Functional calibration does not improve the concurrent validity of magneto-inertial wearable sensor-based thorax and lumbar angle measurements when compared with retro-reflective motion capture

2021 ◽  
Author(s):  
Daniel S. Cottam ◽  
Amity C. Campbell ◽  
Paul C. Davey ◽  
Peter Kent ◽  
Bruce C. Elliott ◽  
...  
Keyword(s):  
Motion Capture ◽  
Concurrent Validity ◽  
Wearable Sensor ◽  
Angle Measurements

Wearable Sensor Integration and Bio-motion Capture: A Practical Perspective

2014 ◽  
pp. 495-526 ◽  
Author(s):  
Zhiqiang Zhang ◽  
Athanasia Panousopoulou ◽  
Guang-Zhong Yang
Keyword(s):  
Motion Capture ◽  
Sensor Integration ◽  
Wearable Sensor

scholarly journals Reliability and concurrent validity of angle measurements in lower limb: EOS 3D goniometer versus 2D manual goniometer

2020 ◽  
Vol 24 ◽  
pp. 96-102
Author(s):  
Jun Huang ◽  
Fanli Tian ◽  
Zhigang Zhang ◽  
Weidong Shi ◽  
Jun Lin ◽  
...  
Keyword(s):  
Lower Limb ◽  
Concurrent Validity ◽  
Angle Measurements

scholarly journals Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yueru Li
Keyword(s):  
Data Fusion ◽  
Coordinate System ◽  
Motion Capture ◽  
Estimation Algorithm ◽  
Iteration Step ◽  
Whole Body ◽  
Great Likelihood ◽  
Wearable Sensor ◽  
Fusion Algorithm ◽  
Model Learning

In this paper, through an in-depth study and analysis of dance motion capture algorithms in wearable sensor networks, the extended Kalman filter algorithm and the quaternion method are selected after analysing a variety of commonly used data fusion algorithms and pose solving algorithms. In this paper, a sensor-body coordinate system calibration algorithm based on hand-eye calibration is proposed, which only requires three calibration poses to complete the calibration of the whole-body sensor-body coordinate system. In this paper, joint parameter estimation algorithm based on human joint constraints and limb length estimation algorithm based on closed joint chains are proposed, respectively. The algorithm is an iterative optimization algorithm that divides each iteration into an expectation step and a great likelihood step, and the best convergence value can be found efficiently according to each iteration step. The feature values of each pose action are fed into the algorithm for model learning, which enables the training of the model. The trained model is then tested by combining the collected gesture data with the algorithmic model to recognize and classify the gesture data, observe its recognition accuracy, and continuously optimize the model to achieve accurate recognition of human gesture actions.

A Compact Low Cost Wearable Sensor System for Quantitative Gait Measurement

2014 ◽  
Vol 627 ◽  
pp. 212-216
Author(s):  
Ming Gui Tan ◽  
Cheng Boon Leong ◽  
Jee Hou Ho ◽  
Hui Ting Goh ◽  
Hoon Kiat Ng
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Vision System ◽  
Low Cost ◽  
Sensor System ◽  
Wearable Sensor ◽  
Gait Parameters ◽  
Emg Electrodes ◽  
3D Motion Capture ◽  
Quantitative Gait Analysis

The demand for quantitative gait analysis increases due to increasing number of neurological disorder patients. Conventional gait analysis tools such as 3D motion capture systemsare relatively expensive. Therefore, there is a need to develop a low cost sensor system to obtain the spatial temporal gait parameters without compromising too much on the accuracy. This paper describesthe development of a wearable low cost sensor system which consists ofrelatively less sensing elements with 2 accelerometers, 4 force sensitive resistors (FSR) and 2 EMG electrodes. Thesensor output was validated by a vision system and the relative error was less than 5% formost of the gait parameters measured.

