Supersensitive all-fabric pressure sensors using printed textile electrode arrays for human motion monitoring and human–machine interaction

2018 ◽  
Vol 6 (48) ◽  
pp. 13120-13127 ◽  
Author(s):  
Ziqiang Zhou ◽  
Ying Li ◽  
Jiang Cheng ◽  
Shanyong Chen ◽  
Rong Hu ◽  
...  
Keyword(s):  
Cotton Fabric ◽  
Pressure Sensors ◽  
Human Motion ◽  
Electrode Arrays ◽  
Human Machine Interaction ◽  
Silver Paste ◽  
Textile Electrode ◽  
Human Motion Monitoring ◽  
Machine Interaction ◽  
Coated Cotton

Supersensitive all-fabric pressure sensors with a bottom interdigitated textile electrode screen-printed using silver paste and a top bridge of AgNW-coated cotton fabric are successfully fabricated for human motion monitoring and human–machine interaction.

Synthesis of 2D Ti3C2Tx MXene for Flexible Strain and Pressure Sensors

2021 ◽  
Author(s):  
Yuping Zeng ◽  
Wei Wu
Keyword(s):  
Human Computer Interaction ◽  
Pressure Sensors ◽  
Human Motion ◽  
Flexible Sensors ◽  
Microelectronic Devices ◽  
Human Motion Monitoring ◽  
Computer Interaction

As an important device in flexible and wearable microelectronic devices, flexible sensors have engaged a lot of attention due to their wide application in human motion monitoring, human-computer interaction and...

scholarly journals sEMG Based Human Motion Intention Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Li Zhang ◽  
Geng Liu ◽  
Bing Han ◽  
Zhe Wang ◽  
Tong Zhang
Keyword(s):  
Human Motion ◽  
Human Machine Interaction ◽  
Existing Problems ◽  
Model Based ◽  
Wearable Robots ◽  
Intention Recognition ◽  
Future Challenges ◽  
Bioelectrical Signal ◽  
Machine Interaction ◽  
Motion Intention

Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

Gold nanoparticle densely packed micro/nanowire-based pressure sensors for human motion monitoring and physiological signal detection

Nanoscale ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 4925-4932 ◽  
Author(s):  
Shun-Xin Li ◽  
Hong Xia ◽  
Yi-Shi Xu ◽  
Chao Lv ◽  
Gong Wang ◽  
...  
Keyword(s):  
Gold Nanoparticles ◽  
Signal Detection ◽  
Gold Nanoparticle ◽  
Pressure Sensors ◽  
Human Motion ◽  
Physiological Signal ◽  
Human Motion Monitoring ◽  
Flexible Pressure Sensors

Gold nanoparticles were assembled into highly aligned micro/nanowires for flexible pressure sensors.

A Wireless Human Motion Monitoring System Based on Joint Angle Sensors and Smart Shoes

2014 ◽  
Author(s):  
Wenlong Zhang ◽  
Masayoshi Tomizuka ◽  
Nancy Byl
Keyword(s):  
Monitoring System ◽  
Joint Angle ◽  
Pressure Sensors ◽  
Human Motion ◽  
Contact Forces ◽  
Three Dimensions ◽  
Sensor Data ◽  
Measurement Unit ◽  
Clinical Environment ◽  
Human Motion Monitoring

In this paper, a wireless human motion monitoring system based on joint angle sensors and smart shoes is introduced. An inertial measurement unit (IMU) is employed in a joint angle sensor to estimate the lower-extremity joint rotation in three dimensions. Four pressure sensors are embedded in a smart shoe to measure the distribution of ground contact forces (GCFs). Zig-bee and Bluetooth modules are combined with the joint angle sensors and smart shoes respectively to make the whole system wireless. It is shown that gait phase and step length can be calculated based on the raw sensor data for gait analysis. To provide visual feedback to the users, with the consent of Apple Inc., an user interface application is developed on an iPad. Experimental results are obtained from both a healthy subject and a stroke patient for comparison. Some discussions are made about the potential use of this system in a clinical environment.

scholarly journals A Wireless Human Motion Monitoring System for Smart Rehabilitation

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Wenlong Zhang ◽  
Masayoshi Tomizuka ◽  
Nancy Byl
Keyword(s):  
Monitoring System ◽  
Visual Feedback ◽  
Inertial Sensors ◽  
Center Of Pressure ◽  
Physical Therapists ◽  
Pressure Sensors ◽  
Disease Patient ◽  
Measurement Data ◽  
Human Motion ◽  
Human Motion Monitoring

In this paper, a wireless human motion monitoring system is presented for gait analysis and visual feedback in rehabilitation training. The system consists of several inertial sensors and a pair of smart shoes with pressure sensors. The inertial sensors can capture lower-extremity joint rotations in three dimensions and the smart shoes can measure the force distributions on the two feet during walking. Based on the raw measurement data, gait phases, step lengths, and center of pressure (CoP) are calculated to evaluate the abnormal walking behaviors. User interfaces are developed on both laptops and mobile devices to provide visual feedback to patients and physical therapists. The system has been tested on healthy subjects and then applied in a clinical study with 24 patients. It has been verified that the patients are able to understand the intuitive visual feedback from the system, and similar training performance has been achieved compared to the traditional gait training with physical therapists. The experimental results with one healthy subject, one stroke patient, and one Parkinson's disease patient are compared to demonstrate the performance of the system.

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