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A special purpose embedded system for neural machine interface for artificial legs
2011
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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This paper presents a design and implementation of a neural-machine interface (NMI) for artificial legs that can decode amputee's intended movement in real time. ...
performance for real-time controlling of artificial legs. ...
A neural-machine interface (NMI) that deciphers neural signals from amputees to identify the users' intended movements is the center of the neural control system for artificial limbs. ...
doi:10.1109/iembs.2011.6091288
pmid:22255511
pmcid:PMC3676650
dblp:conf/embc/ZhangHY11
fatcat:pdoqq7zt2zgdpodtts5uvgk6mu
On Design and Implementation of Neural-Machine Interface for Artificial Legs
2012
IEEE Transactions on Industrial Informatics
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The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. ...
to interpret such signals, and make decisions to identify user's intent for prostheses control in real time. ...
Fig. 1 . 1 Software architecture of EMG-based neural-machine interface for artificial legs.
Fig. 2 . 2 Hardware architecture of designed neural-machine interface. ...
doi:10.1109/tii.2011.2166770
pmid:22389637
pmcid:PMC3290414
fatcat:2j74i4q5ejbxffgl4pduj5kwzy
Integrating neuromuscular and cyber systems for neural control of artificial legs
2010
Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems - ICCPS '10
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Our preliminary experiment on a human subject demonstrated the feasibility of our designed real-time neuralmachine interface for artificial legs. ...
This paper presents a design and implementation of a cyberphysical system (CPS) for neurally controlled artificial legs. ...
Figure 2 . 2 Hardware architecture of designed neural-machine interface.
Figure 3 . 3 Block diagram of embedded system design on MPC5566 EVB for real-time testing. ...
doi:10.1145/1795194.1795213
dblp:conf/iccps/HuangSYZZLRS10
fatcat:ajgpx6l7rrb4ni5jgak2dxxgya
Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning
2022
Micromachines
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The aim of this study is to develop a new device for controlling a drone in a virtual environment. This device is attached to the upper limb of the person involved in the control of that drone. ...
For precise control, the newly created device uses MEMS sensor technology and artificial intelligence-specific methods. ...
It allows precise control due to the combination of MEMS sensor technology and the MLP artificial neural network. This device allows for control of a drone in a virtual reality simulator. ...
doi:10.3390/mi13040521
pmid:35457827
pmcid:PMC9024457
fatcat:ootcdhc3tvav3dfa56mcmhohay
AI and Robotics to Rehabilitate Lower Body Paralysis
2021
International Journal for Research in Applied Science and Engineering Technology
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However, a very important part of bringing these opportunities to success is guaranteeing that approaching AI technology works well for individuals with a large variety of skills. ...
We will disclose some machines that have an excellent impact on the lives of the elderly and other people with disabilities. ...
The lower extremity exoskeleton robot, a special artificial limb, which enwraps the lower limbs of the physical body, is an integrated application of the exoskeleton robots in lower artificial limbs. ...
doi:10.22214/ijraset.2021.36223
fatcat:zvbaoktya5batoxj7oarkl322m
Promise of a low power mobile CPU based embedded system in artificial leg control
2012
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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) for artificial limbs. ...
This paper presents the design and implementation of a low power embedded system using mobile processor technology (Intel Atom™ Z530 Processor) specifically tailored for a neural-machine interface (NMI ...
These difficulties limit our capability to further optimize and develop the NMI for neural control of powered lower limb prostheses. ...
doi:10.1109/embc.2012.6347178
pmid:23367113
pmcid:PMC3676646
fatcat:lhdwqre6cnfxbnhljmygvxas5i
The neural control of limb movement
1992
IEEE Control Systems
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Additionally, new tools like computer simulations, medical imaging and computational models are being used to simulate simple movement tasks and compare the outcomes with real limb control and neural elements ...
Researchers are regularly improving existing devices for ease of use and efficiency, and new ones are being developed such that it can mimic the maneuverability of the natural limb. ...
Thus, Kwon and Kim [110] noted this issue of intention sensing of human motion and devised a method for real time upper limb motion prediction using EMG signals and an artificial neural network algorithm ...
doi:10.1109/37.168814
fatcat:pwdrjhfvvfce7jdtzipqiqccuq
Implementing an FPGA system for real-time intent recognition for prosthetic legs
2012
Proceedings of the 49th Annual Design Automation Conference on - DAC '12
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This paper presents the design and implementation of a cyber physical system (CPS) for neural-machine interface (NMI) that continuously senses signals from a human neuromuscular control system and recognizes ...
