scholarly journals Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living

2020 ◽  
Vol 17 (3) ◽  
pp. 1082 ◽  
Author(s):  
Saifur Rahman ◽  
Muhammad Irfan ◽  
Mohsin Raza ◽  
Khawaja Moyeezullah Ghori ◽  
Shumayla Yaqoob ◽  
...  
Keyword(s):  
Physical Activity ◽  
Performance Analysis ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Method Performance ◽  
Light Gradient ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithms

Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms. The study presents the performance analysis of several boosting algorithms (extreme gradient boosting—XGB, light gradient boosting machine—LGBM, gradient boosting—GB, cat boosting—CB and AdaBoost) in a fair and unbiased performance way using uniform dataset, feature set, feature selection method, performance metric and cross-validation techniques. The study utilizes the Smartphone-based dataset of thirty individuals. The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%). These findings suggest the strength of the proposed system in classifying activity of daily living using only the smartphone sensor’s data and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing.

scholarly journals Efficient Seismic Stability Analysis of Embankment Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms

2021 ◽  
Vol 9 ◽  
Author(s):  
Luqi Wang ◽  
Jiahao Wu ◽  
Wengang Zhang ◽  
Lin Wang ◽  
Wei Cui
Keyword(s):  
Stability Analysis ◽  
Water Level ◽  
Seismic Stability ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Primary Concern ◽  
Light Gradient ◽  
Water Level Changes ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithms

Embankments are widespread throughout the world and their safety under seismic conditions is a primary concern in the geotechnical engineering community since the failure events may lead to disastrous consequences. This study proposes an efficient seismic slope stability analysis approach by introducing advanced gradient boosting algorithms, namely Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). A database consisting of 600 datasets is prepared for model calibration and evaluation, where the factor of safety (FS) is regarded as the output and four influential factors are selected as the inputs. For each dataset, the FS corresponding to the four inputs is evaluated using the commercial geotechnical software of Slide2. As an illustration, the proposed approach is applied to the seismic stability analysis of a hypothetical embankment example subjected to water level changes. For comparison, the predictive performance of CatBoost, LightGBM, and XGBoost is investigated. Moreover, the Shapley additive explanations (SHAP) method is used in this study to explore the relative importance of the four features. Results show that all the three gradient boosting algorithms (i.e., CatBoost, LightGBM, and XGBoost) perform well in the prediction of FS for both the training dataset and testing dataset. Among the four influencing factors, the friction angle φ is the most important feature variable, followed by horizontal seismic coefficient Kh, cohesion c, and saturated permeability ks.

Nutritional status, activities of daily living and physical activity in aged residents in 10 German nursing homes*

2007 ◽  
Vol 32 (03) ◽  
Author(s):  
J Bai ◽  
S Lesser ◽  
S Paker-Eichelkraut ◽  
S Overzier ◽  
S Strathmann ◽  
...  
Keyword(s):  
Physical Activity ◽  
Nursing Homes ◽  
Nutritional Status ◽  
Activities Of Daily Living ◽  
Daily Living

scholarly journals Cardio-metabolic and socio-demographic risk factors associated with dependency in basic and instrumental activities of daily living among older Iranian adults: Bushehr elderly health program

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kazem Khalagi ◽  
Akram Ansarifar ◽  
Noushin Fahimfar ◽  
Mahnaz Sanjari ◽  
Safoora Gharibzdeh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Education Level ◽  
Lower Education Level ◽  
History Of ◽  
Elderly Health ◽  
Lower Education ◽  
Demographic Risk ◽  
Demographic Risk Factors

