scholarly journals Handwriting Recognition in Free Space Using WIMU-Based Hand Motion Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shashidhar Patil ◽  
Dubeom Kim ◽  
Seongsill Park ◽  
Youngho Chai
Keyword(s):  
Real Time ◽  
Motion Analysis ◽  
Computer Games ◽  
Free Space ◽  
Handwriting Recognition ◽  
Angular Rate ◽  
Measurement Unit ◽  
Hand Motion ◽  
Motion Data ◽  
3D Space

We present a wireless-inertial-measurement-unit- (WIMU-) based hand motion analysis technique for handwriting recognition in three-dimensional (3D) space. The proposed handwriting recognition system is not bounded by any limitations or constraints; users have the freedom and flexibility to write characters in free space. It uses hand motion analysis to segment hand motion data from a WIMU device that incorporates magnetic, angular rate, and gravity sensors (MARG) and a sensor fusion algorithm to automatically distinguish segments that represent handwriting from nonhandwriting data in continuous hand motion data. Dynamic time warping (DTW) recognition algorithm is used to recognize handwriting in real-time. We demonstrate that a user can freely write in air using an intuitive WIMU as an input and hand motion analysis device to recognize the handwriting in 3D space. The experimental results for recognizing handwriting in free space show that the proposed method is effective and efficient for other natural interaction techniques, such as in computer games and real-time hand gesture recognition applications.

scholarly journals Evaluation of real time motion tracking accuracy of customised IMU sensor for application in a mobile badminton virtual reality training system

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Zahari Taha ◽  
Mohd Yashim Wong ◽  
Hwa Jen Yap ◽  
Amirul Abdullah ◽  
Wee Kian Yeo
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Motion Tracking ◽  
Training System ◽  
Microsoft Kinect ◽  
Measurement Unit ◽  
Tracking Accuracy ◽  
Improve Performance ◽  
Motion Data ◽  
Time Motion

Immersion is one of the most important aspects in ensuring the applicability of Virtual Reality systems to training regimes aiming to improve performance. To ensure that this key aspect is met, the registration of motion between the real world and virtual environment must be made as accurate and as low latency as possible. Thus, an in-house developed Inertial Measurement Unit (IMU) system is developed for use in tracking the movement of the player’s racquet. This IMU tracks 6 DOF motion data and transmits it to the mobile training system for processing. Physically, the custom motion is built into the shape of a racquet grip to give a more natural sensation when swinging the racquet. In addition to that, an adaptive filter framework is also established to cope with different racquet movements automatically, enabling real-time 6 DOF tracking by balancing the jitter and latency. Experiments are performed to compare the efficacy of our approach with other conventional tracking methods such as the using Microsoft Kinect. The results obtained demonstrated noticeable accuracy and lower latency when compared with the aforementioned methods.

Comparison of Real-Time Measured and Predicted Wellhead Fatigue Performance in a Deepwater Drilling Operation

2015 ◽  
Author(s):  
James Mackenzie ◽  
Neil Murdoch ◽  
Stuart Killbourn
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Fatigue Damage ◽  
Angular Rate ◽  
Fe Analysis ◽  
Processing Unit ◽  
Real Time Monitoring ◽  
Drilling Operation ◽  
Motion Data ◽  
Vessel Motion

