scholarly journals Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home Environments

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7560
Author(s):  
Idongesit Ekerete ◽  
Matias Garcia-Constantino ◽  
Yohanca Diaz-Skeete ◽  
Chris Nugent ◽  
James McLaughlin
Keyword(s):  
Home Environment ◽  
Support Vector ◽  
Battery Life ◽  
Home Environments ◽  
Video Cameras ◽  
Average Classification Accuracy ◽  
Accuracy And Precision ◽  
Ankle Rehabilitation ◽  
Rehabilitation Exercises ◽  
Unobtrusive Sensing

The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists ascertain if exercises have been performed as prescribed. Whilst wearable devices have been shown to provide advantages such as high accuracy and precision during monitoring activities, disadvantages such as limited battery life and users’ inability to remember to charge and wear the devices are often the challenges for their usage. In addition, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. Therefore, this paper proposes the use and fusion of privacy-friendly and Unobtrusive Sensing Solutions (USSs) for data collection and processing during SPAREs in home environments. The present work aims to monitor SPAREs such as dorsiflexion, plantarflexion, inversion, and eversion using radar and thermal sensors. The main contributions of this paper include (i) privacy-friendly monitoring of SPAREs in a home environment, (ii) fusion of SPAREs data from homogeneous and heterogeneous USSs, and (iii) analysis and comparison of results from single, homogeneous, and heterogeneous USSs. Experimental results indicated the advantages of using heterogeneous USSs and data fusion. Cluster-based analysis of data gleaned from the sensors indicated an average classification accuracy of 96.9% with Neural Network, AdaBoost, and Support Vector Machine, amongst others.

scholarly journals Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home Environments

2021 ◽  
Author(s):  
Idongesit Ekerete ◽  
Matias Garcia-Constantino ◽  
Yohanca Diaz ◽  
Chris Nugent ◽  
James Mclaughlin
Keyword(s):  
Continuous Wave ◽  
Movement Patterns ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Home Environments ◽  
Fmcw Radar ◽  
Radar Sensors ◽  
Ankle Rehabilitation ◽  
Fundamental Movement Patterns ◽  
Rehabilitation Exercises

The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists to ascertain if exercises have been performed as prescribed. Whilst wearable devices have been shown to provide advantages such as high accuracy and precision during monitoring activities, disadvantages such as limited battery life, users' inability to remember to charge and wear the devices are often the challenges for their usage. Also, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. This paper, therefore, proposes the use and fusion of unobtrusive and privacy-friendly sensing solutions for data collection and processing during SPAREs in home environments. Two Infrared Thermopile Array (ITA-32) thermal sensors and two Frequency Modulated Continuous Wave (FMCW) Radar sensors were used to simultaneously monitor 15 healthy participants during SPAREs which involved twisting their ankle in 4-fundamental movement patterns namely (i) extension, (ii) flexion, (iii) eversion and (iv) inversion. Experimental results indicated the ability to identify thermal blobs of participants performing the 4 fundamental movement patterns of the human ankle. Cluster-based analysis of data gleaned from the ITA-32 sensors and the FMCW Radar sensors indicated average classification accuracy of 96.9% with K-Nearest Neighbours, Neural Network, AdaBoost, Decision Tree, Stochastic Gradient Descent and Support Vector Machine, amongst others.

scholarly journals Localisation of Room Occupants in Home Environments using Unobtrusive Sensing Solutions

2021 ◽  
Author(s):  
Idongesit Ekerete ◽  
Chris Nugent ◽  
James McLaughlin
Keyword(s):  
Continuous Wave ◽  
Wearable Sensors ◽  
Ground Truth ◽  
Battery Life ◽  
Strong Positive Correlation ◽  
Time Data ◽  
Radar Sensor ◽  
Home Environments ◽  
Significant Difference ◽  
Unobtrusive Sensing

This paper proposes the localisation of room occupants in home environments using Unobtrusive Sensing Solutions (USSs). The ability to localise room occupants in home environments can help in the objective monitoring of sedentary behaviour. While wearable sensors can provide tangible information on health and wellness, they have battery life issues and the inability to perform prolonged monitoring. This work uses heterogeneous USSs in the form of an Infrared Thermopile Array (ITA-64) thermal sensor and a Multi-Chirp Frequency Modulated Continuous Wave Mono-pulse (MC-FMCW-M) Radar sensor to monitor room occupants. Digital filters and background subtraction algorithms were used to process the thermal images gleaned from the ITA-64 thermal sensors. The MC-FMCW-M Radar sensor used multi-chirp and Doppler shift principles to estimate the exact location of the targeted room occupants. The estimated distances from the Radar Sensor were compared with ground truth values. Experimental results demonstrated the ability to identify thermal blobs of occupants present in the room at any particular time. Data analyses indicated no significant difference (p = 0.975) and a very strong positive correlation (r = 0.998) between the ground truth distance values and those obtained from the Radar Sensor.

