scholarly journals Optical Fiber Force Myography Sensor for Identification of Hand Postures

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Eric Fujiwara ◽  
Carlos Kenichi Suzuki
Keyword(s):  
Optical Fiber ◽  
Wrist Joint ◽  
Low Cost ◽  
Neural Network Classifier ◽  
Optical Signals ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Average Accuracy ◽  
Artificial Neural Network Classifier ◽  
Force Myography

A low-cost optical fiber force myography sensor for noninvasive hand posture identification is proposed. The transducers are comprised of 10 mm periodicity silica multimode fiber microbending devices mounted in PVC plates, providing 0.05 N−1 sensitivity over ~20 N range. Next, the transducers were attached to the user forearm by means of straps in order to monitor the posterior proximal radial, the anterior medial ulnar, and the posterior distal radial muscles, and the acquired FMG optical signals were correlated to the performed gestures using a 5 hidden layers, 20-neuron artificial neural network classifier with backpropagation architecture, followed by a competitive layer. The overall results for 9 postures and 6 subjects indicated a 98.4% sensitivity and 99.7% average accuracy, being comparable to the electromyographic approaches. Moreover, in contrast to the current setups, the proposed methodology allows the identification of poses characterized by different configurations of fingers and wrist joint displacements with the utilization of only 3 transducers and a simple interrogation scheme, being suitable to further applications in human-computer interfaces.

scholarly journals Characterization of Hand Gestures by a Smartphone-Based Optical Fiber Force Myography Sensor

2020 ◽  
Vol 2 (1) ◽  
pp. 46
Author(s):  
Matheus S. Rodrigues ◽  
Pedro M. Lazari ◽  
Marco C. P. Soares ◽  
Eric Fujiwara
Keyword(s):  
Optical Fiber ◽  
Optical Fibers ◽  
Low Cost ◽  
Optical Fiber Sensor ◽  
Hand Gestures ◽  
Optical Signals ◽  
Integrated Optical ◽  
Reliable Performance ◽  
Force Myography ◽  
Polymer Optical Fibers

In this paper, a smartphone-integrated, optical fiber sensor based on the force myography technique (FMG), which characterizes the stimuli of the forearm muscles in terms of mechanical pressures, was proposed for the identification of hand gestures. The device’s flashlight excites a pair of polymer optical fibers and the output signals are detected by the camera. The light intensity is modulated through wearable, force-driven microbending transducers placed in the forearm and the acquired optical signals are processed by an algorithm based on decision trees and residual error. The sensor provided a hit rate of 87% regarding four postures, yielding reliable performance with a simple, portable, and low-cost setup embedded on a smartphone.

scholarly journals Heterogeneous integration of rigid, soft, and liquid materials for self-healable, recyclable, and reconfigurable wearable electronics

2020 ◽  
Vol 6 (45) ◽  
pp. eabd0202
Author(s):  
Chuanqian Shi ◽  
Zhanan Zou ◽  
Zepeng Lei ◽  
Pengcheng Zhu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Low Cost ◽  
Well Being ◽  
Electronic System ◽  
Physical Activities ◽  
Heterogeneous Integration ◽  
Wearable Electronics ◽  
Materials Chemistry ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Wearable Electronic

Wearable electronics can be integrated with the human body for monitoring physical activities and health conditions, for human-computer interfaces, and for virtual/augmented reality. We here report a multifunctional wearable electronic system that combines advances in materials, chemistry, and mechanics to enable superior stretchability, self-healability, recyclability, and reconfigurability. This electronic system heterogeneously integrates rigid, soft, and liquid materials through a low-cost fabrication method. The properties reported in this wearable electronic system can find applications in many areas, including health care, robotics, and prosthetics, and can benefit the well-being, economy, and sustainability of our society.

scholarly journals TWO LOW-COST HUMAN COMPUTER INTERFACES FOR PEOPLE WITH SEVERE DISABILITIES

2004 ◽  
Vol 16 (06) ◽  
pp. 344-349 ◽  
Author(s):  
MU-CHUN SU ◽  
YANG-HAN LEE ◽  
CHENG-HUI WU ◽  
SHI-YONG SU ◽  
YU-XIANG ZHAO
Keyword(s):  
Low Cost ◽  
Severe Disabilities ◽  
Disabled Persons ◽  
Main Application ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
The Disabled ◽  
Potential Applications ◽  
And Control

The object of this paper is to present a set of techniques integrated into two low-cost human computer interfaces. Although the interfaces have many potential applications, one main application is to help the disabled persons to attain or regain some degree of independent communications and control. The first interface is a voice-controlled mouse and the second one is an accelerometer-based mouse.

