A portable, self-contained neuroprosthetic hand with deep learning-based finger control

@article{Nguyen2021APS,
  title={A portable, self-contained neuroprosthetic hand with deep learning-based finger control},
  author={Anh Tuan Nguyen and Markus W. Drealan and Diu Khue Luu and Ming Jiang and Jian Xu and Jonathan Cheng and Qi Zhao and Edward W. Keefer and Zhi Yang},
  journal={Journal of Neural Engineering},
  year={2021},
  volume={18}
}
Objective. Deep learning-based neural decoders have emerged as the prominent approach to enable dexterous and intuitive control of neuroprosthetic hands. Yet few studies have materialized the use of deep learning in clinical settings due to its high computational requirements. Approach. Recent advancements of edge computing devices bring the potential to alleviate this problem. Here we present the implementation of a neuroprosthetic hand with embedded deep learning-based control. The neural… 

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References

SHOWING 1-10 OF 45 REFERENCES

A bioelectric neural interface towards intuitive prosthetic control for amputees

It is demonstrated that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF) and is intuitive as it directly maps complex prosthesis movements to the patient’s true intention.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods, and show that several factors can be fundamental for the analysis of sEMG data.

The SmartHand transradial prosthesis

The SmartHand holds the promise to be experimentally fitted on transradial amputees and employed as a bi-directional instrument for investigating -during realistic experiments- different interfaces, control and feedback strategies in neuro-engineering studies.

A Deep Learning Framework for Decoding Motor Imagery Tasks of the Same Hand Using EEG Signals

This study presents a novel three-stage framework for decoding MI tasks associated with the same hand for able-bodied and transradial amputated subjects and demonstrates the efficacy of the proposed framework.

Inexpensive and Portable System for Dexterous High-Density Myoelectric Control of Multiarticulate Prostheses

This work developed an inexpensive and portable EMG control system by integrating low-cost microcontrollers with a six-channel surface EMG (sEMG) acquisition device that performs satisfactorily and highlights the practicality and efficiency of the modified Kalman filter for dexterous EMG-based control.

Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees

A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary EMG signals and to simultaneously control movements of a dexterous artificial hand and statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees.

Deep Learning Movement Intent Decoders Trained With Dataset Aggregation for Prosthetic Limb Control

The short-term and long-term performances of MLP- and CNN-based decoders trained with DAgger demonstrated their potential to provide more accurate and naturalistic control of prosthetic hands than alternate approaches.

Decoding EEG and LFP signals using deep learning: heading TrueNorth

Merging deep learning and TrueNorth technologies for real-time analysis of brain-activity data at the point of sensing will create the next generation of wearables at the intersection of neurobionics and artificial intelligence.

A recurrent neural network for closed-loop intracortical brain-machine interface decoders.

The ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during a standard center-out reach task using a cortical brain–machine interface (BMI) in a closed loop is explored.

Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

This paper reviews the nerve-machine interface modalities with more focus on peripheral nerve interfaces, which are responsible for provision of sensory feedback and proposes a hybrid interface technique for achieving better selectivity and long-term stability using the available nerve interfacing techniques.