scholarly journals Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation

2021 ◽  
Vol 11 (20) ◽  
pp. 9765
Author(s):  
Hui Xue ◽  
Bjørn-Morten Batalden ◽  
Puneet Sharma ◽  
Jarle André Johansen ◽  
Dilip K. Prasad
Keyword(s):  
Machine Learning ◽  
Situation Awareness ◽  
Learning Algorithm ◽  
Training System ◽  
Wearable Sensor ◽  
Task Load ◽  
Novel Approach ◽  
Driving Skill ◽  
Maritime Navigation ◽  
Nasa Task Load Index

This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies.

Subjective Measurement Assessment in a Full Mission Scout-Attack Helicopter Simulation

1998 ◽  
Vol 42 (1) ◽  
pp. 26-30 ◽  
Author(s):  
Susan T. Heers ◽  
Patricia A. Casper
Keyword(s):  
Situation Awareness ◽  
Inverse Relationship ◽  
Advanced Technology ◽  
Stepwise Multiple Regression ◽  
Task Load ◽  
Advanced Technologies ◽  
Additional Equipment ◽  
The Us ◽  
Perceived Workload ◽  
Nasa Task Load Index

A full mission helicopter simulation was conducted in support of the US Army's Rotorcraft Pilot's Associate Advanced Technology Demonstration. Four crews flew four doctrinally correct scenarios under two mission equipment package conditions. The Advanced Mission Equipment Package (AMEP) contained additional equipment and longer sensor ranges than the Baseline Mission Equipment Package (BMEP). Following each run, pilots filled out the NASA Task Load Index (TLX) workload scales and a perceived situation awareness (SA) scale. TLX ratings were lower for the AMEP, while SA ratings were higher for the AMEP. A similar inverse relationship was found in the scenario effects. A stepwise multiple regression found a significant relationship between SA ratings and three of the component TLX subscale ratings. Both perceived workload and situation awareness ratings indicate a benefit from the advanced technologies available on the AMEP. These measures were also sensitive to the varying demands of the scenarios and pilot responsibilities.

AUTONOMOUS BEHAVIORS OF GRAPHICAL AVATARS BASED ON MACHINE LEARNING

2012 ◽  
Vol 26 (02) ◽  
pp. 1251002
Author(s):  
YUESHENG HE ◽  
YUAN YAN TANG
Keyword(s):  
Machine Learning ◽  
Hierarchical Structure ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Research Area ◽  
Machine Learning Techniques ◽  
3D Animation ◽  
Graphical Environment ◽  
Novel Approach ◽  
The Difference

Graphical avatars have gained popularity in many application domains such as three-dimensional (3D) animation movies and animated simulations for product design. However, the methods to edit avatars' behaviors in the 3D graphical environment remained to be a challenging research topic. Since the hand-crafted methods are time-consuming and inefficient, the automatic actions of the avatars are required. To achieve the autonomous behaviors of the avatars, artificial intelligence should be used in this research area. In this paper, we present a novel approach to construct a system of automatic avatars in the 3D graphical environments based on the machine learning techniques. Specific framework is created for controlling the behaviors of avatars, such as classifying the difference among the environments and using hierarchical structure to describe these actions. Because of the requirement of simulating the interactions between avatars and environments after the classification of the environment, Reinforcement Learning is used to compute the policy to control the avatar intelligently in the 3D environment for the solution of the problem of different situations. Thus, our approach has solved problems such as where the levels of the missions will be defined and how the learning algorithm will be used to control the avatars. In this paper, our method to achieve these goals will be presented. The main contributions of this paper are presenting a hierarchical structure to control avatars automatically, developing a method for avatars to recognize environment and presenting an approach for making the policy of avatars' actions intelligently.

scholarly journals AI-powered Posture Training: Application of Machine Learning in Sitting Posture Recognition Using the LifeChair Smart Cushion

2019 ◽  
Author(s):  
Katia Bourahmoune ◽  
Toshiyuki Amagasa
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Well Being ◽  
Recognition System ◽  
Training System ◽  
Sensor Data ◽  
Physical And Mental Health ◽  
Sitting Posture ◽  
Posture Monitoring ◽  
Posture Recognition

