scholarly journals Monitoring of sitting postures with sensor networks in controlled and free-living environments: A systematic review (Preprint)

2020 ◽  
Author(s):  
Arpita Kappattanavar ◽  
Nico Steckhan ◽  
Jan Philipp Sachs ◽  
Bert Arnrich ◽  
Erwin Böttinger
Keyword(s):  
Pressure Sensors ◽  
Classification Problem ◽  
Living Environment ◽  
Free Living ◽  
Data Set ◽  
Posture Analysis ◽  
Sitting Posture ◽  
Prolonged Sitting ◽  
Medium Quality

BACKGROUND Background: Prolonged sitting postures have been reported to increase the probability of developing low back pain. Moreover, the majority of employees in the industrial world work ninety percent of their time in a seated position. OBJECTIVE This review focuses on the technologies and algorithms that have been used to classify seating postures on a chair with respect to spine and limb movements. METHODS Three electronic literature databases have been surveyed to identify the studies classifying sitting posture in adults. Fourteen articles have been finally shortlisted. These articles were categorized into low, medium, and high quality. Most of the articles were categorized as medium quality (12/14). RESULTS The majority of the studies used pressure sensors (13/14) to classify sitting postures. Neural Networks were the most frequently (6/14) used approaches for classifying sitting postures. CONCLUSIONS Based on the current study the classification of sitting posture is still in the nascent stage and hence, we would suggest personalized sitting posture analysis. Furthermore, the review emphasizes identifying at least five basic postures along with different limb and spine movements in a free-living environment. It is essential to annotate the data set with ground truths for subsequent training of the classifier to solve the sitting posture classification problem.

scholarly journals Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

10.2196/21105 ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. e21105
Author(s):  
Arpita Mallikarjuna Kappattanavar ◽  
Nico Steckhan ◽  
Jan Philipp Sachs ◽  
Harry Freitas da Cruz ◽  
Erwin Böttinger ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Pressure Sensors ◽  
Ground Truth ◽  
Living Environment ◽  
Measurement Unit ◽  
Future Research ◽  
Free Living ◽  
Medium Quality ◽  
Underlying Causes ◽  
Future Work

Background A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain. Objective This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements. Methods A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases in the shortlisted papers. Results A total of 14 papers were shortlisted from 952 papers obtained after a systematic search. The majority of the studies used pressure sensors to measure sitting postures, whereas neural networks were the most frequently used approaches for classification tasks in this context. Only 2 studies were performed in a free-living environment. Most studies presented ethical and methodological shortcomings. Moreover, the findings indicate that the strategic placement of sensors can lead to better performance and lower costs. Conclusions The included studies differed in various aspects of design and analysis. The majority of studies were rated as medium quality according to our assessment. Our study suggests that future work for posture classification can benefit from using inertial measurement unit sensors, since they make it possible to differentiate among spine movements and similar postures, considering transitional movements between postures, and using three-dimensional cameras to annotate the data for ground truth. Finally, comparing such studies is challenging, as there are no standard definitions of sitting postures that could be used for classification. In addition, this study identifies five basic sitting postures along with different combinations of limb and spine movements to help guide future research efforts.

Sitting posture analysis by pressure sensors

2008 ◽  
Author(s):  
Kazuhiro Kamiya ◽  
Mineichi Kudo ◽  
Hidetoshi Nonaka ◽  
Jun Toyama
Keyword(s):  
Pressure Sensors ◽  
Posture Analysis ◽  
Sitting Posture

scholarly journals Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study

2021 ◽  
Author(s):  
Lacey H Etzkorn ◽  
Amir S Heravi ◽  
Katherine C Wu ◽  
Wendy S Post ◽  
Jacek K Urbanek ◽  
...  
Keyword(s):  
Cohort Study ◽  
Living Environment ◽  
Change Point Detection ◽  
Classification Algorithms ◽  
Accelerometer Data ◽  
Multicenter Aids Cohort Study ◽  
Free Living ◽  
Living Body ◽  
Posture Classification

As health studies increasingly monitor free-living heart performance via ECG patches with ac- celerometers, researchers will seek to investigate cardio-electrical responses to physical activity and sedentary behavior, increasing demand for fast, scalable methods to process accelerometer data. We provide the first published analysis of tri-axial accelerometry data from Zio XT patch and introduce an extension of posture classification algorithms for use with ECG patches worn in the free-living environment. Our novel extensions to posture classification include (1) estimation of an upright posture for each individual without the reference measurements used by existing posture classification algorithms; (2) correction for device removal and re-positioning using novel spherical change-point detection; and (3) classification of upright and recumbent periods using a clustering and voting process rather than a simple inclination threshold used in other algorithms. Methods were built using data from 14 participants from the Multicenter AIDS Cohort Study (MACS), and applied to 1, 250 MACS participants. As no posture labels exist in the free-living environment, we evaluate the algorithm against labelled data from the Towson Accelerometer Study and against data labelled by hand from the MACS study.

