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.

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.

Flexible Capacitive Pressure Sensors with Micro-Patterned Porous Dielectric Layer for Wearable Electronics

2022 ◽  
Author(s):  
Jing Wang ◽  
Longwei Li ◽  
Lanshuang Zhang ◽  
Panpan Zhang ◽  
Xiong Pu
Keyword(s):  
Pressure Sensor ◽  
Flexible Electronics ◽  
Dielectric Layer ◽  
Pressure Sensors ◽  
High Sensitivity ◽  
Capacitive Sensor ◽  
Human Motion ◽  
Capacitive Pressure Sensor ◽  
Wearable Electronics ◽  
Emulsified Water

Abstract Highly sensitive soft sensors play key roles in flexible electronics, which therefore have attracted much attention in recent years. Herein, we report a flexible capacitive pressure sensor with high sensitivity by using engineered micro-patterned porous polydimethylsiloxane (PDMS) dielectric layer through an environmental-friendly fabrication procedure. The porous structure is formed by evaporation of emulsified water droplets during PDMS curing process, while the micro-patterned structure is obtained via molding on sandpaper. Impressively, this structure renders the capacitive sensor with a high sensitivity up to 143.5 MPa-1 at the pressure range of 0.068~150 kPa and excellent anti-fatigue performance over 20,000 cycles. Meanwhile, the sensor can distinguish different motions of the same person or different people doing the same action. Our work illustrates the promising application prospects of this flexible pressure sensor for the security field or human motion monitoring area.

scholarly journals Fall Recognition System to Determine the Point of No Return in Real-Time

2021 ◽  
Vol 11 (18) ◽  
pp. 8626
Author(s):  
Bae Sun Kim ◽  
Yong Ki Son ◽  
Joonyoung Jung ◽  
Dong-Woo Lee ◽  
Hyung Cheol Shin
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Recognition Rate ◽  
Recognition System ◽  
Learning Model ◽  
Body Motion ◽  
Measurement Unit ◽  
Neural Network Learning ◽  
Point Of No Return ◽  
Time Fall

In this study, we collected data on human falls, occurring in four directions while walking or standing, and developed a fall recognition system based on the center of mass (COM). Fall data were collected from a lower-body motion data acquisition device comprising five inertial measurement unit sensors driven at 100 Hz and labeled based on the COM-norm. The data were learned to classify which stage of the fall a particular instance belongs to. It was confirmed that both the representative convolutional neural network learning model and the long short-term memory learning model were performed within a time of 10 ms on the embedded platform (Jetson TX2) and the recognition rate exceeded 94%. Accordingly, it is possible to verify the progress of the fall during the unbalanced and falling steps, which are classified by subdividing the critical step in which the real-time fall proceeds with the output of the fall recognition model every 10 ms. In addition, it was confirmed that a real-time fall can be judged by specifying the point of no return (PONR) near the point of entry of the falling down stage.

scholarly journals Capture of 3D Human Motion Pose in Virtual Reality Based on Video Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Qiang Fu ◽  
Xingui Zhang ◽  
Jinxiu Xu ◽  
Haimin Zhang
Keyword(s):  
Virtual Reality ◽  
Fuzzy Logic ◽  
Human Body ◽  
Estimation Algorithm ◽  
Human Motion ◽  
Body Motion ◽  
Classification Model ◽  
Accuracy Rate ◽  
Motion State ◽  
Adaptive Adjustment

Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s.

High sensitivity and broad detection range flexible capacitive pressure sensor based on rGO cotton fiber for human motion detection

2021 ◽  
Author(s):  
Rongliang Zheng ◽  
Youyuan Wang ◽  
Zhanxi Zhang ◽  
Yanfang Zhang ◽  
Jinzhan Liu
Keyword(s):  
Pressure Sensor ◽  
Cotton Fiber ◽  
Pressure Sensors ◽  
High Sensitivity ◽  
Human Motion ◽  
Superior Performance ◽  
Capacitive Pressure Sensor ◽  
Joint Movement ◽  
Process Technology ◽  
Flexible Pressure Sensors

Abstract Recently, flexible pressure sensors have attracted considerable interest in electronic skins, wearable devices, intelligent robots and biomedical diagnostics. However, the design of high sensitivity flexible pressure sensors often relies on expensive materials and complex process technology, which greatly limit their popularity and applications. Even worse, chemical-based sensors are poorly biocompatible and harmful to the environment. Here, we developed a flexible capacitive pressure sensor based on reduced graphene oxide (rGO) cotton fiber by a simple and low-cost preparation process. The environmentally friendly sensor exhibited a comprehensive performance with not only ultra-high sensitivity (up to 15.84 kPa-1) and a broad sensing range (0-500 kPa), but also excellent repeatability (over 400 cycles), low hysteresis (≤11.6%), low detection limit (<0.1 kPa) and wide frequency availability (sensitivity from 19.71 kPa-1 to 11.24 kPa-1, frequency from 100 Hz to 10 kHz). Based on its superior performance, the proposed sensor can detect various external stimuli (vertical stress, bending and airflow) and has been successfully applied for facial expression recognition, breathing detection, joint movement and walking detection, showing great potential for application in artificial electronic skin and wearable healthcare devices.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

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.

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