Humans spend on average more than half of their day sitting down. The ill-effects of poor sitting posture and prolonged sitting on physical and mental health have been extensively studied, and solutions for curbing this sedentary epidemic have received special attention in recent years. With the recent advances in sensing technologies and Artificial Intelligence (AI), sitting posture monitoring and correction is one of the key problems to address for enhancing human well-being using AI. We present the application of a sitting posture training smart cushion called LifeChair that combines a novel pressure sensing technology, a smartphone app interface and machine learning (ML) for real-time sitting posture recognition and seated stretching guidance. We present our experimental design for sitting posture and stretch pose data collection using our posture training system. We achieved an accuracy of 98.93% in detecting more than 13 different sitting postures using a fast and robust supervised learning algorithm. We also establish the importance of taking into account the divergence in user body mass index in posture monitoring. Additionally, we present the first ML-based human stretch pose recognition system for pressure sensor data and show its performance in classifying six common chair-bound stretches.

scholarly journals Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas M. Clarke ◽  
Sasha K. Whitmarsh ◽  
Jenna L. Hounslow ◽  
Adrian C. Gleiss ◽  
Nicholas L. Payne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Body Position ◽  
High Amplitude ◽  
Pelagic Fish ◽  
Supervised Machine Learning ◽  
Accelerometer Data ◽  
Direct Observations ◽  
Novel Approach ◽  
Free Ranging

Abstract Background Tri-axial accelerometers have been used to remotely describe and identify in situ behaviours of a range of animals without requiring direct observations. Datasets collected from these accelerometers (i.e. acceleration, body position) are often large, requiring development of semi-automated analyses to classify behaviours. Marine fishes exhibit many “burst” behaviours with high amplitude accelerations that are difficult to interpret and differentiate. This has constrained the development of accurate automated techniques to identify different “burst” behaviours occurring naturally, where direct observations are not possible. Methods We trained a random forest machine learning algorithm based on 624 h of accelerometer data from six captive yellowtail kingfish during spawning periods. We identified five distinct behaviours (swim, feed, chafe, escape, and courtship), which were used to train the model based on 58 predictive variables. Results Overall accuracy of the model was 94%. Classification of each behavioural class was variable; F1 scores ranged from 0.48 (chafe) – 0.99 (swim). The model was subsequently applied to accelerometer data from eight free-ranging kingfish, and all behaviour classes described from captive fish were predicted by the model to occur, including 19 events of courtship behaviours ranging from 3 s to 108 min in duration. Conclusion Our findings provide a novel approach of applying a supervised machine learning model on free-ranging animals, which has previously been predominantly constrained to direct observations of behaviours and not predicted from an unseen dataset. Additionally, our findings identify typically ambiguous spawning and courtship behaviours of a large pelagic fish as they naturally occur.

scholarly journals Machine Learning Algorithm for DNA-Based Molecular Ligands

2019 ◽  
Author(s):  
Christina Baek ◽  
Sang-Woo Lee ◽  
Beom-Jin Lee ◽  
Dong-Hyun Kwak
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Learning Algorithm ◽  
Dna Nanotechnology ◽  
Search Space ◽  
Molecular Computing ◽  
Training Data ◽  
Molecular Systems ◽  
Novel Approach

Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that function in dynamic environments. Here, we introduce an in vitro molecular algorithm that ‘learns’ molecular models from training data, opening the possibility of ‘machine learning’ in wet molecular systems. Our algorithm enables enzymatic weight update by targeting internal loop structures in DNA and ensemble learning, based on the hypernetwork model. This novel approach allows massively parallel processing of DNA with enzymes for specific structural selection for learning in an iterative manner. We also introduce an intuitive method of DNA data construction to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets. By combining molecular computing and machine learning the proposed algorithm makes a step closer to developing molecular computing technologies for future access to more intelligent molecular systems.

Development and pilot test of a machine learning-based knee exercise system with video demonstration, real-time feedback, and exercise performance score

2021 ◽  
Vol 65 (1) ◽  
pp. 1519-1523
Author(s):  
Tianrong Chen ◽  
Calvin Kalun Or
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Exercise Performance ◽  
Lower Limb ◽  
Physical Therapists ◽  
Learning Algorithm ◽  
Training System ◽  
Perceived Usefulness ◽  
Chronic Knee Pain ◽  
Video Demonstration

Exercise therapy is a common and effective approach for managing chronic knee pain. However, individuals often receive minimal supervision from physical therapists when exercises are performed at home. In this study, we developed a video-based training system to allow individuals to perform lower limb exercises, based on a machine learning algorithm for pose detection and estimation. The system included three key features: (1) an exercise video demonstration, (2) real-time tracking and feedback of exercise movements, and (3) an overall score of exercise performance. We also pilot tested the system by having participants (n = 8) to use the system to perform lower limb exercises for 3 consecutive days. The results indicated that, compared with the baseline, the perceived usefulness of the system ( t = 3.25, p = 0.01) and perceived lower limb muscle strength ( t = 2.94, p = 0.02) significantly improved after 3 days. These findings provide knowledge about the initial views on this system by the participants. However, further enhancements of the features and full-scale experiments to examine the usability and acceptance of the system and its impact on knee health are needed.

