scholarly journals Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1177
Author(s):  
Joan Bordoy ◽  
Dominik Jan Schott ◽  
Jizhou Xie ◽  
Amir Bannoura ◽  
Philip Klein ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Indoor Localization ◽  
Measurement Unit ◽  
Learning To Learn ◽  
Additional Source ◽  
Random Forest Classification ◽  
Acoustic Transmitter ◽  
Forest Classification ◽  
Source Of Information

An acoustic transmitter can be located by having multiple static microphones. These microphones are synchronized and measure the time differences of arrival (TDoA). Usually, the positions of the microphones are assumed to be known in advance. However, in practice, this means they have to be manually measured, which is a cumbersome job and is prone to errors. In this paper, we present two novel approaches which do not require manual measurement of the receiver positions. The first method uses an inertial measurement unit (IMU), in addition to the acoustic transmitter, to estimate the positions of the receivers. By using an IMU as an additional source of information, the non-convex optimizers are less likely to fall into local minima. Consequently, the success rate is increased and measurements with large errors have less influence on the final estimation. The second method we present in this paper consists of using machine learning to learn the TDoA signatures of certain regions of the localization area. By doing this, the target can be located without knowing where the microphones are and whether the received signals are in line-of-sight or not. We use an artificial neural network and random forest classification for this purpose.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

2019 ◽  
Vol 6 ◽  
pp. 205566831986854 ◽  
Author(s):  
Rob Argent ◽  
Sean Drummond ◽  
Alexandria Remus ◽  
Martin O’Reilly ◽  
Brian Caulfield
Keyword(s):  
Machine Learning ◽  
Knee Joint ◽  
Inertial Measurement Unit ◽  
Joint Angle ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Reference Standard ◽  
Inertial Measurement ◽  
Knee Joint Angle ◽  
Single Sensor

Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

scholarly journals Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3946 ◽  
Author(s):  
Faisal Jamil ◽  
Do Hyeun Kim
Keyword(s):  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Indoor Navigation ◽  
Prediction Algorithm ◽  
Measurement Unit ◽  
Sensor Reading ◽  
Inertial Measurement ◽  
Learning Module ◽  
Alpha Beta

The navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user’s context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization, whereas interest in location-based services is also rising. While numerous inertial measurement unit-based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. In this article, we present a novel solution for improving the accuracy of indoor navigation using a learning to perdition model. The design system tracks the location of the object in an indoor environment where the global positioning system and other satellites will not work properly. Moreover, in order to improve the accuracy of indoor navigation, we proposed a learning to prediction model-based artificial neural network to improve the prediction accuracy of the prediction algorithm. For experimental analysis, we use the next generation inertial measurement unit (IMU) in order to acquired sensing data. The next generation IMU is a compact IMU and data acquisition platform that combines onboard triple-axis sensors like accelerometers, gyroscopes, and magnetometers. Furthermore, we consider a scenario where the prediction algorithm is used to predict the actual sensor reading from the noisy sensor reading. Additionally, we have developed an artificial neural network-based learning module to tune the parameter of alpha and beta in the alpha–beta filter algorithm to minimize the amount of error in the current sensor readings. In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha–beta filter with a learning module performed better than the traditional alpha–beta filter algorithm in terms of RMSE.

scholarly journals Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas

2020 ◽  
Vol 12 (3) ◽  
pp. 369 ◽  
Author(s):  
Alessandro Lapini ◽  
Simone Pettinato ◽  
Emanuele Santi ◽  
Simonetta Paloscia ◽  
Giacomo Fontanelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Central Italy ◽  
Alos Palsar ◽  
Sar Images ◽  
Random Forest Classification ◽  
Envisat Asar ◽  
Learning Framework ◽  
Forest Classification ◽  
Average Accuracy ◽  
Reference Map

In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo-SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in-situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L-, C- and X-bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non-forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient ( σ ¯ °) was computed for each sensor-polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L- and X-bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors.

