scholarly journals Low Cost Inertial Sensors for the Motion Tracking and Orientation Estimation of Human Upper Limbs in Neurological Rehabilitation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 54254-54268 ◽  
Author(s):  
Lu Bai ◽  
Matthew G. Pepper ◽  
Yong Yan ◽  
Malcolm Phillips ◽  
Mohamed Sakel
Keyword(s):  
Motion Tracking ◽  
Inertial Sensors ◽  
Low Cost ◽  
Upper Limbs ◽  
Neurological Rehabilitation ◽  
Orientation Estimation

Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Arash Atrsaei ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Kalman Filter ◽  
Motion Capture ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Human Motion ◽  
Home Based ◽  
Human Arm ◽  
Arm Motion

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

Body-Segment Orientation Estimation in Rider-Bicycle Interactions With an Un-Calibrated Monocular Camera and Wearable Gyroscopes

2013 ◽  
Author(s):  
Xiang Lu ◽  
Yizhai Zhang ◽  
Kaiyan Yu ◽  
Jingang Yi ◽  
Jingtai Liu
Keyword(s):  
Low Cost ◽  
High Accuracy ◽  
Human Anatomy ◽  
Upper Limbs ◽  
Body Segment ◽  
Orientation Estimation ◽  
Monocular Camera ◽  
Estimation Scheme ◽  
Inertial Measurements ◽  
Segment Orientation

We present a real-time human body-segment (e.g., upper limbs) orientation estimation scheme in rider-bicycle interactions. The estimation scheme is built on the fusion of measurements of an un-calibrated monocular camera on the bicycle and a set of small wearable gyroscopes attached to rider’s upper limbs. The known optical features are conveniently collocated with the gyroscopes. The design of an extended Kalman filter (EKF) to fuse the vision/inertial measurements compensates for the drifting errors by directly integrating gyroscope measurements. The characteristic and constraints from human anatomy and the rider-bicycle interactions are used to enhance the EKF performance. We demonstrate the effectiveness of the estimation design through bicycle riding experiments. The attractive properties of the proposed pose estimation in human-machine interactions include low-cost, high-accuracy, and wearable configurations for outdoor personal activities. Although we only present the application for rider-bicycle interactions, the proposed estimation scheme is readily extended and used for other types of human-machine interactions.

Flight motion tracking with new configuration of hybrid inertial sensors

2020 ◽  
Vol 37 (5) ◽  
pp. 1683-1701
Author(s):  
Xin Wang ◽  
Jie Yan ◽  
Dongzhu Feng ◽  
Yonghua Fan ◽  
Dongsheng Yang
Keyword(s):  
Motion Tracking ◽  
Inertial Sensors ◽  
Low Cost ◽  
Angular Acceleration ◽  
Measurement Unit ◽  
Flight Vehicle ◽  
Body Frame ◽  
Content Type ◽  
Translational Movement ◽  
Angular Velocities

Purpose This paper aims to describe a novel hybrid inertial measurement unit (IMU) for motion capturing via a new configuration of strategically distributed inertial sensors, and a calibration approach for the accelerometer and gyroscope sensors mounted in a flight vehicle motion tracker built on the inertial navigation system. Design/methodology/approach The hybrid-IMU is designed with five accelerometers and one auxiliary gyroscope instead of the accelerometer and gyroscope triads in the conventional IMU. Findings Simulation studies for tracking with both attitude angles and translational movement of a flight vehicle are conducted to illustrate the effectiveness of the proposed method. Originality/value The cross-quadratic terms of angular velocity are selected to process the direct measurements of angular velocities of body frame and to avoid the integration of angular acceleration vector compared with gyro-free configuration based on only accelerometers. The inertial sensors are selected from the commercial microelectromechanical system devices to realize its low-cost applications.

scholarly journals DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

2021 ◽  
Author(s):  
Fabiana Di Ciaccio ◽  
Paolo Russo ◽  
Salvatore Troisi
Keyword(s):  
Deep Learning ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Low Cost ◽  
Human Motion ◽  
High Rate ◽  
The Novel ◽  
Wide Range ◽  
Maritime Navigation ◽  
Low Costs

The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES , a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method.

Development of a low-cost wearable sensing glove with multiple inertial sensors and a light and fast orientation estimation algorithm

2016 ◽  
Vol 74 (8) ◽  
pp. 3639-3652 ◽  
Author(s):  
Younggeun Choi ◽  
Kyounghwan Yoo ◽  
Shin Jin Kang ◽  
Beomjoo Seo ◽  
Soo Kyun Kim
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Orientation Estimation ◽  
Wearable Sensing

scholarly journals A Proposal of a Motion Measurement System to Support Visually Impaired People in Rehabilitation Using Low-Cost Inertial Sensors