scholarly journals A Nonlinear Distortion Removal Based on Deep Neural Network for Underwater Acoustic OFDM Communication with the Mitigation of Peak to Average Power Ratio

2020 ◽  
Vol 10 (14) ◽  
pp. 4986 ◽  
Author(s):  
Xuefei Ma ◽  
Waleed Raza ◽  
Zhiqiang Wu ◽  
Muhammad Bilal ◽  
Ziqi Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Power Efficiency ◽  
Learning Algorithm ◽  
Average Power ◽  
Decision Feedback ◽  
Power Ratio ◽  
Underwater Acoustic ◽  
Deep Learning Algorithm ◽  
Novel Approach

Machine learning and deep learning algorithms have proved to be a powerful tool for developing data-driven signal processing algorithms for challenging engineering problems. This paper studies the modern machine learning algorithm for modeling nonlinear devices like power amplifiers (PAs) for underwater acoustic (UWA) orthogonal frequency divisional multiplexing (OFDM) communication. The OFDM system has a high peak to average power ratio (PAPR) in the time domain because the subcarriers are added coherently via inverse fast Fourier transform (IFFT). This causes a higher bit error rate (BER) and degrades the performance of the PAs; hence, it reduces the power efficiency. For long-range underwater acoustic applications such as the long-term monitoring of the sea, the PA works in full consumption mode. Thus, it becomes a challenging task to minimize power consumption and unnecessary distortion. To mitigate this problem, a receiver-based nonlinearity distortion mitigation method is proposed, assuming that the transmitting side has enough computation power. We propose a novel approach to identify the nonlinear power model using a modern deep learning algorithm named frequentative decision feedback (FFB); PAPR performance is verified by the clipping method. The simulation results prove the better performance of the PA model with a BER with the shortest learning time.

scholarly journals A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows

2017 ◽  
Author(s):  
Pedro M. Milani ◽  
Julia Ling ◽  
Gonzalo Saez-Mischlich ◽  
Julien Bodart ◽  
John K. Eaton
Keyword(s):  
Machine Learning ◽  
Film Cooling ◽  
Learning Algorithm ◽  
Turbulent Heat Flux ◽  
Navier Stokes ◽  
Turbulent Heat ◽  
Turbulent Diffusivity ◽  
Film Cooling Effectiveness ◽  
Cooling Flows ◽  
Novel Approach

In film cooling flows, it is important to know the temperature distribution resulting from the interaction between a hot main flow and a cooler jet. However, current Reynolds-averaged Navier-Stokes (RANS) models yield poor temperature predictions. A novel approach for RANS modeling of the turbulent heat flux is proposed, in which the simple gradient diffusion hypothesis (GDH) is assumed and a machine learning algorithm is used to infer an improved turbulent diffusivity field. This approach is implemented using three distinct data sets: two are used to train the model and the third is used for validation. The results show that the proposed method produces significant improvement compared to the common RANS closure, especially in the prediction of film cooling effectiveness.

scholarly journals Logistic Regression for Machine Learning in Process Tomography

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3400 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Edward Kozłowski ◽  
Grzegorz Kłosowski ◽  
Konrad Niderla
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Transmission Tomography ◽  
Impedance Tomography ◽  
Output Image ◽  
Novel Approach ◽  
Process Tomography ◽  
Net Method

The main goal of the research presented in this paper was to develop a refined machine learning algorithm for industrial tomography applications. The article presents algorithms based on logistic regression in relation to image reconstruction using electrical impedance tomography (EIT) and ultrasound transmission tomography (UST). The test object was a tank filled with water in which reconstructed objects were placed. For both EIT and UST, a novel approach was used in which each pixel of the output image was reconstructed by a separately trained prediction system. Therefore, it was necessary to use many predictive systems whose number corresponds to the number of pixels of the output image. Thanks to this approach the under-completed problem was changed to an over-completed one. To reduce the number of predictors in logistic regression by removing irrelevant and mutually correlated entries, the elastic net method was used. The developed algorithm that reconstructs images pixel-by-pixel is insensitive to the shape, number and position of the reconstructed objects. In order to assess the quality of mappings obtained thanks to the new algorithm, appropriate metrics were used: compatibility ratio (CR) and relative error (RE). The obtained results enabled the assessment of the usefulness of logistic regression in the reconstruction of EIT and UST images.

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