This paper discusses the use of a real-time monitoring system to track wave driven fatigue damage at critical locations in a subsea well during a 290-day drilling operation in the Mediterranean Sea. Real-time monitoring was employed due to up-front finite element (FE) analysis predicting fatigue damage rates of sufficient magnitude that the required safety factor would not be met. LMRP/BOP motions were recorded using two subsea monitoring units (each containing accelerometers and angular rate sensors). These were installed on the LMRP and above the lower flex joint. The accelerometer signals were used to deduce the static inclination of the well and the angular rate signals the dynamic inclination due to vessel motion and wave loading on the riser. Data was transmitted in real-time to a central processing unit on board the vessel. This converted the motion data into fatigue damage accumulation using non-linear transfer functions derived from the FE model. The damage was displayed in a simplified format for use in decision-making. The recorded data revealed the motions of the LMRP/BOP and hence the damage rates in the well to be considerably less than predicted by the FE analysis. The damage rates derived from the monitoring system therefore served as a continuously updating demonstration that an acceptable margin of safety against suffering a fatigue failure was being maintained thus permitting drilling to proceed safely. A subsequent benchmarking study which additionally involved processing of recorded vessel motion data, current data and seastate data was undertaken to identify possible reasons for the differences between the predicted and measured LMRP/BOP motions. This showed that conservative assumptions made on the soil stiffness (due to limited availability of data), differences between the predicted and measured riser natural periods and differences between the predicted and measured vessel motions all contributed to the LMRP/BOP motions and hence the damage rates predicted by the up-front FE analysis being larger than measured in the field. The systematic identification of the factors which contributed to the differences between the predicted and measured response is described in this paper. Areas where further work would be of value to help improve the reliability of up-front, analytically predicted fatigue damage rates are also discussed.

scholarly journals Real-time Smartphone Activity Classification Using Inertial Sensors—Recognition of Scrolling, Typing, and Watching Videos While Sitting or Walking

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 655 ◽  
Author(s):  
Sijie Zhuo ◽  
Lucas Sherlock ◽  
Gillian Dobbie ◽  
Yun Sing Koh ◽  
Giovanni Russello ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Sensors ◽  
Optimal Solution ◽  
Sensor Data ◽  
Measurement Unit ◽  
Time Activity ◽  
Motion Data ◽  
Android App ◽  
Human Participants ◽  
Access Policies

By developing awareness of smartphone activities that the user is performing on their smartphone, such as scrolling feeds, typing and watching videos, we can develop application features that are beneficial to the users, such as personalization. It is currently not possible to access real-time smartphone activities directly, due to standard smartphone privileges and if internal movement sensors can detect them, there may be implications for access policies. Our research seeks to understand whether the sensor data from existing smartphone inertial measurement unit (IMU) sensors (triaxial accelerometers, gyroscopes and magnetometers) can be used to classify typical human smartphone activities. We designed and conducted a study with human participants which uses an Android app to collect motion data during scrolling, typing and watching videos, while walking or seated and the baseline of smartphone non-use, while sitting and walking. We then trained a machine learning (ML) model to perform real-time activity recognition of those eight states. We investigated various algorithms and parameters for the best accuracy. Our optimal solution achieved an accuracy of 78.6% with the Extremely Randomized Trees algorithm, data sampled at 50 Hz and 5-s windows. We conclude by discussing the viability of using IMU sensors to recognize common smartphone activities.

Motion Prediction with Artificial Neural Networks Using Wearable Strain Sensors Based on Flexible Thin Graphite Films

2019 ◽  
Vol 826 ◽  
pp. 111-116
Author(s):  
Takahiro Kanokoda ◽  
Yuki Kushitani ◽  
Moe Shimada ◽  
Jun Ichi Shirakashi
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Wearable Devices ◽  
Human Motion ◽  
Strain Sensors ◽  
Motion Prediction ◽  
Prediction System ◽  
Hand Motion ◽  
Motion Data ◽  
Human Motion Prediction

A human motion prediction system can be used to estimate human gestures in advance to the actual action for reducing delays in interactive system. We have already reported a method of simple and easy fabrication of strain sensors and wearable devices using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human motion, with high durability and fast response. In this study, we have demonstrated hand motion prediction by neural networks (NNs) using hand motion data obtained from data gloves based on PGSs. In our experiments, we measured hand motions of subjects for learning. We created 4-layered NNs to predict human hand motion in real-time. As a result, the proposed system successfully predicted hand motion in real-time. Therefore, these results suggested that human motion prediction system using NNs is able to forecast various types of human behavior using human motion data obtained from wearable devices based on PGSs.

