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 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 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 Automotive Frequency Modulated Continuous Wave Radar Interference Reduction Using Per-Vehicle Chirp Sequences

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2831 ◽  
Author(s):  
Youn-Sik Son ◽  
Hyuk-Kee Sung ◽  
Seo Heo
Keyword(s):  
Continuous Wave ◽  
Signal Interference ◽  
Radar Signal ◽  
Driver Assistance ◽  
Interference Reduction ◽  
Fmcw Radar ◽  
Radar Sensors ◽  
Vehicle To Vehicle ◽  
Radar Systems ◽  
Wave Radar

Recently, many automobiles adopt radar sensors to support advanced driver assistance system (ADAS) functions. As the number of vehicles with radar systems increases the probability of radar signal interference and the accompanying ghost target problems become serious. In this paper, we propose a novel algorithm where we deploy per-vehicle chirp sequence in a frequency modulated continuous wave (FMCW) radar to mitigate the vehicle-to-vehicle radar interference. We devise a chirp sequence set so that the slope of each vehicle’s chirp sequence does not overlap within the set. By assigning one of the chirp sequences to each vehicle, we mitigate the interference from the radar signals transmitted by the neighboring vehicles. We confirm the performance of the proposed method stochastically by computer simulation. The simulation results show that the detection and false alarm performance is improved significantly by the proposed method.

scholarly journals Evaluation of a robust correlation-based true-speed-over-ground measurement system employing a FMCW radar

2019 ◽  
Vol 11 (7) ◽  
pp. 686-693 ◽  
Author(s):  
Torsten Reissland ◽  
Bjoern Lenhart ◽  
Johann Lichtblau ◽  
Michael Sporer ◽  
Robert Weigel ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Field Measurements ◽  
Proof Of Concept ◽  
Fmcw Radar ◽  
Radar Sensors ◽  
Novel Approach ◽  
Ground Measurement ◽  
Wave Radar ◽  
24 Ghz

AbstractThis paper presents a novel approach for the determination of True-Speed-Over-Ground for trains. Speed determination is accomplished by correlating the received signals of two side-looking radar sensors. The theoretically achievable precision is derived. Test measurements are done in two different scenarios to give a proof of concept. Thereafter a series of field measurements is performed to rate the practical suitability of the approach. The results of the measurements are thoroughly evaluated. The test and field measurements are carried out using a 24 GHz frequency modulated continuous wave radar.

scholarly journals Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1965
Author(s):  
Jyoti Bhatia ◽  
Aveen Dayal ◽  
Ajit Jha ◽  
Santosh Kumar Vishvakarma ◽  
Soumya Joshi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traffic Management ◽  
Continuous Wave ◽  
Multiple Input Multiple Output ◽  
Cost Effective ◽  
Gradient Boosting ◽  
Support Vector ◽  
Angle Of Arrival ◽  
Fmcw Radar ◽  
Wide Range

Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW Radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classification technique extends the potential of mmWave FMCW Radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW Radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.

scholarly journals A Wideband Dielectric Waveguide-Based 160-GHz Radar Target Generator

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2801
Author(s):  
Martin Geiger ◽  
Christian Wegner ◽  
Winfried Mayer ◽  
Christian Waldschmidt
Keyword(s):  
Dielectric Waveguide ◽  
Continuous Wave ◽  
Low Cost ◽  
Radar Target ◽  
Test Procedures ◽  
Test Environment ◽  
Fmcw Radar ◽  
Radar Sensors ◽  
Full Wave ◽  
Anechoic Chambers

The increasing number of radar sensors in commercial and industrial products leads to a growing demand for system functionality tests. Conventional test procedures require expensive anechoic chambers to provide a defined test environment for radar sensors. In this paper, a compact and low cost dielectric waveguide radar target generator for level probing radars is presented. The radar target generator principle is based on a long dielectric waveguide as a one-target scenery. By manipulating the field distribution of the waveguide, a specific reflection of a radar target is generated. Two realistic scenarios for a tank level probing radar are investigated and suitable targets are designed with full wave simulations. Target distances from 13 cm to at least 9 m are realized with an extruded dielectric waveguide with dielectric losses of 2 dB/m at 160 GHz. Low loss (0.5 dB) and low reflection holders are used to fix the waveguide. Due to the dispersion of the dielectric waveguide, a detailed analysis of its impact on frequency-modulated continuous wave (FMCW) radars is given and compared to free-space propagation. The functionality of the radar target generator is verified with a 160-GHz FMCW radar prototype.

Applications of radar measurement technology using 24 GHz, 61 GHz, 80 GHz and 122 GHz FMCW radar sensors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Akanksha Bhutani ◽  
Sören Marahrens ◽  
Marius Kretschmann ◽  
Serdal Ayhan ◽  
Steffen Scherr ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Continuous Wave ◽  
Estimation Method ◽  
Signal Propagation ◽  
Distance Measurement ◽  
Radar Signal ◽  
Measurement Technology ◽  
Fmcw Radar ◽  
Radar Sensors ◽  
24 Ghz

Abstract This paper presents a review of radar applications in high-accuracy distance measurement of a target. The radars included in this review are frequency modulated continuous wave (FMCW) radar sensors operating in four different millimeter-wave frequency bands, namely 24 GHz, 61 GHz, 80 GHz and 122 GHz. The radar sensors are used to measure the distance of standard and complex targets in a short range of a few meters, thus indicating that the choice of target and the medium used for radar signal propagation also play a key role in determining the distance measurement accuracy of an FMCW radar. The standard target is a trihedral corner reflector in a laboratory-based free space measurement setup and the complex targets include a piston in an oil-filled hydraulic cylinder and a planar positioning stage used in micromachining. In each of these measurement scenarios, a distance measurement accuracy in micrometer range is achieved due to the use of a sophisticated signal processing algorithm that is based on a combined frequency and phase estimation method. The paper is concluded with a technical comparison of the accuracy achieved by the FMCW radars reviewed in this article with other related works.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

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