scholarly journals Supervised Expert System for Wearable MEMS Accelerometer-Based Fall Detector

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gabriele Rescio ◽  
Alessandro Leone ◽  
Pietro Siciliano
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Fall Detection ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Posture Analysis ◽  
Mems Accelerometer ◽  
Specificity And Sensitivity ◽  
Controlled Environments ◽  
Wireless Accelerometer

Falling is one of the main causes of trauma, disability, and death among older people. Inertial sensors-based devices are able to detect falls in controlled environments. Often this kind of solution presents poor performances in real conditions. The aim of this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a machine-learning scheme for people fall detection, by using a triaxial MEMS wearable wireless accelerometer. The proposed approach allows to generalize the detection of fall events in several practical conditions. It appears invariant to the age, weight, height of people, and to the relative positioning area (even in the upper part of the waist), overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end user. In order to limit the workload, the specific study on posture analysis has been avoided, and a polynomial kernel function is used while maintaining high performances in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an one-class support vector machine classifier in a stand-alone PC.

scholarly journals Failure analysis on a water pump based on a low-cost MEMS accelerometer and machine learning classifiers

2020 ◽  
Vol 41 (2) ◽  
pp. 171
Author(s):  
Luciane Agnoletti dos Santos Pedotti ◽  
Ricardo Mazza Zago ◽  
Jefferson Cutrim Rocha ◽  
José Gilberto Dalfré Filho ◽  
Mateus Giesbrecht ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rotor Blade ◽  
Accurate Result ◽  
Low Cost ◽  
Failure Diagnosis ◽  
Support Vector ◽  
Water Pump ◽  
Vibration Signals ◽  
Mems Accelerometer ◽  
Machine Learning Classifiers

This work presents a failure diagnosis tool for a water pump using a low-cost MEMS accelerometer. It was inserted three types of failures: rotor blade (new and damaged), pump soleplate tightness (stiff or loose), and cavitation, in this case on three conditions: none, incipient and severe, totaling twelve fault combinations. These conditions were tested under two different speeds to perform the diagnosis, totaling twenty-four tests. In all cases, the vibration signals from axes X, Y, and Z were acquired. Some features extracted from the vibration spectra from X-axis were used to compose the dataset. These data were analyzed employing logistic regression, a linear support vector machine (SVM), and an artificial neural network multilayer perceptron (ANN-MLP). We compared these three techniques of machine learning and evaluated which one was able to obtain the most accurate result. Using the ANN-MLP, the system was able to detect all three types of failures inserted, with about 100% of accuracy on the rotor blade condition, 92% for anchorage faults, and about 99% accuracy on cavitation state. As a conclusion, it is demonstrated that this classifier algorithm can be used to process the data from the low-cost MEMS accelerometer in predictive maintenance as an accurate tool.

A Sensor Fusion Based Pan-Tilt Platform for Activity Tracking and Fall Detection

2017 ◽  
Author(s):  
Zhangjie Chen ◽  
Hanwei Liu ◽  
Yuqiao Wang ◽  
Ya Wang
Keyword(s):  
Sensor Fusion ◽  
Degrees Of Freedom ◽  
Learning Algorithm ◽  
Low Cost ◽  
Fall Detection ◽  
Support Vector ◽  
Fusion Algorithm ◽  
Activity Tracking ◽  
Term Care ◽  
Sensor Module

This paper presents a pan-tilt sensor fusion platform for activity tracking and fall-detection which can work as a reliable surveillance system with long-term care function. A low cost thermal array sensor and a distance sensor are integrated together as the sensor module. The sensor module is installed on a pan-tilt orienting mechanism with two rotation degrees of freedom to increase the field of view while reducing the number of sensors used on-board. The performance of the sensor test platform is analyzed. The location of the indoor object as well as its size can be estimated based on a novel sensor fusion algorithm. The support vector machine (SVM) based machine learning algorithm is applied for fall detection. The preliminary experiment result shows a 95% accuracy to identify falling action from similar normal indoor activity such as sitting and picking up stuff.

scholarly journals Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4335
Author(s):  
Goran Šeketa ◽  
Lovro Pavlaković ◽  
Dominik Džaja ◽  
Igor Lacković ◽  
Ratko Magjarević
Keyword(s):  
Large Scale ◽  
Detection System ◽  
Low Cost ◽  
Fall Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Data Segmentation ◽  
Post Impact

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

scholarly journals A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors

2021 ◽  
Vol 13 ◽  
Author(s):  
Xiaoqun Yu ◽  
Jaehyuk Jang ◽  
Shuping Xiong
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Injury Prevention ◽  
Large Scale ◽  
Inertial Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
Support Vector ◽  
Wearable Inertial Sensors ◽  
Scale Motion

