Development of Flexible Sensors for Measuring Human Motion and Displacement of Novel Flexible Pneumatic Actuator

2011 ◽  
Vol 5 (5) ◽  
pp. 621-628 ◽  
Author(s):  
Tetsuya Akagi ◽  
◽  
Shujiro Dohta ◽  
Hiroaki Kuno ◽  
Akimasa Fukuhara ◽  
...  
Keyword(s):  
Body Movement ◽  
Human Movement ◽  
Human Motion ◽  
The Body ◽  
Displacement Sensor ◽  
Pneumatic Cylinder ◽  
Flexible Sensors ◽  
Sensing System ◽  
Linear Encoder ◽  
Bending Sensor

The importance of wearable devices in nursing care and rehabilitation has been strongly recognized. The purpose of our study is to develop a flexible displacement and bending sensor which can measure human movement and the movement of a flexible actuator. In this paper, a skin displacement sensing system using a flexible string-like flexible displacement sensor to measure the body movement, i.e., measuring the displacement of the skin, was proposed and tested. A flexible linear encoder that can measure the displacement of the flexible pneumatic cylinder using four photoreflectors was also developed. In addition, the measurement using both sensors was done to measure the human motion and the flexible actuator.

Adaptation of multi-segmented body movements during vibratory proprioceptive and galvanic vestibular stimulation

2007 ◽  
Vol 17 (1) ◽  
pp. 47-62
Author(s):  
Per-Anders Fransson ◽  
Magnus Hjerpe ◽  
Rolf Johansson
Keyword(s):  
Body Movement ◽  
Movement Pattern ◽  
Human Movement ◽  
Vestibular Stimulation ◽  
Galvanic Vestibular Stimulation ◽  
The Body ◽  
Body Posture ◽  
Support Surface ◽  
Vibratory Stimulation ◽  
Movement Coordination

Control of orthograde posture and use of adaptive adjustments constitutes essential topics of human movement control, both in maintenance of static posture and in ensuring body stability during locomotion. The objective was to investigate, in twelve normal subjects, how head, shoulder, hip and knee movements and torques induced towards the support surface were affected by vibratory proprioceptive and galvanic vestibular stimulation, and to investigate whether movement pattern, body posture and movement coordination were changed over time. Our findings suggest that the adaptive process to enhance stability involves both alteration of the multi-segmented movement pattern and alteration of body posture. The magnitude of the vibratory stimulation intensity had a prominent influence on the evoked multi-segmented movement pattern. The trial conditions also influenced whether the posture were altered and if these posture adjustments were done directly at stimulation onset or gradually over a longer period. Moreover, the correlation values showed that the subjects, primarily during trials with vibratory stimulation alone, significantly increased the body movement coordination at stimulation onset and maintained this movement pattern throughout the stimulation period. Furthermore, when exposed to balance perturbations the test subjects synchronized significantly the head and torso movements in anteroposterior direction during all trial conditions.

scholarly journals Development of Flexible Pneumatic Cylinder with Built-in Flexible Linear Encoder and Flexible Bending Sensor

2012 ◽  
Vol 6 (4) ◽  
pp. 359-372 ◽  
Author(s):  
Tetsuya AKAGI ◽  
Shujiro DOHTA ◽  
Hisashi MATSUSHITA ◽  
Akimasa FUKUHARA
Keyword(s):  
Pneumatic Cylinder ◽  
Linear Encoder ◽  
Bending Sensor

Video-Based Motion Capture for Measuring Human Movement

2011 ◽  
pp. 81-100 ◽  
Author(s):  
Chee Kwang Quah ◽  
Michael Koh ◽  
Alex Ong ◽  
Hock Soon Seah ◽  
Andre Gagalowicz
Keyword(s):  
Computer Vision ◽  
Digital Media ◽  
Motion Capture ◽  
Human Movement ◽  
Human Motion ◽  
The Body ◽  
Optical Systems ◽  
Human Motion Analysis ◽  
Capture Method ◽  
The Subject

Through the advancement of electronics technologies, human motion analysis applications span many domains. Existing commercially available magnetic, mechanical and optical systems for motion capture and analyses are far from being able to operate in natural scenarios and environments. The current shortcoming of requiring the subject to wear sensors and markers on the body has prompted development directed towards a marker-less setup using computer vision approaches. However, there are still many challenges and problems in computer vision methods such as inconsistency of illumination, occlusion and lack of understanding and representation of its operating scenario. The authors present a videobased marker-less motion capture method that has the potential to operate in natural scenarios such as occlusive and cluttered scenes. In specific applications in sports biomechanics and education, which are stimulated by the usage of interactive digital media and augmented reality, accurate and reliable capture of human motion are essential.

MARKERLESS HUMAN MOTION TRACKING FROM A SINGLE CAMERA USING INTERVAL PARTICLE FILTERING

2007 ◽  
Vol 16 (04) ◽  
pp. 593-609 ◽  
Author(s):  
JAMAL SABOUNE ◽  
FRANÇOIS CHARPILLET
Keyword(s):  
Motion Capture ◽  
Particle Filtering ◽  
Motion Tracking ◽  
Human Movement ◽  
Search Space ◽  
Human Motion ◽  
The Body ◽  
Single Camera ◽  
Human Motion Capture ◽  
Key Points

In this paper we present a new approach for marker less human motion capture from conventional camera feeds. The aim of our study is to recover 3D positions of key points of the body that can serve for gait analysis. Our approach is based on foreground extraction, an articulated body model and particle filters. In order to be generic and simple, no restrictive dynamic modeling was used. A new modified particle-filtering algorithm was introduced. It is used efficiently to search the model configurations space. This new algorithm, which we call Interval Particle Filtering, reorganizes the configurations search space in an optimal deterministic way and proved to be efficient in tracking natural human movement. Results for human motion capture from a single camera are presented and compared to results obtained from a marker based system. The system proved to be able to track motion successfully even in partial occlusions and even outdoors.