scholarly journals Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods

2021 ◽  
Vol 11 (19) ◽  
pp. 9096
Author(s):  
Idongesit Ekerete ◽  
Matias Garcia-Constantino ◽  
Alexandros Konios ◽  
Mustafa A. Mustafa ◽  
Yohanca Diaz-Skeete ◽  
...  
Keyword(s):  
Data Mining ◽  
Activity Recognition ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Home Environments ◽  
Use Of Data ◽  
Home Based ◽  
Actual Recognition ◽  
Using Data ◽  
Unobtrusive Sensing

This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test.

scholarly journals Smarter Robotic Sprayer System for Precision Agriculture

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2061
Author(s):  
André Rodrigues Baltazar ◽  
Filipe Neves dos Santos ◽  
António Paulo Moreira ◽  
António Valente ◽  
José Boaventura Cunha
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Reference Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Accuracy Score ◽  
Accuracy And Precision ◽  
Perception System ◽  
Water Rate ◽  
Leaf Density

The automation of agricultural processes is expected to positively impact the environment by reducing waste and increasing food security, maximising resource use. Precision spraying is a method used to reduce the losses during pesticides application, reducing chemical residues in the soil. In this work, we developed a smart and novel electric sprayer that can be assembled on a robot. The sprayer has a crop perception system that calculates the leaf density based on a support vector machine (SVM) classifier using image histograms (local binary pattern (LBP), vegetation index, average, and hue). This density can then be used as a reference value to feed a controller that determines the air flow, the water rate, and the water density of the sprayer. This perception system was developed and tested with a created dataset available to the scientific community and represents a significant contribution. The results of the leaf density classifier show an accuracy score that varies between 80% and 85%. The conducted tests prove that the solution has the potential to increase the spraying accuracy and precision.

scholarly journals Predicting fatalities among shark attacks: comparison of classifiers

2019 ◽  
Vol 8 (4) ◽  
pp. 360 ◽  
Author(s):  
Lim Mei Shi ◽  
Aida Mustapha ◽  
Yana Mazwin Mohmad Hassim
Keyword(s):  
Support Vector Machines ◽  
Performance Measurement ◽  
Support Vector ◽  
Error Measure ◽  
Accuracy And Precision ◽  
Vector Machines ◽  
Shark Attacks ◽  
Probabilistic Error

<span lang="EN-US">This paper presents the comparisons of different classifiers on predicting Shark attack fatalities. In this study, we are comparing two classifiers which are Support vector machines(SVMs) and Bayes Point Machines(BPMs) on Shark attacks dataset. The comparison of the classifiers were based on the accuracy, recall, precision and F1-score as the performance measurement. The results obtained from this study showed that BPMs predicted the fatality of shack attack victim experiment with higher accuracy and precision than the SVMs because BPMs have “average” identifier which can minimize the probabilistic error measure. From this experiment, it is concluded that BPMs are more suitable in predicting fatality of shark attack victim as BPMs is an improvement of SVMs.</span>

The Animal Classification: An Evaluation of Different Transfer Learning Pipeline

Mekatronika ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 27-31
Author(s):  
Ken-ji Ee ◽  
Ahmad Fakhri Bin Ab. Nasir ◽  
Anwar P. P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman ◽  
Nur Hafieza Ismail
Keyword(s):  
Transfer Learning ◽  
Classification System ◽  
Cross Validation ◽  
Support Vector ◽  
Svm Classifier ◽  
Average Classification Accuracy ◽  
Validation Technique ◽  
Search Approach ◽  
Fold Cross Validation

The animal classification system is a technology to classify the animal class (type) automatically and useful in many applications. There are many types of learning models applied to this technology recently. Nonetheless, it is worth noting that the extraction of the features and the classification of the animal features is non-trivial, particularly in the deep learning approach for a successful animal classification system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards animal classification applications are somewhat limited. The present study aims to determine a suitable TL-conventional classifier pipeline for animal classification. The VGG16 and VGG19 were used in extracting features and then coupled with either k-Nearest Neighbour (k-NN) or Support Vector Machine (SVM) classifier. Prior to that, a total of 4000 images were gathered consisting of a total of five classes which are cows, goats, buffalos, dogs, and cats. The data was split into the ratio of 80:20 for train and test. The classifiers hyper parameters are tuned by the Grids Search approach that utilises the five-fold cross-validation technique. It was demonstrated from the study that the best TL pipeline identified is the VGG16 along with an optimised SVM, as it was able to yield an average classification accuracy of 0.975. The findings of the present investigation could facilitate animal classification application, i.e. for monitoring animals in wildlife.

scholarly journals Police Officers’ Perceptions of Body-Worn Video Cameras in Beijing