Abstract Background Iran’s population is aging. Disability is a major public health problem for older adults, not only in Iran but all over the world. The purpose of this study was to investigate the relationship between cardio-metabolic and socio-demographic risk factors and disability in people 60 years and older in Iran. Methods The baseline (cross-sectional) data of 2426 samples from the Bushehr Elderly Health (BEH) program was included in the analysis. The participants were selected through multi-stage random sampling in Bushehr, southern Iran. Socio-demographic characteristics, as well as the history of diabetes and other chronic diseases, and smoking were measured using standardized questionnaires. Anthropometric measurements and laboratory tests were performed under standard conditions. Dependency was determined by the questionnaires of basic activities of daily living (BADL) and instrumental activities of daily living (IADL) using Barthel and Lawton scales respectively. Multiple logistic regression was used in the analysis. Results Mean (Standard Deviation) of the participants’ age was 69.3 (6.4) years (range: 60 and 96 years), and 48.1% of the participants were men. After adjusting for potential confounders, being older, being female (OR (95%CI): 2.3 (1.9–2.9)), having a lower education level, a history of diabetes mellitus (OR: 1.4 (1.2–1.7)) and past smoking (OR: 1.3 (1.0–1.6)), and no physical activity (OR: 1.5 (1.2–1.9)) were significantly associated with dependency in IADL. Also, being older and female (OR: 2.4 (1.9–3.0)), having a lower education level, no physical activity (OR: 2.2 (1.6–2.9)) and daily intake of calories (OR: 0.99 (0.99–0.99)) were associated with dependency in BADL. Conclusion Dependency in older adults can be prevented by increasing community literacy, improving physical activity, preventing and controlling diabetes mellitus, avoiding smoking, and reducing daily calorie intake.

scholarly journals Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jong Ho Kim ◽  
Haewon Kim ◽  
Ji Su Jang ◽  
Sung Mi Hwang ◽  
So Young Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Confidence Interval ◽  
Neck Circumference ◽  
Difficult Laryngoscopy ◽  
Gradient Boosting ◽  
Test Set ◽  
Equal Distribution ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Abstract Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

scholarly journals Physical activity in secondary stroke prevention

2019 ◽  
Vol 67 (4) ◽  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Activities Of Daily Living ◽  
Brain Function ◽  
Stroke Prevention ◽  
Daily Living ◽  
Recurrent Stroke ◽  
Treatment Strategies ◽  
Secondary Stroke Prevention

After having a stroke the main challenges are reducing the risk of recurrent stroke, improving impaired brain function, quality of life, independence in activities of daily living and reintegration into the community. [1] Lesion-induced impairment of brain function also has, besides its effects on e.g. motor, sensory, visual and speech function, an influence on e.g. cognition and mood, all of which are determinants of post-stroke physical activity. The evidence for a benefit of physical activity in secondary stroke prevention is increasing and treatment strategies aimed at factors which are limiting physical activity are more and more recognized.

scholarly journals Establishing a Credit Risk Evaluation System for SMEs Using the Soft Voting Fusion Model

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 202
Author(s):  
Ge Gao ◽  
Hongxin Wang ◽  
Pengbin Gao
Keyword(s):  
Credit Risk ◽  
Evaluation System ◽  
Predictive Accuracy ◽  
Assessment System ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fusion Model ◽  
Light Gradient ◽  
Extreme Gradient Boosting ◽  
The Government

In China, SMEs are facing financing difficulties, and commercial banks and financial institutions are the main financing channels for SMEs. Thus, a reasonable and efficient credit risk assessment system is important for credit markets. Based on traditional statistical methods and AI technology, a soft voting fusion model, which incorporates logistic regression, support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), is constructed to improve the predictive accuracy of SMEs’ credit risk. To verify the feasibility and effectiveness of the proposed model, we use data from 123 SMEs nationwide that worked with a Chinese bank from 2016 to 2020, including financial information and default records. The results show that the accuracy of the soft voting fusion model is higher than that of a single machine learning (ML) algorithm, which provides a theoretical basis for the government to control credit risk in the future and offers important references for banks to make credit decisions.

scholarly journals A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living

2021 ◽  
pp. 1-8
Author(s):  
Kerstin Bach ◽  
Atle Kongsvold ◽  
Hilde Bårdstu ◽  
Ellen Marie Bardal ◽  
Håkon S. Kjærnli ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Ground Truth ◽  
Daily Physical Activity ◽  
Gradient Boosting ◽  
Free Living ◽  
Recording Time ◽  
Learning Classifier ◽  
Extreme Gradient Boosting ◽  
Activity Types