The real-time experiments on a human subject demonstrated its real-time, self-contained, and high accuracy in identifying three major lower limb movement tasks (level-ground walking, stair ascent, and ...
The authors thank Fan Zhang, Quan Ding, Ding Wang, Lin Du, and Ming Liu at the University of Rhode Island, for their suggestion and assistance in this study.
REFERENCES ...
doi:10.1145/2228360.2228394
dblp:conf/dac/ZhangHY12
fatcat:qkns7bttgnbxzjrk57s2ly2ea4
Adaptation Strategies for Personalized Gait Neuroprosthetics
2021
Frontiers in Neurorobotics
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Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. ...
of interface computing architectures. ...
ACKNOWLEDGMENTS The authors thank the organizers of IEEE/EMBS NER conference in 2021 for the opportunity to hold the mini symposium Adaptation Strategies for Personalized Gait Neuroprosthetics as part ...
doi:10.3389/fnbot.2021.750519
pmid:34975445
pmcid:PMC8716811
fatcat:yhi54olpwjfyzg65niy4vqlddy
Advanced Intelligent Control through Versatile Intelligent Portable Platforms
2020
Sensors
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Deep research and communicating new trends in the design, control and applications of the real time control of intelligent sensors systems using advanced intelligent control methods and techniques is the ...
, adaptive sensor networks, human-robot (H2R) interaction systems and machine-to-machine (M2M) interfaces. ...
The innovative end-to-end pipeline uses deep convolutional neural networks for training real-time accurate models, which can be applied to human-machine interactions on humanoids robots or other intelligent ...
doi:10.3390/s20133644
pmid:32610597
pmcid:PMC7374349
fatcat:pdjc2zwt3nablactkfjculzsr4
Human-Robot Interaction in Rehabilitation and Assistance: a Review
2020
Current Robotics Reports
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routines in terms of activities for assistive systems and exercises for rehabilitation devices to fit each user's need and maximize their effectiveness. ...
Recent Findings The survey on the literature reveals that new efforts are put on utilizing machine learning approaches alongside novel developments in sensing technology to adapt the systems with user ...
Gordleeva et al. proposed a human-robot interface for as lower limb exoskeleton which records and processes multimodal signals collected using a foot motor imagery-based brain-machine interface (BMI) and ...
doi:10.1007/s43154-020-00015-4
fatcat:t6vkv6iic5apxcvt7qvlqwoxjq
Control Systems of Bionic Limbs of the New Generation and Control Systems with EMG Signals of VR and Games and Toys
[chapter]
2018
Biofeedback
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This technology has also found so far mostly theoretical management, but undergoes testing practical, for control of the knee module of a bionic artificial limb of the lower limb. ...
handling and their transmission to the processor where by means of a neural network there is a recognition of a gripper, further data are transferred for control of a bionic hand. ...
neural-control interfaces, there is a fear, and after time there is adaptability. ...
doi:10.5772/intechopen.74794
fatcat:dme6kcj2pncgjjtk6y37hmbpue
Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges
2021
Sensors
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New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. ...
Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. ...
both for lower limb [66] and for upper limb rehabilitation [68] after SCI. ...
doi:10.3390/s21062084
pmid:33809721
pmcid:PMC8002299
fatcat:jv3tjm3w2nh2xhhlymbk7lmm5i
ExoNet Database: Wearable Camera Images of Human Locomotion Environments
[article]
2020
bioRxiv
pre-print
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Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. ...
Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoids and autonomous legged robots. ...
Improving the Performance of a
300
Neural-Machine Interface for Artificial Legs Using Prior Knowledge of Walking Environment.
301
Annual International Conference of the IEEE Engineering in Medicine ...
doi:10.1101/2020.10.23.352054
fatcat:vbuqcbvwpjh6hmj2qla5hy3ewy
Accepted Papers
2021
2021 IEEE URUCON
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Linear Programming Approach for Phasor Measurement Unit Placement Lower Limb Muscles Modeling With Logical Gates for Gait Analysis Instruction Ottavianelli, Martín Hybrid Model of Artificial Neural Network-Cuckoo ...
Real-Time Dehazing System Using the DCP Algorithm Ensemble of Machine Learning Models for an Improved Facial Emotion Recognition Palomares, Ricardo Ocampo, Ernesto Applying Bayesian Networks to Help Physicians ...
doi:10.1109/urucon53396.2021.9647111
fatcat:nscnueqsozci3i3znjobyoeeiy
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