2020 ◽  
pp. 105756772091991
Author(s):  
Feng Jiang ◽  
Chuanyu Xie ◽  
Tom Ellis
Keyword(s):  
Law Enforcement ◽  
Police Officers ◽  
Video Recording ◽  
The United States ◽  
Research Literature ◽  
Public Security ◽  
Battery Life ◽  
Online Questionnaire ◽  
Video Cameras ◽  
High Level

The Chinese police started using body-worn video cameras (BWVCs) from 2010 in some cities and provinces. On July 1, 2016, shortly after the death of Lei Yang during arrest by police, the Ministry of Public Security ( Gong’anbu) introduced BWVCs as mandatory for all the Chinese frontline police officers through issuing Regulations on Audio and Video Recording of Onsite Law Enforcement for Public Security Units (RAVR). However, despite the nationwide use of BWVCs, the research literature on BWVCs in China remains sparse. Studies from the United States and the United Kingdom provide evidence of the importance of officers’ buy-in to the new technology. It is, therefore, essential to know Chinese officers’ views and evaluations of using BWVCs. Using an anonymized online questionnaire, adapted from published international prior studies, this article reports and evaluates the views of 255 Beijing officers of the Beijing Police Department. Our analysis suggests that, overall, there was a high level of support and a high level of self-reported use for BWVCs among respondents not only because they are required to use them but also because they wanted to. Officers perceived more benefits than disadvantages of using BWVCs and most thought BWVCs would help them in their daily work without reducing their enthusiasm for law enforcement. Some differences were found between officers from different working units and between male and female officers. There were also weak negative correlations between length of service as a police officer and supportive attitudes toward BWVCs. Most criticisms were about technical issues such as higher expectations on the battery life and BWVC reliability.

Depressed Patients Intelligent Recognition in Smart Home Environment

2021 ◽  
Vol 11 (2) ◽  
pp. 353-359
Author(s):  
Jie Zhang ◽  
Tingting Zhao ◽  
Yuan Liu
Keyword(s):  
Home Environment ◽  
Smart Home ◽  
Recognition Rate ◽  
Mental Illnesses ◽  
Support Vector ◽  
Data Sets ◽  
Future Technology ◽  
Duration Of Illness ◽  
Smart Home Environment ◽  
Intelligent Recognition

Depression is one of the most harmful diseases in society today, and the etiology and pathological mechanism of depression is one of the most complicated mental illnesses. As the population of people with depression grows, the patient's long duration of illness and the harmfulness of the results make the disease the biggest challenge in the diagnosis of mental illness. How to improve the recognition rate of depression and make diagnosis and treatment as early as possible is the most effective way. According to the clinical medical manifestations of patients with depression, it is found that there is a very obvious difference between the patients with depression and the normal group in terms of speech characteristics, such as lower tones and slower speech speed. Therefore, this paper proposes a method for intelligent recognition of depression based on speech signals in combination with the contemporary smart home environment. A novel ensemble support vector machine (ESVM) algorithm is proposed in this article, which is applied to several classic depression speech data sets. The organic combination of depression recognition and smart home environment can adapt to the development of future technology.

Active Rehabilitation Exercises With a Parallel Structure Ankle Rehabilitation Prototype

2017 ◽  
Vol 15 (5) ◽  
pp. 786-794 ◽  
Author(s):  
W.M.A. Rosado ◽  
A.B. Ortega ◽  
L.G.V. Valdes ◽  
J.R. Ascencio ◽  
C.D.G. Beltrán
Keyword(s):  
Parallel Structure ◽  
Ankle Rehabilitation ◽  
Active Rehabilitation ◽  
Rehabilitation Exercises

Blind and Sighted Children's Spatial Knowledge of Their Home Environments

1996 ◽  
Vol 19 (4) ◽  
pp. 797-816 ◽  
Author(s):  
Ann E. Bigelow
Keyword(s):  
Home Environment ◽  
Visually Impaired ◽  
Spatial Knowledge ◽  
School Age Children ◽  
School Age ◽  
Home Environments ◽  
Visual Ability ◽  
Spatial Understanding ◽  
Straight Line ◽  
Blind Children

The development of spatial knowledge of the home environment was longitudinally studied in three groups of school-age children who varied in their visual ability: totally blind, visually impaired, and normally sighted. The children were asked to judge which of three locations in their homes was the closest to a designated position: (1) judging by the routes necessary to get to the locations; and (2) judging by straight-line distances to the locations. Locations were either on the same floor as the designed position, on a different floor, or in the yard. Totally blind children were delayed in mastery of the tasks compared to the other children, particularly in judging straight-line distances between familiar locations. Their mistakes suggest that their spatial understanding of their home environments is based on their knowledge of routes between places rather than on their knowledge of the overall layout of the familiar space.

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