scholarly journals Validation of wearable visual feedback for retraining foot progression angle using inertial sensors and an augmented reality headset

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Angelos Karatsidis ◽  
Rosie E. Richards ◽  
Jason M. Konrath ◽  
Josien C. van den Noort ◽  
H. Martin Schepers ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Visual Feedback ◽  
Inertial Sensors ◽  
Foot Progression Angle

scholarly journals A Wireless Human Motion Monitoring System for Smart Rehabilitation

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Wenlong Zhang ◽  
Masayoshi Tomizuka ◽  
Nancy Byl
Keyword(s):  
Monitoring System ◽  
Visual Feedback ◽  
Inertial Sensors ◽  
Center Of Pressure ◽  
Physical Therapists ◽  
Pressure Sensors ◽  
Disease Patient ◽  
Measurement Data ◽  
Human Motion ◽  
Human Motion Monitoring

In this paper, a wireless human motion monitoring system is presented for gait analysis and visual feedback in rehabilitation training. The system consists of several inertial sensors and a pair of smart shoes with pressure sensors. The inertial sensors can capture lower-extremity joint rotations in three dimensions and the smart shoes can measure the force distributions on the two feet during walking. Based on the raw measurement data, gait phases, step lengths, and center of pressure (CoP) are calculated to evaluate the abnormal walking behaviors. User interfaces are developed on both laptops and mobile devices to provide visual feedback to patients and physical therapists. The system has been tested on healthy subjects and then applied in a clinical study with 24 patients. It has been verified that the patients are able to understand the intuitive visual feedback from the system, and similar training performance has been achieved compared to the traditional gait training with physical therapists. The experimental results with one healthy subject, one stroke patient, and one Parkinson's disease patient are compared to demonstrate the performance of the system.

Augmented Reality Enhanced Nanomanipulation by Atomic Force Microscopy With Local Scan

2007 ◽  
Author(s):  
Guangyong Li ◽  
Lianqing Liu ◽  
Ning Xi
Keyword(s):  
Augmented Reality ◽  
Visual Feedback ◽  
Robotic System ◽  
Force Microscopy ◽  
Position Errors ◽  
Positioning Errors ◽  
Atomic Force ◽  
Nano Sensors ◽  
Low Efficiency ◽  
Time Changes

Atomic Force Microscope (AFM) has been used to manipulate nano-objects for more than a decade. However, it is still in the infant stage to serve as a manufacturing tool for fabrication of nanodevices. The major hindrance is the low efficiency due to the absence of visual feedback, positioning errors, and losing objects during manipulation. The lack of visual feedback can be solved by integrating an augmented reality interface into an AFM based nano-robotic system. Through the augmented reality interface, the operator can manipulate the nano-objects and simultaneously observe the real-time changes of the nano-environment. Position errors caused by thermal drift and nonlinearity of piezoactuators often lead the AFM tip to a wrong position and in turn miss the nano-objects. Due to the small touching area between AFM tip and the object, the tip often slips over or slips aside the nano-object during manipulation. All these problems can be solved by introducing a local scan mechanism to the AFM based robotic system. The local scan strategies will improve the reliability of the visual feedback, therefore, significantly improve the efficiency of AFM based nano-manipulation. In this paper, the augmented reality interface is briefly introduced. And then the local scan strategies are proposed to eliminate the positioning errors, relocate the actual position of nano-objects, and find back the nano-objects if they are lost during manipulation. The paper finally demonstrates that single carbon nanotube (CNT) based nano-sensors can be fabricated by the AFM based nano-robotic system assisted by local scan.

scholarly journals An Augmented Reality U-Academy Module: From Basic Principles to Connected Subjects

2017 ◽  
Vol 11 (5) ◽  
pp. 105 ◽  
Author(s):  
Paulo Menezes
Keyword(s):  
Augmented Reality ◽  
Computer Graphics ◽  
Pose Estimation ◽  
Inertial Sensors ◽  
Pattern Detection ◽  
Final Part ◽  
Basic Principles ◽  
Virtual And Augmented Reality ◽  
Basic Concepts

<p class="0abstract">A module for learning about virtual and augmented reality is being developed under the U-Academy project. The module is composed of three parts. The first part is an introduction to the basic concepts of virtual and augmented reality with the help of illustrative examples. The second part presents some of the current uses of augmented reality and its prospective use in several areas that range from industry to medicine. The final part aims at those students interested in the insights of this technology by presenting the underlying concepts such as: camera models, computer graphics, pattern detection and pose estimation from inertial sensors or camera images.</p>

scholarly journals Walking With Augmented Reality: A Preliminary Assessment of Visual Feedback With a Cable-Driven Active Leg Exoskeleton (C-ALEX)