Introduction: Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity types. This study aimed to evaluate the performance of a machine learning classifier for detecting sitting, standing, lying, walking, running, and cycling based on a dual versus single accelerometer setups during free-living. Methods: Twenty-two adults (mean age [SD, range] 38.7 [14.4, 25–68] years) were wearing two Axivity AX3 accelerometers positioned on the low back and thigh along with a GoPro camera positioned on the chest to record lower body movements during free-living. The labeled videos were used as ground truth for training an eXtreme Gradient Boosting classifier using window lengths of 1, 3, and 5 s. Performance of the classifier was evaluated using leave-one-out cross-validation. Results: Total recording time was ∼38 hr. Based on 5-s windowing, the overall accuracy was 96% for the dual accelerometer setup and 93% and 84% for the single thigh and back accelerometer setups, respectively. The decreased accuracy for the single accelerometer setup was due to a poor precision in detecting lying based on the thigh accelerometer recording (77%) and standing based on the back accelerometer recording (64%). Conclusion: Key daily physical activity types can be accurately detected during free-living based on dual accelerometer recording, using an eXtreme Gradient Boosting classifier. The overall accuracy decreases marginally when predictions are based on single thigh accelerometer recording, but detection of lying is poor.

scholarly journals Interpretable Machine Learning for Early Neurological Deterioration Prediction in Atrial Fibrillation-Related Stroke

2021 ◽  
Author(s):  
Seong Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
Kyungmi O ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting ◽  
Early Neurological Deterioration ◽  
Feature Importance

Abstract We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multi-center prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanations (SHAP) method to evaluate feature importance. Of the 3,623 stroke patients, the 2,363 who had arrived at the hospital within 24 hours of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.778, 95% CI, 0.726 - 0.830). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the SHAP method can be adjusted to individualize the features’ effects on the predictive power of the model.

scholarly journals Frailty Level Monitoring and Analysis after a Pilot Six-Week Randomized Controlled Clinical Trial Using the FRED Exergame Including Biofeedback Supervision in an Elderly Day Care Centre

2019 ◽  
Vol 16 (5) ◽  
pp. 729 ◽  
Author(s):  
Iranzu Mugueta-Aguinaga ◽  
Begonya Garcia-Zapirain
Keyword(s):  
Physical Activity ◽  
Activities Of Daily Living ◽  
Day Care ◽  
Daily Living ◽  
Statistical Significance ◽  
Control Group ◽  
Study Group ◽  
State Of Health ◽  
Care Centre ◽  
Day Care Centre

Background: Frailty is a status of extreme vulnerability to endogenous and exogenous stressors exposing the individual to a higher risk of negative health-related outcomes. Exercise using interactive videos, known as exergames, is being increasingly used to increase physical activity by improving health and the physical function in elderly adults. The purpose of this study is to ascertain the reduction in the degree of frailty, the degree of independence in activities of daily living, the perception of one’s state of health, safety and cardiac healthiness by the exercise done using FRED over a 6-week period in elderly day care centre. Material and Methods: Frail volunteers >65 years of age, with a score of <10 points (SPPB), took part in the study. A study group and a control group of 20 participants respectively were obtained. Following randomisation, the study group (20) took part in 18 sessions in total over 6 months, and biofeedback was recorded in each session. Results: After 6 weeks, 100% of patients from the control group continued evidencing frailty risk, whereas only 5% of patients from the study group did so, with p < 0.001 statistical significance. In the case of the EQ-VAS, the control group worsened (−12.63 points) whereas the study group improved (12.05 points). The Barthel Index showed an improvement in the study group after 6 weeks, with statistically significant evidence and a value of p < 0.003906. Safety compliance with the physical activity exceeded 87% and even improved as the days went by. Discussion: Our results stand out from those obtained by other authors in that FRED is an ad hoc-designed exergame, significantly reduced the presence and severity of frailty in a sample of sedentary elders, thus potentially modifying their risk profile. It in turn improves the degree of independence in activities of daily living and the perception of one’s state of health, proving to be a safe and cardiac healthy exercise. Conclusions: The study undertaken confirms the fact that the FRED game proves to be a valid technological solution for reducing frailty risk. Based on the study conducted, the exergame may be considered an effective, safe and entertaining alternative.

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