scholarly journals Gender and Parity in Statistical Prediction of Anterior Carry Hand-Loads from Inertial Sensor Data

2019 ◽  
Vol 63 (1) ◽  
pp. 1142-1146
Author(s):  
Sol Lim ◽  
Clive D’Souza
Keyword(s):  
Prediction Models ◽  
Inertial Sensor ◽  
Load Level ◽  
Gender Effects ◽  
Classification Model ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Statistical Prediction ◽  
Men And Women ◽  
Gender Specific

The objective of this study was to examine potential gender effects on the performance of a statistical algorithm for predicting hand-load levels that uses body-worn inertial sensor data. Torso and pelvic kinematic data was obtained from 11 men and 11 women in a laboratory experiment while they carried anterior hand-loads of 13.6 kg, and 22.7 kg, and during unloaded walking. Nine kinematic variables expressed as relative changes from unloaded gait were calculated and used as predictors in a statistical classification model predicting load-level (no-load, 13.6 kg, and 22.7 kg). To compare effects of gender on prediction accuracy, prediction models were built using both, gender-balanced gait data and gender-specific data (i.e., separate models for men and women) and evaluated using hold-out validation techniques. The gender-balanced model correctly classified load levels with an accuracy of 74.2% and 80.0% for men and women, respectively. The gender-specific models had accuracies of 68.3% and 85.0% for men and women, respectively. Findings indicated a lack of classification parity across gender, and possibly across other types of personal attributes such as age, ethnicity, and health condition. While preliminary, this study hopes to draw attention to challenges in algorithmic bias, parity and fairness, particularly as machine learning techniques gain popularity in ergonomics practice.

Gender effects on odor-induced taste enhancement and subsequent food consumption

2020 ◽  
Vol 37 (5) ◽  
pp. 511-519
Author(s):  
Yamen Koubaa ◽  
Amira Eleuch
Keyword(s):  
Food Consumption ◽  
Free Food ◽  
Gender Effects ◽  
Taste Perception ◽  
Personal Factors ◽  
Men And Women ◽  
Content Type ◽  
Gender Specific ◽  
Taste Enhancement ◽  
Detection And Recognition

Purpose The purpose of this paper is to test for gender-specific effects on odor-induced taste enhancement and subsequent food consumption in olfactory food marketing. Design/methodology/approach Lab experiments conducted among female and male participants using vanillin as a stimulus and ratings of sweetness, taste pleasantness and eating of sugar-free food as measures. Findings Odor-induced taste enhancement is gender-specific. Female consumers outperform male consumers in olfactory reaction and sweetness perception. While men outperform women in food consumption. Research limitations/implications Odor intensity was set to the concentration level of 0.00005per cent according to the findings from (Fujimaru and Lim, 2013). The authors believe that this intensity level is appropriate for both men and women. Still, there may be some gender effects on intensity levels, which are not explored here. The author’s test for the effects of one personal factor, gender and odor-induced taste enhancement of sugar-free food. The authors think that investigating the combined effects of more personal factors such as age, culture and so on adds to the accuracy of the results. Practical implications It seems that the stronger sensory capacities of women in terms of odor detection and recognition already confirmed in the literature extends to the cross-modal effects of this sensory detection and recognition on taste enhancement. It seems appropriate to tailor olfactory food advertising according to the gender of the target audience. Originality/value Odor-induced taste enhancement is still a novel subject in marketing. While most of the research has investigated the effects of smelling congruent odors on taste perception and food consumption among mixed groups of men and women, the value of this paper lies in the investigation of the potential moderating effects of gender on this relationship.

scholarly journals An IoT-Based Smart Building Solution for Indoor Environment Management and Occupants Prediction

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2959
Author(s):  
Alessandro Floris ◽  
Simone Porcu ◽  
Roberto Girau ◽  
Luigi Atzori
Keyword(s):  
Machine Learning ◽  
Indoor Environment ◽  
Prediction Models ◽  
Pearson Correlation ◽  
Environmental Parameters ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Environment Management ◽  
Smart Building

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

Development and validation of a predictive model to assess an individual's risk of pancreatic cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 4045-4045
Author(s):  
Byung-Ho Nam ◽  
Ami Yu ◽  
Sang Myung Woo ◽  
Hye-Ryung Yang ◽  
Jungnam Joo ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Fasting Glucose ◽  
Risk Group ◽  
Risk Prediction Models ◽  
Men And Women ◽  
Urine Glucose ◽  
Gender Specific

4045 Background: Due to the very low survival of pancreatic cancer (PC), early detection is a critical strategy to improve the outcome of PC.Screening individuals with genetic syndromes associated with a high incidence of PC or families predisposed to PC is increasing. However, those populations account for only 10% of all PC cases. A different approach for developing an effective surveillance tool is needed to identify high-risk individuals without hereditary risks. The goal of this study was to develop and validate risk prediction models for filtering purposes as part of the sporadic PC surveillance activities. Methods: Based on an eight-year follow-up of a cohort study involving 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997, gender-specific risk prediction models were developed using the Cox proportional hazards model. The models were validated using independent data of 500,046 men and 627,629 women who had biennial examinations in 1998-1999. The models’ performance was evaluated with respect to their discrimination and calibration abilities based on the C-statistic and the Hosmer-Lemeshow (H-L) type χ2-statistic. Results: Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the model for men. In the model for women, height, BMI, fasting glucose, urine glucose, smoking, and drinking habits were included. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. Model validation showed excellent model performance with C-statistics (95% confidence interval) of 0.813 (0.800−0.826) and 0.804 (0.788−0.820) for men and women, respectively. The H-L type χ2-statistics (P-values) were 7.478 (0.587) and 10.297 (0.327) for men and women, respectively. Five different risk groups could be identified with hazard ratios (HR) greater than 20 in the highest risk group compared to the lowest risk group in both the men and women. Conclusions: Gender-specific individualized risk prediction models for PC were developed and validated with a high level of performance. These models can be used to identify high-risk individuals who may benefit from increased surveillance of PC.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

2021 ◽  
Vol 13 (7) ◽  
pp. 3870
Author(s):  
Mehrbakhsh Nilashi ◽  
Shahla Asadi ◽  
Rabab Ali Abumalloh ◽  
Sarminah Samad ◽  
Fahad Ghabban ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Sustainability Assessment ◽  
Regression Trees ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Sustainability Performance ◽  
Learning Techniques ◽  
Self Organizing

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira
Keyword(s):  
Machine Learning ◽  
Environmental Monitoring ◽  
Data Quality ◽  
Outlier Detection ◽  
Prediction Models ◽  
Sensor Data ◽  
Natural Phenomenon ◽  
Monitoring Systems ◽  
Data Errors ◽  
Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

scholarly journals On Expressive Features for Gait Analysis using Lower Limb Inertial Sensor Data

2020 ◽  
Vol 53 (2) ◽  
pp. 15990-15997
Author(s):  
Felix Laufer ◽  
Michael Lorenz ◽  
Bertram Taetz ◽  
Gabriele Bleser
Keyword(s):  
Gait Analysis ◽  
Lower Limb ◽  
Inertial Sensor ◽  
Sensor Data
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