scholarly journals An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Chengyu Guo ◽  
Jie Liu ◽  
Xiaohai Fan ◽  
Aihong Qin ◽  
Xiaohui Liang
Keyword(s):  
Low Cost ◽  
Recognition Rate ◽  
Recognition Task ◽  
Graph Model ◽  
Autoregressive Process ◽  
Human Motion ◽  
Lower Limbs ◽  
Body Motion ◽  
Fusion Model ◽  
Full Body

This paper presents a method to recognize continuous full-body human motion online by using sparse, low-cost sensors. The only input signals needed are linear accelerations without any rotation information, which are provided by four Wiimote sensors attached to the four human limbs. Based on the fused hidden Markov model (FHMM) and autoregressive process, a predictive fusion model (PFM) is put forward, which considers the different influences of the upper and lower limbs, establishes HMM for each part, and fuses them using a probabilistic fusion model. Then an autoregressive process is introduced in HMM to predict the gesture, which enables the model to deal with incomplete signal data. In order to reduce the number of alternatives in the online recognition process, a graph model is built that rejects parts of motion types based on the graph structure and previous recognition results. Finally, an online signal segmentation method based on semantics information and PFM is presented to finish the efficient recognition task. The results indicate that the method is robust with a high recognition rate of sparse and deficient signals and can be used in various interactive applications.

Highly sensitive and stretchable strain sensors based on chopped carbon fibers sandwiched between silicone rubber layers for human motion detections

2019 ◽  
Vol 54 (3) ◽  
pp. 423-434 ◽  
Author(s):  
MB Azizkhani ◽  
Sh Rastgordani ◽  
A. Pourkamali Anaraki ◽  
J Kadkhodapour ◽  
B Shirkavand Hadavand
Keyword(s):  
Carbon Fibers ◽  
Motion Detection ◽  
Silicone Rubber ◽  
Low Cost ◽  
High Sensitivity ◽  
Human Motion ◽  
Strain Sensors ◽  
Body Motion ◽  
Gauge Factor ◽  
Human Motion Detection

Tuning the electromechanical performance in piezoresistive composite strain sensors is primarily attained through appropriately employing the materials system and the fabrication process. High sensitivity along with flexibility in the strain sensing devices needs to be met according to the application (e.g. human motion detection, health and sports monitoring). In this paper, a highly stretchable and sensitive strain sensor with a low-cost fabrication is proposed which is acquired by embedding the chopped carbon fibers sandwiched in between silicone rubber layers. The electrical and mechanical features of the sensor were characterized through stretch/release loading tests where a considerably high sensitivity (the gauge factor about 100) was observed with very low hysteresis. This implies high strain reversibility (i.e. full recovery in each cycle) over 700 loading cycles. Moreover, the sensors exhibited ultra-high stretchability (up to ∼300% elongation) in addition to a low stiffness meaning minimal mechanical effects induced by the sensor for sensitive human motion monitoring applications including large and small deformations. The results suggest the promising capability of the present sensor in reflecting the human body motion detection.

scholarly journals Incremental learning of full body motion primitives and their sequencing through human motion observation

2011 ◽  
Vol 31 (3) ◽  
pp. 330-345 ◽  
Author(s):  
Dana Kulić ◽  
Christian Ott ◽  
Dongheui Lee ◽  
Junichi Ishikawa ◽  
Yoshihiko Nakamura
Keyword(s):  
Incremental Learning ◽  
Human Motion ◽  
Body Motion ◽  
Motion Primitives ◽  
Full Body

scholarly journals Human Motion State Recognition Based on Flexible, Wearable Capacitive Pressure Sensors

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1219
Author(s):  
Qingyang Yu ◽  
Peng Zhang ◽  
Yucheng Chen
Keyword(s):  
Flexible Substrate ◽  
Recognition Rate ◽  
Pressure Sensors ◽  
Recognition System ◽  
Human Motion ◽  
Body Motion ◽  
Capacitive Pressure Sensor ◽  
Wearable Sensor ◽  
State Recognition ◽  
Motion State

Human motion state recognition technology based on flexible, wearable sensor devices has been widely applied in the fields of human–computer interaction and health monitoring. In this study, a new type of flexible capacitive pressure sensor is designed and applied to the recognition of human motion state. The electrode layers use multi-walled carbon nanotubes (MWCNTs) as conductive materials, and polydimethylsiloxane (PDMS) with microstructures is embedded in the surface as a flexible substrate. A composite film of barium titanate (BaTiO3) with a high dielectric constant and low dielectric loss and PDMS is used as the intermediate dielectric layer. The sensor has the advantages of high sensitivity (2.39 kPa−1), wide pressure range (0–120 kPa), low pressure resolution (6.8 Pa), fast response time (16 ms), fast recovery time (8 ms), lower hysteresis, and stability. The human body motion state recognition system is designed based on a multi-layer back propagation neural network, which can collect, process, and recognize the sensor signals of different motion states (sitting, standing, walking, and running). The results indicate that the overall recognition rate of the system for the human motion state reaches 94%. This proves the feasibility of the human motion state recognition system based on the flexible wearable sensor. Furthermore, the system has high application potential in the field of wearable motion detection.

