Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Tae Hyong Kim ◽  
Ahnryul Choi ◽  
Hyun Mu Heo ◽  
Kyungran Kim ◽  
Kyungsuk Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Continuous Time ◽  
Classification Accuracy ◽  
Fall Detection ◽  
Series Data ◽  
Machine Learning Classifiers ◽  
Detection Model ◽  
Learning Classifiers ◽  
Proposed Model

Pre-impact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust pre-impact fall detection model was developed to classify various activities and falls as multiclass and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection (ILFS) algorithm, auto labeling of activities, and application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multiclass showed significantly higher average classification accuracy of 99.57 ± 0.01% for discrete data-based classifiers and 99.84 ± 0.02% for continuous time series-based classifiers than previous models (p < 0.01). In the future, multiclass pre-impact fall detection models can be applied to fall protector devices by detecting various activities for sending alerts or immediate feedback reactions to prevent falls.

scholarly journals Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

2021 ◽  
Author(s):  
Harisu Abdullahi Shehu ◽  
William Browne ◽  
Hedwig Eisenbarth
Keyword(s):  
Machine Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Classification Accuracy ◽  
Video Frame ◽  
Video Databases ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Good Classification ◽  
Sequential Voting

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.

Flow Instability Detection in Offshore Oil Wells with Multivariate Time Series Machine Learning Classifiers

2021 ◽  
Author(s):  
Bruno Guilherme Carvalho ◽  
Ricardo Emanuel Vaz Vargas ◽  
Ricardo Menezes Salgado ◽  
Celso Jose Munaro ◽  
Flavio Miguel Varejao
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Flow Instability ◽  
Multivariate Time Series ◽  
Oil Wells ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Offshore Oil

scholarly journals Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

2021 ◽  
Author(s):  
Harisu Abdullahi Shehu ◽  
William Browne ◽  
Hedwig Eisenbarth
Keyword(s):  
Machine Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Classification Accuracy ◽  
Video Frame ◽  
Video Databases ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Good Classification ◽  
Sequential Voting

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.

scholarly journals Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

2020 ◽  
Vol 24 (21) ◽  
pp. 16453-16482 ◽  
Author(s):  
Pradeep Hewage ◽  
Ardhendu Behera ◽  
Marcello Trovati ◽  
Ella Pereira ◽  
Morteza Ghahremani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
State Of The Art ◽  
Series Data ◽  
Proposed Model ◽  
Medium Range ◽  
Weather Stations ◽  
Local Weather

Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.

scholarly journals New statistical model for misreported data with application to current public health challenges

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David Moriña ◽  
Amanda Fernández-Fontelo ◽  
Alejandra Cabaña ◽  
Pedro Puig
Keyword(s):  
Public Health ◽  
Time Series ◽  
Human Papillomavirus ◽  
Continuous Time ◽  
Time Series Data ◽  
Real Data ◽  
Current Data ◽  
Series Data ◽  
Proposed Model ◽  
Public Health Challenges

AbstractThe main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.

A feature-centric spam email detection model using diverse supervised machine learning algorithms

2020 ◽  
Vol 38 (3) ◽  
pp. 633-657
Author(s):  
Ammara Zamir ◽  
Hikmat Ullah Khan ◽  
Waqar Mehmood ◽  
Tassawar Iqbal ◽  
Abubakker Usman Akram
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Research Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Content Type ◽  
Detection Model ◽  
Proposed Model

Purpose This research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection. Design/methodology/approach Existing studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam). Findings Analysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%. Originality/value This research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.

scholarly journals Detection and Quantification of Resting Tremor in Parkinson’s Disease Using Long-Term Acceleration Data

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Han Yuan ◽  
Sen Liu ◽  
Jiali Liu ◽  
Hai Lin ◽  
Cuiwei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Classifiers ◽  
Detection Model ◽  
Resting Tremor ◽  
Learning Classifiers ◽  
Acceleration Data ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Detection And Quantification

Long-term monitoring of resting tremor is key to assess the status of patients suffering from Parkinson’s disease (PD), which is of vital importance for reasonable medication. The detection and quantification of resting tremor in reported works rely heavily on specified movements and are not appropriate for long-term monitoring in real-life condition. The purpose of this study is to develop a detection model for long-term monitoring of resting tremor and explore an effective indicator for tremor quantification. This study included long-term acceleration data from PD patients and proposed a resting tremor detection model based on machine learning classifiers and Synthetic Minority Oversampling Technique (SMOTE). Four machine learning classifiers, K-Nearest Neighbor (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM), were compared. Furthermore, an indicator called tremor timing ratio (TTR) was defined and calculated for tremor quantification. The detection model with RF classifier achieved the highest overall accuracy of 94.81%. The sample entropy of the acceleration signal was proved most influential in the classification by exploring the feature importance. Through the Kruskal-Wallis test and the Mann-Whitney U test, the TTR had a strong correlation with the subscore of resting tremor in Unified Parkinson Disease Rating Scale (UPDRS). Such two-step evaluation process for resting tremor can detect the tremor effectively and is expected to be applied in long-term monitoring of PD patients in daily life to realize a more comprehensive assessment of PD.

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