scholarly journals Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6642
Author(s):  
Javier González-Alonso ◽  
David Oviedo-Pastor ◽  
Héctor J. Aguado ◽  
Francisco J. Díaz-Pernas ◽  
David González-Ortega ◽  
...  
Keyword(s):  
Real Time ◽  
Motion Analysis ◽  
Human Body ◽  
Human Motion ◽  
Measurement Unit ◽  
Human Motion Analysis ◽  
Body Parts ◽  
Entry Level ◽  
Wide Range ◽  
Do It Yourself

Recent studies confirm the applicability of Inertial Measurement Unit (IMU)-based systems for human motion analysis. Notwithstanding, high-end IMU-based commercial solutions are yet too expensive and complex to democratize their use among a wide range of potential users. Less featured entry-level commercial solutions are being introduced in the market, trying to fill this gap, but still present some limitations that need to be overcome. At the same time, there is a growing number of scientific papers using not commercial, but custom do-it-yourself IMU-based systems in medical and sports applications. Even though these solutions can help to popularize the use of this technology, they have more limited features and the description on how to design and build them from scratch is yet too scarce in the literature. The aim of this work is two-fold: (1) Proving the feasibility of building an affordable custom solution aimed at simultaneous multiple body parts orientation tracking; while providing a detailed bottom-up description of the required hardware, tools, and mathematical operations to estimate and represent 3D movement in real-time. (2) Showing how the introduction of a custom 2.4 GHz communication protocol including a channel hopping strategy can address some of the current communication limitations of entry-level commercial solutions. The proposed system can be used for wireless real-time human body parts orientation tracking with up to 10 custom sensors, at least at 50 Hz. In addition, it provides a more reliable motion data acquisition in Bluetooth and Wi-Fi crowded environments, where the use of entry-level commercial solutions might be unfeasible. This system can be used as a groundwork for developing affordable human motion analysis solutions that do not require an accurate kinematic analysis.

Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽  
2020 ◽  
Vol 2020 (2) ◽  
pp. 9-11
Author(s):  
Tomohiro Fukuda
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Computer Games ◽  
Construction Projects ◽  
Mixed Reality ◽  
Semantic Segmentation ◽  
Environment Design ◽  
Aviation Training ◽  
Architecture And Design ◽  
World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

scholarly journals Communication infrastructure for situational awareness enhancement in WAMS with optimal PMU placement

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bhargav Appasani ◽  
Amitkumar Vidyakant Jha ◽  
Sunil Kumar Mishra ◽  
Abu Nasar Ghazali
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Area Measurement ◽  
Measurement Unit ◽  
Monitoring And Control ◽  
Communication Infrastructure ◽  
Wide Area Measurement System ◽  
Digital Elevation ◽  
Elevation Model ◽  
And Control

AbstractReal time monitoring and control of a modern power system has achieved significant development since the incorporation of the phasor measurement unit (PMU). Due to the time-synchronized capabilities, PMU has increased the situational awareness (SA) in a wide area measurement system (WAMS). Operator SA depends on the data pertaining to the real-time health of the grid. This is measured by PMUs and is accessible for data analytics at the data monitoring station referred to as the phasor data concentrator (PDC). Availability of the communication system and communication delay are two of the decisive factors governing the operator SA. This paper presents a pragmatic metric to assess the operator SA and ensure optimal locations for the placement of PMUs, PDC, and the underlying communication infrastructure to increase the efficacy of operator SA. The uses of digital elevation model (DEM) data of the surface topography to determine the optimal locations for the placement of the PMU, and the microwave technology for communicating synchrophasor data is another important contribution carried out in this paper. The practical power grid system of Bihar in India is considered as a case study, and extensive simulation results and analysis are presented for validating the proposed methodology.

scholarly journals Continuous Fusion of Motion Data Using an Axis-Angle Rotation Representation with Uniform B-Spline

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5004
Author(s):  
Haohao Hu ◽  
Johannes Beck ◽  
Martin Lauer ◽  
Christoph Stiller
Keyword(s):  
Forward Direction ◽  
Measurement Unit ◽  
Automated Driving ◽  
Real World Data ◽  
Inertial Measurement ◽  
B Spline ◽  
Motion Data ◽  
Angle Rotation ◽  
Representation Method ◽  
Fusion Approach

The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept.

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