Research on pre-impact fall detection with wearable inertial sensors (detecting fall accidents prior to body-ground impacts) has grown rapidly in the past decade due to its great potential for developing an on-demand fall-related injury prevention system. However, most researchers use their own datasets to develop fall detection algorithms and rarely make these datasets publicly available, which poses a challenge to fairly evaluate the performance of different algorithms on a common basis. Even though some open datasets have been established recently, most of them are impractical for pre-impact fall detection due to the lack of temporal labels for fall time and limited types of motions. In order to overcome these limitations, in this study, we proposed and publicly provided a large-scale motion dataset called “KFall,” which was developed from 32 Korean participants while wearing an inertial sensor on the low back and performing 21 types of activities of daily living and 15 types of simulated falls. In addition, ready-to-use temporal labels of the fall time based on synchronized motion videos were published along with the dataset. Those enhancements make KFall the first public dataset suitable for pre-impact fall detection, not just for post-fall detection. Importantly, we have also developed three different types of latest algorithms (threshold based, support-vector machine, and deep learning), using the KFall dataset for pre-impact fall detection so that researchers and practitioners can flexibly choose the corresponding algorithm. Deep learning algorithm achieved both high overall accuracy and balanced sensitivity (99.32%) and specificity (99.01%) for pre-impact fall detection. Support vector machine also demonstrated a good performance with a sensitivity of 99.77% and specificity of 94.87%. However, the threshold-based algorithm showed relatively poor results, especially the specificity (83.43%) was much lower than the sensitivity (95.50%). The performance of these algorithms could be regarded as a benchmark for further development of better algorithms with this new dataset. This large-scale motion dataset and benchmark algorithms could provide researchers and practitioners with valuable data and references to develop new technologies and strategies for pre-impact fall detection and proactive injury prevention for the elderly.

Feature Extraction and Data Analysis of Basketball Motion Postures: Acquisition with an Inertial Sensor

2021 ◽  
Author(s):  
Zhuqing Li
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Basketball Players ◽  
Posture Analysis ◽  
Sport Training ◽  
Mean Variance

Abstract This paper mainly analyzed the application of inertial sensors in basketball posture analysis. The data of 20 basketball players in different postures were collected by MEMS inertial sensors. The mean, variance, and skewness were taken as features to compare the performance of C4.5, random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM) algorithms in analyzing posture data. It was found that the classification accuracy of the KNN algorithm was around 90%, and the classification accuracy of C4.5, RF, and SVM algorithms was all above 90%. The classification accuracy of the RF algorithm was the highest (98.72%), which was significantly higher than C4.5 and SVM algorithms. The results verified the advantages of the RF algorithm in basketball posture analysis. The research results confirm the reliability of the inertial sensor in the field of motion posture analysis and make some contributions to its application in sport training. This paper provides support for the analysis of motion posture.

scholarly journals Fallen People Detection Capabilities Using Assistive Robot

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 915 ◽  
Author(s):  
Saturnino Maldonado-Bascón ◽  
Cristian Iglesias-Iglesias ◽  
Pilar Martín-Martín ◽  
Sergio Lafuente-Arroyo
Keyword(s):  
Mobile Robots ◽  
High Performance ◽  
Low Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Support Vector ◽  
Svm Classifier ◽  
People Detection ◽  
Person Detection ◽  
Static View

One of the main problems in the elderly population and for people with functional disabilities is falling when they are not supervised. Therefore, there is a need for monitoring systems with fall detection functionality. Mobile robots are a good solution for keeping the person in sight when compared to static-view sensors. Mobile-patrol robots can be used for a group of people and systems are less intrusive than ones based on mobile robots. In this paper, we propose a novel vision-based solution for fall detection based on a mobile-patrol robot that can correct its position in case of doubt. The overall approach can be formulated as an end-to-end solution based on two stages: person detection and fall classification. Deep learning-based computer vision is used for person detection and fall classification is done by using a learning-based Support Vector Machine (SVM) classifier. This approach mainly fulfills the following design requirements—simple to apply, adaptable, high performance, independent of person size, clothes, or the environment, low cost and real-time computing. Important to highlight is the ability to distinguish between a simple resting position and a real fall scene. One of the main contributions of this paper is the input feature vector to the SVM-based classifier. We evaluated the robustness of the approach using a realistic public dataset proposed in this paper called the Fallen Person Dataset (FPDS), with 2062 images and 1072 falls. The results obtained from different experiments indicate that the system has a high success rate in fall classification (precision of 100% and recall of 99.74%). Training the algorithm using our Fallen Person Dataset (FPDS) and testing it with other datasets showed that the algorithm is independent of the camera setup.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Validation of a point-of-care rapid diagnostic test for hepatitis C for use in resource-limited settings

2019 ◽  
Vol 11 (4) ◽  
pp. 314-315
Author(s):  
James S Leathers ◽  
Maria Belen Pisano ◽  
Viviana Re ◽  
Gertine van Oord ◽  
Amir Sultan ◽  
...  
Keyword(s):  
Point Of Care ◽  
Low Cost ◽  
Rapid Diagnosis ◽  
Direct Acting Antivirals ◽  
Resource Limited Settings ◽  
Rapid Diagnosis Test ◽  
Resource Limited ◽  
Specificity And Sensitivity ◽  
Hcv Screening ◽  
Direct Acting

Abstract Background Treatment of HCV with direct-acting antivirals has enabled the discussion of HCV eradication worldwide. Envisioning this aim requires implementation of mass screening in resource-limited areas, usually constrained by testing costs. Methods We validated a low-cost, rapid diagnosis test (RDT) for HCV in three different continents in 141 individuals. Results The HCV RDT showed 100% specificity and sensitivity across different samples regardless of genotype or viral load (in samples with such information, 90%). Conclusions The HCV test validated in this study can allow for HCV screening in areas of need when properly used.

Sign in / Sign up

Export Citation Format

Share Document