Smartphone Inclinometry Is a Valid and Reliable Tool for Measuring Frontal Plane Tibial Alignment in Healthy and Osteoarthritic Knees

2021 ◽  
Author(s):  
Calvin T F Tse ◽  
Jesse M Charlton ◽  
Jennifer Lam ◽  
Joanne Ho ◽  
Jessica Bears ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Motion Capture ◽  
Concurrent Validity ◽  
Clinical Decision Making ◽  
Frontal Plane ◽  
Healthy Individuals ◽  
Validity And Reliability ◽  
Knee Alignment ◽  
Specialized Equipment ◽  
Healthy Participants

Abstract Objective Frontal plane knee alignment plays an integral role in tibiofemoral knee osteoarthritis development and progression. Accessible methods for obtaining direct or indirect measures of knee alignment may help inform clinical decision-making when specialized equipment is unavailable. The current study evaluated the concurrent validity, as well as intersession (within-rater) and interrater (within-session) reliability of smartphone inclinometry for measuring static frontal plane tibial alignment—a known proxy of frontal plane knee alignment. Methods Twenty healthy individuals and thirty-eight patients with knee osteoarthritis were measured for frontal plane tibial alignment by a pair of raters using smartphone inclinometry, manual inclinometry, and three-dimensional motion capture simultaneously. Healthy participants were measured on two separate days. Bland–Altman analysis, supplemented with ICC(2,k), was used to assess concurrent validity. ICC(2,k), standard error of measurement (SEM), and minimum detectable change with 95% confidence limits (MDC95) were used to assess measurement reliability. Results Compared against motion capture, smartphone inclinometry measured frontal plane tibial alignment with a mean difference of 0.7 and 1.1 degrees (biased towards varus) for healthy participants and participants with knee osteoarthritis, respectively [ICC(2,k) ≥ 0.87]. Smartphone inclinometry measurements demonstrated adequate intersession (within-rater) relative [ICC(2,k) = 0.91] and absolute (SEM = 0.7 degrees; MDC95 = 1.8 degrees ) reliability, which outperformed manual inclinometry [ICC(2,k) = 0.85; SEM = 1.0 degree; MDC95 = 2.6 degrees]. Interrater (within-session) reliability of smartphone inclinometry was acceptable in both cohorts [ICC(2,k) = 0.93; SEM = 0.4 degrees to 1.2 degrees; MDC95 = 1.2 degrees to 3.2 degrees]. Conclusions Smartphone inclinometry is sufficiently valid and reliable for measuring frontal plane tibial alignment in healthy individuals and patients with medial tibiofemoral knee osteoarthritis. Impact Smartphones are readily accessible by clinicians and researchers. Our assessment of measurement validity and reliability supports the use of smartphone inclinometry as a clinically available tool to measure frontal plane tibial alignment without medical imaging or specialized equipment.

scholarly journals Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor

2018 ◽  
Author(s):  
Todd J. Hullfish ◽  
Feini Qu ◽  
Brendan D. Stoeckl ◽  
Peter M. Gebhard ◽  
Robert L. Mauck ◽  
...  
Keyword(s):  
Motion Capture ◽  
Knee Flexion ◽  
Low Cost ◽  
Calibration Procedure ◽  
Knee Extension ◽  
Strongly Correlated ◽  
Wearable Sensor ◽  
Additional Equipment ◽  
Peak Knee ◽  
Rms Error

AbstractLow-cost sensors provide a unique opportunity to continuously monitor patient progress during rehabilitation; however, these sensors have yet to demonstrate the fidelity and lack the calibration paradigms necessary to be viable tools for clinical research. Therefore, the purpose of this study was to validate a low-cost wearable sensor that accurately measured peak knee extension during clinical exercises and needed no additional equipment for calibration. Knee flexion was quantified using a 9-axis motion sensor and directly compared to motion capture data. Peak extension values during seated knee extensions were accurate within 5 degrees across all subjects (RMS error: 2.6 degrees, P = 0.29) but less accurate during sit-to-stand exercises (RMS error: 16.6 degrees, P = 0.48). Knee flexion during gait strongly correlated (0.84 ≤ rxy ≤ 0.99) with motion capture measurements but demonstrated average errors of 10 degrees. This study demonstrated a low-cost sensor that satisfied our criteria: a simple calibration procedure resulting in accurate measures of joint function during clinical exercises, making it a feasible tool for continuous patient monitoring to guide regenerative rehabilitation.

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