Towards a shape-performance integrated digital twin for lumbar spine analysis

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 8
Author(s):  
Xiwang He ◽  
Yiming Qiu ◽  
Xiaonan Lai ◽  
Zhonghai Li ◽  
Liming Shu ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Real Time ◽  
Body Movement ◽  
Physical Space ◽  
Gaussian Process Regression ◽  
Human Movement ◽  
The Body ◽  
Virtual Space ◽  
The Real ◽  
Digital Twin

Background: With significant advancement and demand for digital transformation, the digital twin has been gaining increasing attention as it is capable of establishing real-time mapping between physical space and virtual space. In this work, a shape-performance integrated digital twin solution is presented to predict the real-time biomechanics of the lumbar spine during human movement. Methods: A finite element model (FEM) of the lumbar spine was firstly developed using computed tomography (CT) and constrained by the body movement which was calculated by the inverse kinematics algorithm. The Gaussian process regression was utilized to train the predicted results and create the digital twin of the lumbar spine in real-time. Finally, a three-dimensional virtual reality system was developed using Unity3D to display and record the real-time biomechanics performance of the lumbar spine during body movement. Results: The evaluation results presented an agreement (R-squared > 0.8) between the real-time prediction from digital twin and offline FEM prediction. Conclusions: This approach provides an effective method of real-time planning and warning in spine rehabilitation.

Video-Based Motion Capture for Measuring Human Movement

2011 ◽  
pp. 209-228
Author(s):  
Chee Kwang Quah ◽  
Michael Koh ◽  
Alex Ong ◽  
Hock Soon Seah ◽  
Andre Gagalowicz
Keyword(s):  
Computer Vision ◽  
Digital Media ◽  
Motion Capture ◽  
Human Movement ◽  
Human Motion ◽  
The Body ◽  
Optical Systems ◽  
Human Motion Analysis ◽  
Capture Method ◽  
The Subject

Through the advancement of electronics technologies, human motion analysis applications span many domains. Existing commercially available magnetic, mechanical and optical systems for motion capture and analyses are far from being able to operate in natural scenarios and environments. The current shortcoming of requiring the subject to wear sensors and markers on the body has prompted development directed towards a marker-less setup using computer vision approaches. However, there are still many challenges and problems in computer vision methods such as inconsistency of illumination, occlusion and lack of understanding and representation of its operating scenario. The authors present a videobased marker-less motion capture method that has the potential to operate in natural scenarios such as occlusive and cluttered scenes. In specific applications in sports biomechanics and education, which are stimulated by the usage of interactive digital media and augmented reality, accurate and reliable capture of human motion are essential.

scholarly journals Analysis and Research on Human Movement in Sports Scene

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yan Wang ◽  
Yuchen Zhang ◽  
LinJun Shen ◽  
ShuMing Wang
Keyword(s):  
Body Movement ◽  
Movement Analysis ◽  
Human Movement ◽  
Human Motion ◽  
Whole Body ◽  
Human Motion Analysis ◽  
Analysis Method ◽  
Multilabel Classification ◽  
Movement Process ◽  
Rope Skipping

As a whole-body sport, skipping rope plays an increasingly important role in daily life. In rope-skipping education, due to the lack of professional teachers, the training efficiency of students is low. The rope-skipping monitoring device is heavy and expensive, and the cost of labor statistics and energy consumption are high. In order to quickly analyze the movement process of students and provide correct guidance, this article implements the movement analysis method of the human body movement process. The problem of limb posture analysis in rope skipping is transformed into a multilabel classification problem, a real-time human motion analysis method based on mobile vision is proposed, and the algorithm model is verified in the rope-skipping scene. The experimental results prove that this paper proposes the improved algorithm, which achieved the expected effect. In the analysis of rope-skipping action, the choice of hyperparameters during the experiment is introduced, and it is verified that the proposed ALSTM-LSTM can solve the problem of multilabel classification in the rope-skipping process. The accuracy rate reaches 95.1%, and it can provide the best in all indicators and good performance. It is of great significance for movement analysis and movement quality evaluation during exercise.

The Visual Simulation Analysis of Human Body Movement Model

2014 ◽  
Vol 556-562 ◽  
pp. 3913-3916
Author(s):  
Jun Jie Wang
Keyword(s):  
Human Body ◽  
Simulation Analysis ◽  
Body Movement ◽  
Linear Optimization ◽  
Human Movement ◽  
The Body ◽  
Body Parts ◽  
Optimization Strategy ◽  
Matching Accuracy ◽  
Movement Model

This paper proposes the re-built human body movement model with multiple cameras. In the tracking frame of the non-linear optimization strategy, the paper builds the body dynamic model to dynamically simulate the human movement which effectively solves the issues of the body parts overlap and tracking errors accumulate. Compared with traditional methods, the required equipment is very economic and the matching accuracy of the algorithm is quite high. The paper applies the athletes as the experimental examples which illustrate the proposed algorithm can effectively increase the 3D image tracking matching accuracy in dynamic videos as the analysis basis.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

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