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 848
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Miguel Ángel Cuba ◽  
Sergio Sánchez Lara ◽  
José Javier Serrano Olmedo
Keyword(s):  
Visually Impaired ◽  
Inertial Sensors ◽  
Personal Autonomy ◽  
Low Cost ◽  
Step Length ◽  
Hand Position ◽  
Visually Impaired People ◽  
Gait Parameters ◽  
Impaired Person ◽  
Further Development

The rehabilitation of a visually impaired person (VIP) is a systematic process where the person is provided with tools that allow them to deal with the impairment to achieve personal autonomy and independence, such as training for the use of the long cane as a tool for orientation and mobility (O&M). This process must be trained personally by specialists, leading to a limitation of human, technological and structural resources in some regions, especially those with economical narrow circumstances. A system to obtain information about the motion of the long cane and the leg using low-cost inertial sensors was developed to provide an overview of quantitative parameters such as sweeping coverage and gait analysis, that are currently visually analyzed during rehabilitation. The system was tested with 10 blindfolded volunteers in laboratory conditions following constant contact, two points touch, and three points touch travel techniques. The results indicate that the quantification system is reliable for measuring grip rotation, safety zone, sweeping amplitude and hand position using orientation angles with an accuracy of around 97.62%. However, a new method or an improvement of hardware must be developed to improve gait parameters’ measurements, since the step length measurement presented a mean accuracy of 94.62%. The system requires further development to be used as an aid in the rehabilitation process of the VIP. Now, it is a simple and low-cost technological aid that has the potential to improve the current practice of O&M.

scholarly journals Development and Experimental Evaluation of a Low-Cost Cooperative UAV Localization Network Prototype

2018 ◽  
Vol 7 (4) ◽  
pp. 42 ◽  
Author(s):  
Salil Goel ◽  
Allison Kealy ◽  
Bharat Lohani
Keyword(s):  
Information Sharing ◽  
Inertial Sensors ◽  
Low Cost ◽  
Wide Band ◽  
Cooperative Networks ◽  
Global Navigation Satellite Systems ◽  
Cooperative Localization ◽  
Cooperative Network ◽  
Precise Positioning ◽  
Assisted Navigation

Precise localization is one of the key requirements in the deployment of UAVs (Unmanned Aerial Vehicles) for any application including precision mapping, surveillance, assisted navigation, search and rescue. The need for precise positioning is even more relevant with the increasing automation in UAVs and growing interest in commercial UAV applications such as transport and delivery. In the near future, the airspace is expected to be occupied with a large number of unmanned as well as manned aircraft, a majority of which are expected to be operating autonomously. This paper develops a new cooperative localization prototype that utilizes information sharing among UAVs and static anchor nodes for precise positioning of the UAVs. The UAVs are retrofitted with low-cost sensors including a camera, GPS receiver, UWB (Ultra Wide Band) radio and low-cost inertial sensors. The performance of the low-cost prototype is evaluated in real-world conditions in partially and obscured GNSS (Global Navigation Satellite Systems) environments. The performance is analyzed for both centralized and distributed cooperative network designs. It is demonstrated that the developed system is capable of achieving navigation grade (2–4 m) accuracy in partially GNSS denied environments, provided a consistent communication in the cooperative network is available. Furthermore, this paper provides experimental validation that information sharing is beneficial to improve positioning performance even in ideal GNSS environments. The experiments demonstrate that the major challenges for low-cost cooperative networks are consistent connectivity among UAV platforms and sensor synchronization.

Natural Locomotion Based on Foot-Mounted Inertial Sensors in a Wireless Virtual Reality System

2015 ◽  
Vol 24 (4) ◽  
pp. 298-321 ◽  
Author(s):  
Ernesto de la Rubia ◽  
Antonio Diaz-Estrella
Keyword(s):  
Virtual Reality ◽  
Gait Cycle ◽  
Inertial Sensors ◽  
Low Cost ◽  
Point Of View ◽  
Handheld Devices ◽  
Preliminary Evaluation ◽  
Virtual Reality System ◽  
Object Searching

Virtual reality has become a promising field in recent decades, and its potential now seems clearer than ever. With the development of handheld devices and wireless technologies, interest in virtual reality is also increasing. Therefore, there is an accompanying interest in inertial sensors, which can provide such advantages as small size and low cost. Such sensors can also operate wirelessly and be used in an increasing number of interactive applications. An example related to virtual reality is the ability to move naturally through virtual environments. This is the objective of the real-walking navigation technique, for which a number of advantages have previously been reported in terms of presence, object searching, and collision, among other concerns. In this article, we address the use of foot-mounted inertial sensors to achieve real-walking navigation in a wireless virtual reality system. First, an overall description of the problem is presented. Then, specific difficulties are identified, and a corresponding technique is proposed to overcome each: tracking of foot movements; determination of the user’s position; percentage estimation of the gait cycle, including oscillating movements of the head; stabilization of the velocity of the point of view; and synchronization of head and body yaw angles. Finally, a preliminary evaluation of the system is conducted in which data and comments from participants were collected.

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