnerable groups.

• Physical Therapy Rehabilitation Science
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• v.7 no.2
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• pp.54-60
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• 2018

Objective: Persons with chronic stroke fall more often than healthy elderly individuals. The Timed Up and Go test (TUG) is used as a fall prediction tool, but only provides a result for the total measurement time. This study aimed to determine the optimal cut-off values for each of the 6 components of the TUG. Design: Retrospective study. Methods: Thirty persons with chronic stroke participated in the study. TUG evaluation was performed using a wearable miniaturized inertial sensor. Sensitivity, specificity, and predictive values were calculated using the Receiver Operating Characteristic (ROC) curve analysis for the measured values in each section. Optimal values for fall risk classification were determined. Logistic regression analysis was used to investigate the risk of future falls based on TUG. Results: The cut-off values of the 6 sections of the TUG were determined, as follows: sit-to-stand >2.00 seconds (p<0.05), forward gait >4.68 seconds (p<0.05), mid-turn >3.82 seconds (p<0.05), return gait >4.81 seconds (p<0.05), end-turn >2.95 seconds (p<0.05), and stand-to-sit >2.13 seconds (p<0.05). The risk of falling increased by 2.278 times when the mid-turn value was >3.82 seconds (p<0.05). Conclusions: The risk of falls increased by 2.28 times when the value of the mid-turn interval exceeded 3.82 seconds. Therefore, when interpreting TUG results, the predictive accuracy for falls will be higher when the measurement time for each section is analyzed, together with the total time for TUG.

• Journal of Korean Academy of Nursing Administration
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• v.17 no.4
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• pp.484-492
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• 2011

Purpose: The aims of study were; (1) to evaluate the validity and sensitivity of a fall-risk assessment tool, and (2) to establish continuous quality improvement (CQI) methods to monitor the effective use of the risk assessment tool. Methods: A retrospective case-control cohort design was used. Analysis was conducted for 90 admissions as cases and 3,716 as controls during the 2006 and 2007 calendar years was conducted. Fallers were identified from the hospital’s Accident Reporting System, and non-fallers were selected by randomized selection. Accuracy estimates, sensitivity analysis and logistic regression were used. Results: At the lower cutoff score of one, sensitivity, specificity, and positive and negative predictive values were 82.2%, 19.3%, 0.03%, and 96.9%, respectively. The area under the ROC was 0.60 implying poor prediction. Logistic regression analysis showed that five out of nine constitutional items; age, history of falls, gait problems, and confusion were significantly associated with falls. Based on these results, we suggested a tailored falls CQI process with specific indexes. Conclusion: The fall-risk assessment tool was found to need considerable reviews for its validity and usage problems in practice. It is also necessary to develop protocols for use and identify strategies that reflect changes in patient conditions during hospital stay.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.12 no.1
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• pp.46-52
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• 2018

The purpose of this study was to investigate the factors affecting falls in 107 elderly living in the city aged 65 or older by evaluating general characteristics, chronic disease status, medical variables related to falls, balance-related confidence, physical ability and depression. Also, the correlations between the significant differences in variables were identified, and the prediction power was determined by deriving the variables with high influence to induce the fall. In the faller group, urinary incontinence, foot pain, lower extremity weakness, number of chronic disease and medication use were significantly higher than those of the nonfaller group. Also, statistically significant differences were evaluated in ABC (Activities-specific Balance Confidence) score, BBS (Berg Balance Scale) score, SGDS (Short Geriatric Depression Scale), FRT (Functional Reach Test) value. The main correlated factor for fall was ABC score, the lower the ABC score, fall risk is increased which is a significant negative impact. When the evaluation is performed by combining those scales, the hit ratio to classify whether faller or nonfaller is increased to 70.01% which is quite higher value.

• Physical Therapy Korea
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• v.21 no.2
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• pp.37-47
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• 2014

Falls are common after stroke and most frequently related to loss of balance while walking. Consequently, preventing falls is one of the goals of acute, rehabilitative, and chronic stroke care. The purpose of this study was to investigate the incidence and risk factors of falls and to determine how well the Falls Efficacy Scale (FES), Timed Up and Go test (TUG), and Berg Balance Scale (BBS) could distinguish between fallers and non-fallers among stroke patients during inpatient rehabilitation. One hundred and fifteen participants with at least 3 months post-stroke and able to walk at least 3 m with or without a mono cane participated in this study. Fifty-four (47%) participants reported falling, and 15 (27.8%) had a recurrent fall. Logistic regression analysis for predicting falls showed that left hemiplegia [odds ratio (OR)=4.68] and fear of falling (OR=5.99) were strong risk factors for falls. Fallers performed worse than non-fallers on the FES, TUG, and BBS (p<.05, p<.01, respectively). In the receiver operator characteristic curve analysis, the TUG demonstrated the best discriminating ability among the three assessment tools. The cut-off score was 22 seconds on the TUG for discriminating fallers from non-fallers (sensitivity=88.9%, specificity=45.9%) and 27 seconds for discriminating recurrent fallers from single fallers and non-fallers (sensitivity=71.4%, specificity=40.2%). Results suggest that there is a need for providing fall prevention and injury minimization programs for stroke patients who record over 22 seconds on the TUG.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.665-673
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• 2021

Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies' bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company's default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.