scholarly journals Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology

2020 ◽  
Vol 12 (23) ◽  
pp. 9796
Author(s):  
J. M. Olivart i Llop ◽  
D. Moreno-Salinas ◽  
J. Sánchez
Keyword(s):  
Real Time ◽  
Software Design ◽  
Reference System ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Research Areas ◽  
Principal Axes ◽  
Requirement Definition ◽  
Measurement Units ◽  
And Performance

An accurate positioning and attitude computation of vehicles, robots, or even persons is of the utmost importance and critical for the success of many operations in multiple commercial, industrial, and research areas. However, most of these positioning and attitude systems rely on inertial measurement units that must be periodically recalibrated and have a high cost. In the present work, the design of a real-time positioning and attitude system using three positioning sensors based on the GNSS-RTK technology is presented. This kind of system does not need recalibration, and it allows one to define the attitude of a solid by only computing the position of the system in the global reference system and the three angles that the relative positions of the GNSS antennas define with respect to the principal axes of the solid. The position and attitude can be computed in real time for both static and dynamic scenarios. The only limitation of the system is that the antennas need to be in open air to work at full performance and accuracy. All the design phases are covered in the prototype construction: requirement definition, hardware selection, software design, assembly, and validation. The feasibility and performance of the system were tested in both static and dynamic real scenarios.

scholarly journals Toward Real-Time Automated Detection of Turns during Gait Using Wearable Inertial Measurement Units

Sensors ◽  
2014 ◽  
Vol 14 (10) ◽  
pp. 18800-18822 ◽  
Author(s):  
Domen Novak ◽  
Maja Goršič ◽  
Janez Podobnik ◽  
Marko Munih
Keyword(s):  
Real Time ◽  
Automated Detection ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units

BPNN-Based Real-Time Recognition of Locomotion Modes for an Active Pelvis Orthosis with Different Assistive Strategies

2020 ◽  
Vol 17 (01) ◽  
pp. 2050004
Author(s):  
Cheng Gong ◽  
Dongfang Xu ◽  
Zhihao Zhou ◽  
Nicola Vitiello ◽  
Qining Wang
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Experimental Setup ◽  
Inertial Measurement Units ◽  
Recognition Method ◽  
Intent Recognition ◽  
Inertial Measurement ◽  
Wearable Robots ◽  
Measurement Units ◽  
The Cost

Real-time human intent recognition is important for controlling low-limb wearable robots. In this paper, to achieve continuous and precise recognition results on different terrains, we propose a real-time training and recognition method for six locomotion modes including standing, level ground walking, ramp ascending, ramp descending, stair ascending and stair descending. A locomotion recognition system is designed for the real-time recognition purpose with an embedded BPNN-based algorithm. A wearable powered orthosis integrated with this system and two inertial measurement units is used as the experimental setup to evaluate the performance of the designed method while providing hip assistance. Experiments including on-board training and real-time recognition parts are carried out on three able-bodied subjects. The overall recognition accuracies of six locomotion modes based on subject-dependent models are 98.43% and 98.03% respectively, with the wearable orthosis in two different assistance strategies. The cost time of recognition decision delivered to the orthosis is about 0.9[Formula: see text]ms. Experimental results show an effective and promising performance of the proposed method to realize real-time training and recognition for future control of low-limb wearable robots assisting users on different terrains.

scholarly journals On the use of inertial measurement units for real-time quantification of pathological tremor amplitude and frequency

2009 ◽  
Vol 1 (1) ◽  
pp. 1219-1222 ◽  
Author(s):  
J.A. Gallego ◽  
E. Rocon ◽  
J.O. Roa ◽  
J.C. Moreno ◽  
A.D. Koutsou ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Pathological Tremor

scholarly journals Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 106
Author(s):  
Chih-Ya Chang ◽  
Chia-Yeh Hsieh ◽  
Hsiang-Yun Huang ◽  
Yung-Tsan Wu ◽  
Liang-Cheng Chen ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Frozen Shoulder ◽  
Quantitative Information ◽  
Assessment Tools ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
And Performance ◽  
Task Segmentation ◽  
Task Identification

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment.

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