2019 ◽  
Vol 4 (4) ◽  
pp. 3948-3954 ◽  
Author(s):  
Rand Hidayah ◽  
Siddharth Chamarthy ◽  
Avni Shah ◽  
Matthew Fitzgerald-Maguire ◽  
Sunil K. Agrawal
Keyword(s):  
Augmented Reality ◽  
Visual Feedback ◽  
Preliminary Assessment

scholarly journals The effect of changing foot progression angle using real-time visual feedback on rearfoot eversion during running

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246425 ◽  
Author(s):  
Seyed Hamed Mousavi ◽  
Laurens van Kouwenhove ◽  
Reza Rajabi ◽  
Johannes Zwerver ◽  
Juha M. Hijmans
Keyword(s):  
Real Time ◽  
Visual Feedback ◽  
Knee Flexion ◽  
Medial Longitudinal Arch ◽  
Foot Progression Angle ◽  
Longitudinal Arch ◽  
Peak Knee ◽  
Joint Biomechanics ◽  
Rearfoot Eversion ◽  
Hip Internal Rotation

Atypical rearfoot in/eversion may be an important risk factor for running-related injuries. Prominent interventions for atypical rearfoot eversion include foot orthoses, footwear, and taping but a modification derived from gait retraining to correct atypical rearfoot in/eversion is lacking. We aimed to investigate changes in rearfoot in/eversion, subtalar pronation, medial longitudinal arch angle, and selected lower limb joint biomechanics while performing toe-in/toe-out running using real-time visual feedback. Fifteen female runners participated in this study. Subjects performed toe-in/toe-out running using real-time visual feedback on foot progression angle, which was set ±5° from habitual foot progression angle. 3D kinematics of rearfoot in/eversion, subtalar supination/pronation, medial longitudinal arch angle, foot progression angle, hip flexion, ab/adduction and internal/external rotation, knee flexion, ankle dorsiflexion, and ankle power were analyzed. A repeated-measures ANOVA followed by pairwise comparisons was used to analyze changes between three conditions. Toe-in running compared to normal and toe-out running reduced peak rearfoot eversion (mean difference (MD) with normal = 2.1°; p<0.001, MD with toe-out = 3.5°; p<0.001), peak pronation (MD with normal = -2.0°; p<0.001, MD with toe-out = -3.4; p = <0.001), and peak medial longitudinal arch angle (MD with normal = -0.7°; p = 0.022, MD with toe-out = -0.9; p = 0.005). Toe-out running significantly increased these kinematic factors compared to normal and toe-in running. Toe-in running compared to normal running increased peak hip internal rotation (MD = 2.3; p<0.001), and reduced peak knee flexion (MD = 1.3; p = 0.014). Toe-out running compared to normal running reduced peak hip internal rotation (MD = 2.5; p<0.001), peak hip ab/adduction (MD = 2.5; p<0.001), peak knee flexion (MD = 1.5; p = 0.003), peak ankle dorsiflexion (MD = 1.6; p<0.001), and peak ankle power (MD = 1.3; p = 0.001). Runners were able to change their foot progression angle when receiving real-time visual feedback for foot progression angle. Toe-in/toe-out running altered rearfoot kinematics and medial longitudinal arch angle, therefore supporting the potential value of gait retraining focused on foot progression angle using real-time visual feedback when atypical rearfoot in/eversion needs to be modified. It should be considered that changes in foot progression angle when running is accompanied by changes in lower limb joint biomechanics.

Three-Dimensional Motion and Structure Estimation Using Inertial Sensors and Computer Vision for Augmented Reality

2002 ◽  
Vol 11 (5) ◽  
pp. 474-492 ◽  
Author(s):  
Lin Chai ◽  
William A. Hoff ◽  
Tyrone Vincent
Keyword(s):  
Augmented Reality ◽  
Inertial Sensors ◽  
A Priori ◽  
3D Structure ◽  
Three Dimensional ◽  
Real Data ◽  
Single Camera ◽  
Extended Kalman Filters ◽  
Structure Estimation ◽  
Better Than

A new method for registration in augmented reality (AR) was developed that simultaneously tracks the position, orientation, and motion of the user's head, as well as estimating the three-dimensional (3D) structure of the scene. The method fuses data from head-mounted cameras and head-mounted inertial sensors. Two extended Kalman filters (EKFs) are used: one estimates the motion of the user's head and the other estimates the 3D locations of points in the scene. A recursive loop is used between the two EKFs. The algorithm was tested using a combination of synthetic and real data, and in general was found to perform well. A further test showed that a system using two cameras performed much better than a system using a single camera, although improving the accuracy of the inertial sensors can partially compensate for the loss of one camera. The method is suitable for use in completely unstructured and unprepared environments. Unlike previous work in this area, this method requires no a priori knowledge about the scene, and can work in environments in which the objects of interest are close to the user.

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