Optimising Object Classification

2008 ◽  
pp. 234-253 ◽  
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Object Classification ◽  
Original Data ◽  
Classification Problem ◽  
Uncertain Reasoning ◽  
Objective Functions ◽  
Data Set ◽  
Dempster Shafer Theory ◽  
Novel Approach ◽  
Shafer Theory

This chapter investigates the effectiveness of a number of objective functions used in conjunction with a novel technique to optimise the classification of objects based on a number of characteristic values, which may or may not be missing. The classification and ranking belief simplex (CaRBS) technique is based on Dempster-Shafer theory and, hence, operates in the presence of ignorance. The objective functions considered minimise the level of ambiguity and/or ignorance in the classification of companies to being either failed or not-failed. Further results are found when an incomplete version of the original data set is considered. The findings in this chapter demonstrate how techniques such as CaRBS, which operate in an uncertain reasoning based environment, offer a novel approach to object classification problem solving.

scholarly journals A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Feng Hu ◽  
Xiao Liu ◽  
Jin Dai ◽  
Hong Yu
Keyword(s):  
Rough Set Theory ◽  
Classification Problem ◽  
Boundary Region ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Imbalance Data ◽  
Average Accuracy ◽  
Fold Cross Validation

The classification problem for imbalance data is paid more attention to. So far, many significant methods are proposed and applied to many fields. But more efficient methods are needed still. Hypergraph may not be powerful enough to deal with the data in boundary region, although it is an efficient tool to knowledge discovery. In this paper, the neighborhood hypergraph is presented, combining rough set theory and hypergraph. After that, a novel classification algorithm for imbalance data based on neighborhood hypergraph is developed, which is composed of three steps: initialization of hyperedge, classification of training data set, and substitution of hyperedge. After conducting an experiment of 10-fold cross validation on 18 data sets, the proposed algorithm has higher average accuracy than others.

scholarly journals Assistive Devices to Help Correct Sitting-Posture Based on Posture Analysis Results

2021 ◽  
Vol 5 (3) ◽  
pp. 340
Author(s):  
Seung-Min Lee ◽  
Hyeon-Ju Kim ◽  
So-Jeong Ham ◽  
Sunhee Kim
Keyword(s):  
Pressure Sensors ◽  
Ultrasonic Sensor ◽  
Assistive Devices ◽  
Assistive Device ◽  
Raspberry Pi ◽  
Posture Analysis ◽  
Sitting Posture ◽  
Initial Symptoms ◽  
Long Time

As many people spend a lot of time sitting on a chair, diseases such as turtle neck, straight neck, caused by incorrect posture have been increasing. Preventing these diseases and treating initial symptoms is helpful just by sitting properly. However, when people sit, their postures become disturbed without their knowledge. In this paper, we propose an assistive device in the form of a chair that helps people to sit properly and helps correct their sitting posture. The assistive device is equipped with pressure sensors capable of measuring the distribution of pressure applied to the floor of the chair, and an ultrasonic sensor capable of measuring the distance between the user's back and the chair back. First, an ultrasonic sensor and pressure sensors are used to determine the user's posture, and if the user's posture is not correct, an alarm is sent to the user to help the user to correct the posture by himself. Second, stretching information is provided according to the degree of distribution of pressure measured by the pressure sensors, and pressures are applied to the user's back with press-type cushions to help the user sit in a correct posture. In addition, even when sitting in a chair for a long time, an alarm is triggered to induce a person to rise from the chair. After implementing the system based on Raspberry Pi, each operation was checked. Furthermore, it was confirmed through the experiment participants that the proposed assistive device can help people correct their sitting posture.

scholarly journals Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification

2020 ◽  
pp. 1-9
Author(s):  
Supun Nakandala ◽  
Marta M. Jankowska ◽  
Fatima Tuz-Zahra ◽  
John Bellettiere ◽  
Jordan A. Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Logistic Regression ◽  
Random Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Free Living ◽  
Data Set

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

Detection of Physical Activity Types Using Triaxial Accelerometers

2014 ◽  
Vol 11 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Jørgen Skotte ◽  
Mette Korshøj ◽  
Jesper Kristiansen ◽  
Christiana Hanisch ◽  
Andreas Holtermann
Keyword(s):  
Physical Activity ◽  
Sensitivity And Specificity ◽  
Standardized Test ◽  
Test Procedure ◽  
Pressure Sensors ◽  
High Sensitivity ◽  
Threshold Values ◽  
Free Living ◽  
Sitting Posture ◽  
Activity Types

Background:The aim of this study was to validate a triaxial accelerometer setup for identifying everyday physical activity types (ie, sitting, standing, walking, walking stairs, running, and cycling).Methods:Seventeen subjects equipped with triaxial accelerometers (ActiGraph GT3X+) at the thigh and hip carried out a standardized test procedure including walking, running, cycling, walking stairs, sitting, and standing still. A method was developed (Acti4) to discriminate between these physical activity types based on threshold values of standard deviation of acceleration and the derived inclination. Moreover, the ability of the accelerometer placed at the thigh to detect sitting posture was separately validated during free living by comparison with recordings of pressure sensors in the hip pockets.Results:Sensitivity for discriminating between the physical activity types sitting, standing, walking, running, and cycling in the standardized trials were 99%–100% and 95% for walking stairs. Specificity was higher than 99% for all activities. During free living (140 hours of measurements), sensitivity and specificity for detection of sitting posture were 98% and 93%, respectively.Conclusion:The developed method for detecting physical activity types showed a high sensitivity and specificity for sitting, standing, walking, running, walking stairs, and cycling in a standardized setting and for sitting posture during free living.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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