Acceleration Factor Modeling of Flexible Electronic Substrates From Actual Human Body Measurements

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Vikas Yadav ◽  
Jinesh Narangaparambil ◽  
Wei Liu
Keyword(s):  
Human Body ◽  
Flexible Electronics ◽  
Human Motion ◽  
Acceleration Factor ◽  
Body Motion ◽  
Motion Data ◽  
Factor Modeling ◽  
Electronic Substrates ◽  
Resistance Data ◽  
Full Body

Abstract The use of flexible electronics wearable applications has prompted the need to understand the stresses imposed during human motion for a range of activities. Wearable applications may involve situations in which the electronics may be flexed-to-install, stretched or subjected to thousands cycles of dynamic flexing. In order to develop meaningful test-levels, a better understanding is needed of the use-cases, variance, and the acceleration factors. In this study, the human body motion data for walking, jumping, squats, lunges, and bicep curls were measured using a set of ten Vicon cameras to measure the position, velocity, and accelerations of a standard full-body sensor location of the human body. In addition, reliability data has been gathered on test vehicles subjected to dynamic flexing. Continuous resistance data have been gathered on circuits subjected to dynamic flexing till failure for some of the commonly used trace geometries in electronic circuits. Experimental measurements during the accelerated tests of the boards were combined with the human body motion data to model the acceleration factor for different human activities based on the flexing angles. Human motion for multiple subjects and multiple joints has been correlated to the test levels for the development of acceleration factors. Statistical analysis on the variation of the joint angles with hypothesis testing has been conducted for different subjects and for different human body actions. Acceleration factors models have been developed for walking, jumping, squats, lunges, and bicep curls.

scholarly journals CoolMoves

2021 ◽  
Vol 5 (2) ◽  
pp. 1-23
Author(s):  
Karan Ahuja ◽  
Eyal Ofek ◽  
Mar Gonzalez-Franco ◽  
Christian Holz ◽  
Andrew D. Wilson
Keyword(s):  
Human Motion ◽  
Body Motion ◽  
Weighted Distance ◽  
Prediction Probability ◽  
Technical Evaluation ◽  
Spatio Temporal ◽  
Joint Prediction ◽  
Input Cues ◽  
Motion Quality ◽  
Full Body

Current Virtual Reality (VR) systems are bereft of stylization and embellishment of the user's motion - concepts that have been well explored in animations for games and movies. We present CooIMoves, a system for expressive and accentuated full-body motion synthesis of a user's virtual avatar in real-time, from the limited input cues afforded by current consumer-grade VR systems, specifically headset and hand positions. We make use of existing motion capture databases as a template motion repository to draw from. We match similar spatio-temporal motions present in the database and then interpolate between them using a weighted distance metric. Joint prediction probability is then used to temporally smooth the synthesized motion, using human motion dynamics as a prior. This allows our system to work well even with very sparse motion databases (e.g., with only 3-5 motions per action). We validate our system with four experiments: a technical evaluation of our quantitative pose reconstruction and three additional user studies to evaluate the motion quality, embodiment and agency.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

Simulation Analysis of Foot Force in Athletes’ Training Process Based on Sports Biomechanics

2021 ◽  
Vol 7 (5) ◽  
pp. 4900-4913
Author(s):  
Li Huang ◽  
Jianqiu Hu
Keyword(s):  
Simulation Analysis ◽  
Rapid Development ◽  
Human Motion ◽  
Lower Limbs ◽  
Lower Extremity Injury ◽  
Foot Pressure ◽  
Modeling And Analysis ◽  
Analysis System ◽  
Foot Joint ◽  
Sports Biomechanics

Objectives: With the rapid development of sports biomechanics, a new frontier discipline, the modeling and Simulation of human motion, as one of the cutting-edge research topics of sports biomechanics, is receiving more and more attention.. Methods: Based on this, this paper provides theoretical support for the analysis and research of foot stress in the process of training and applies it to guiding practice by using the analysis technology based on sports biomechanics and the method of foot pressure and simulation modeling and analysis system. Results: The results of the study showed that the injury of the athletes in the lower limbs accounted for about 46.7%, followed by the injury of the upper limbs and the injury of the trunk. In the lower extremity injury, the most common part of the foot joint is about 28.1%. Conclusion: Studies have shown that the changes in the force of the athlete’s foot after fatigue have not had the good stability before, the duration of each stage of the completion of the movement is changing, and the control of the ankle joint is decreasing, which greatly increases the foot joint. The possibility of injury.

scholarly journals Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Chunjie Chen ◽  
Xinyu Wu ◽  
Du-xin Liu ◽  
Wei Feng ◽  
Can Wang
Keyword(s):  
Gait Cycle ◽  
Human Motion ◽  
Experimental Result ◽  
Motion Patterns ◽  
Exoskeleton Robot ◽  
Gait Phase ◽  
Phase Recognition ◽  
Motion Intention ◽  
Full Body ◽  
Testing Set

The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS), which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on k-Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

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