scholarly journals Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network

2020 ◽  
Vol 14 ◽  
Author(s):  
Qinghua Zhong ◽  
Yongsheng Zhu ◽  
Dongli Cai ◽  
Luwei Xiao ◽  
Han Zhang
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Frequency Band ◽  
Markov Models ◽  
Recognition Task ◽  
Hybrid Network ◽  
Neural Network Classifier ◽  
Average Accuracy ◽  
Artificial Neural Network Classifier ◽  
Electroencephalogram Eeg

In the human-computer interaction (HCI), electroencephalogram (EEG) access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hybrid network was proposed in this paper. Firstly, the collected EEG was decomposed into four frequency band signals, and the multiscale sample entropy (MSE) features of each frequency band were extracted. Secondly, the constructed 3D MSE feature matrices were fed into a deep hybrid network for autonomous learning. The deep hybrid network was composed of a continuous convolutional neural network (CNN) and hidden Markov models (HMMs). Lastly, HMMs trained with multiple observation sequences were used to replace the artificial neural network classifier in the CNN, and the emotion recognition task was completed by HMM classifiers. The proposed method was applied to the DEAP dataset for emotion recognition experiments, and the average accuracy could achieve 79.77% on arousal, 83.09% on valence, and 81.83% on dominance. Compared with the latest related methods, the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

scholarly journals Identification of Words and Phrases Through a Phonemic-Based Haptic Display

2021 ◽  
Vol 18 (3) ◽  
pp. 1-22
Author(s):  
Charlotte M. Reed ◽  
Hong Z. Tan ◽  
Yang Jiao ◽  
Zachary D. Perez ◽  
E. Courtenay Wilson
Keyword(s):  
Speech Communication ◽  
Haptic Display ◽  
Communication Aids ◽  
Remote Communication ◽  
Computer Interfaces ◽  
Speech Reception ◽  
Communication Device ◽  
Human Computer Interfaces ◽  
Sensory Impairments ◽  
Effective Transmission

Stand-alone devices for tactile speech reception serve a need as communication aids for persons with profound sensory impairments as well as in applications such as human-computer interfaces and remote communication when the normal auditory and visual channels are compromised or overloaded. The current research is concerned with perceptual evaluations of a phoneme-based tactile speech communication device in which a unique tactile code was assigned to each of the 24 consonants and 15 vowels of English. The tactile phonemic display was conveyed through an array of 24 tactors that stimulated the dorsal and ventral surfaces of the forearm. Experiments examined the recognition of individual words as a function of the inter-phoneme interval (Study 1) and two-word phrases as a function of the inter-word interval (Study 2). Following an average training period of 4.3 hrs on phoneme and word recognition tasks, mean scores for the recognition of individual words in Study 1 ranged from 87.7% correct to 74.3% correct as the inter-phoneme interval decreased from 300 to 0 ms. In Study 2, following an average of 2.5 hours of training on the two-word phrase task, both words in the phrase were identified with an accuracy of 75% correct using an inter-word interval of 1 sec and an inter-phoneme interval of 150 ms. Effective transmission rates achieved on this task were estimated to be on the order of 30 to 35 words/min.

scholarly journals An Enhanced Plastic Optical Fiber-Based Surface Plasmon Resonance Sensor with a Double-Sided Polished Structure

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1516
Author(s):  
Lian Liu ◽  
Shijie Deng ◽  
Jie Zheng ◽  
Libo Yuan ◽  
Hongchang Deng ◽  
...  
Keyword(s):  
Surface Plasmon Resonance ◽  
Optical Fiber ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Low Cost ◽  
Fiber Axis ◽  
Plastic Optical Fiber ◽  
Au Film ◽  
Spr Sensor ◽  
Resonance Sensor

An enhanced plastic optical fiber (POF)-based surface plasmon resonance (SPR) sensor is proposed by employing a double-sided polished structure. The sensor is fabricated by polishing two sides of the POF symmetrically along with the fiber axis, and a layer of Au film is deposited on each side of the polished region. The SPR can be excited on both polished surfaces with Au film coating, and the number of light reflections will be increased by using this structure. The simulation and experimental results show that the proposed sensor has an enhanced SPR effect. The visibility and full width at half maximum (FWHM) of spectrum can be improved for the high measured refractive index (RI). A sensitivity of 4284.8 nm/RIU is obtained for the double-sided POF-based SPR sensor when the measured liquid RI is 1.42. The proposed SPR sensor is easy fabrication and low cost, which can provide a larger measurement range and action area to the measured samples, and it has potential application prospects in the oil industry and biochemical sensing fields.

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