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 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.

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.

scholarly journals An open-source and wearable system for measuring 3D human motion in real-time

2021 ◽  
Author(s):  
Patrick Slade ◽  
Ayman Habib ◽  
Jennifer L. Hicks ◽  
Scott L. Delp
Keyword(s):  
Open Source ◽  
Real Time ◽  
Motion Capture ◽  
Human Motion ◽  
Sensor Systems ◽  
Wearable Sensor ◽  
Joint Angles ◽  
Wearable System ◽  
Optical Motion ◽  
Optical Motion Capture

AbstractAnalyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation interventions for conditions such as osteoarthritis, stroke, and Parkinson’s disease. Optical motion capture systems are the current standard for estimating kinematics but require expensive equipment located in a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Further, many wearable sensor systems require a computer in close proximity and rely on proprietary software, making it difficult for researchers to reproduce experimental findings. Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller. We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking (n = 5) and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task (n = 5). The open-source software and hardware are scalable, tracking between 1 and 14 body segments, with one sensor per segment. Kinematics are estimated in real-time using a musculoskeletal model and inverse kinematics solver. The computation frequency, depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track up to 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment. The OpenSenseRT system is accurate, low-cost, and simple to replicate, enabling movement analysis in labs, clinics, homes, and free-living settings.

Correlational Methods for Analysis of Dance Movements

2011 ◽  
Vol 29 (supplement) ◽  
pp. 283-304 ◽  
Author(s):  
Timothy R. Brick ◽  
Steven M. Boker
Keyword(s):  
Motion Capture ◽  
Mirror Symmetry ◽  
Cross Correlation ◽  
The Other ◽  
Free Form ◽  
Motion Capture Data ◽  
Complex Interactions ◽  
Dance Movements ◽  
Time Lagged ◽  
Insight Into

Among the qualities that distinguish dance from other types of human behavior and interaction are the creation and breaking of synchrony and symmetry. The combination of symmetry and synchrony can provide complex interactions. For example, two dancers might make very different movements, slowing each time the other sped up: a mirror symmetry of velocity. Examining patterns of synchrony and symmetry can provide insight into both the artistic nature of the dance, and the nature of the perceptions and responses of the dancers. However, such complex symmetries are often difficult to quantify. This paper presents three methods – Generalized Local Linear Approximation, Time-lagged Autocorrelation, and Windowed Cross-correlation – for the exploration of symmetry and synchrony in motion-capture data as is it applied to dance and illustrate these with examples from a study of free-form dance. Combined, these techniques provide powerful tools for the examination of the structure of symmetry and synchrony in dance.

Optical Unmarked Motion Capture Technology Based on Depth Network and Binocular Vision

2019 ◽  
Author(s):  
Ye Li ◽  
Wenjie Chen ◽  
Qing You ◽  
Yangyang Sun ◽  
Jing Li
Keyword(s):  
Motion Capture ◽  
Binocular Vision
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