scholarly journals Robust and Efficient Indoor Localization Using Sparse Semantic Information from a Spherical Camera

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4128 ◽  
Author(s):  
Irem Uygur ◽  
Renato Miyagusuku ◽  
Sarthak Pathak ◽  
Alessandro Moro ◽  
Atsushi Yamashita ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Semantic Information ◽  
Indoor Localization ◽  
Low Cost ◽  
Large Data ◽  
Point Clouds ◽  
Measurement Unit ◽  
Sensor Model ◽  
Localization Approach ◽  
Spherical Camera

Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. “Sparse semantic” refers to the detection of sparsely distributed objects such as doors and windows. We use sparse semantic information to self-localize on a human-readable 2D annotated map in the sensor model. Thus, compared to previous works using point clouds or other dense and large data structures, our work uses a small amount of sparse semantic information, which efficiently reduces uncertainty in real-time localization. Unlike complex 3D constructions, the annotated map required by our method can be easily prepared by marking the approximate centers of the annotated objects on a 2D map. Our approach is robust to the partial obstruction of views and geometrical errors on the map. The localization is performed using low-cost lightweight sensors, an inertial measurement unit and a spherical camera. We conducted experiments to show the feasibility and robustness of our approach.

scholarly journals Stereo Visual Odometry for Indoor Localization of Ship Model

2020 ◽  
Vol 58 (1) ◽  
pp. 57-75
Author(s):  
Mario Kučić ◽  
Marko Valčić
Keyword(s):  
Autonomous Navigation ◽  
Inertial Measurement Unit ◽  
Indoor Localization ◽  
Low Cost ◽  
Visual Odometry ◽  
Measurement Unit ◽  
Ship Model ◽  
Open Sea ◽  
Autonomous Cars ◽  
Stereo Cameras

Typically, ships are designed for open sea navigation and thus research of autonomous ships is mostly done for that particular area. This paper explores the possibility of using low-cost sensors for localization inside the small navigation area. The localization system is based on the technology used for developing autonomous cars. The main part of the system is visual odometry using stereo cameras fused with Inertial Measurement Unit (IMU) data coupled with Kalman and particle filters to get decimetre level accuracy inside a basin for different surface conditions. The visual odometry uses cropped frames for stereo cameras and Good features to track algorithm for extracting features to get depths for each feature that is used for estimation of ship model movement. Experimental results showed that the proposed system could localize itself within a decimetre accuracy implying that there is a real possibility for ships in using visual odometry for autonomous navigation on narrow waterways, which can have a significant impact on future transportation.

scholarly journals Analysis Of The Effect Of Knowledge On Entrepreneurship Readiness Using Random Forest Classification Machine Learning

2021 ◽  
Vol 23 ◽  
pp. 134-149
Author(s):  
Ariyono Setiawan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Random Forest ◽  
Rapid Development ◽  
Air Transportation ◽  
Sample Population ◽  
Random Forest Classification ◽  
Main Requirement ◽  
Forest Classification ◽  
Entrepreneurial Readiness

Entrepreneurship is a phenomenon that has an important influence on the progress and welfare of the world, so that entrepreneurship is used as the base of economic development. Psychologically, entrepreneurs are people who have a strong internal drive as an effort to achieve certain goals so that they have a tendency to experiment in showing a character that is free from the control of others. Entrepreneurship can be seen from various points of view. The angle and context in question are views from several fields, namely according to economists, management, business people, psychologists and investors. The main requirement that an entrepreneur must have is entrepreneurial knowledge. entrepreneurial readiness is determined by the knowledge possessed and experience in conducting a business (Kurniawati, 2019). In the midst of the rapid development of artificial intelligence (AI) technology today. Not many people know that artificial intelligence consists of several branches, one of which is machine learning. This machine learning (ML) technology is one of the branches of AI that is very interesting. The sample population in this study was obtained from the air transportation school consisting of 7 populations. Data analysis is done by using . The research location is an air transportation school with Machine Learning Random Forest Classification with a population of cadets, lecturers and the general public

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