Long term stability of surface EMG pattern classification for prosthetic control

Amsüss, Sebastian; Paredes, Liliana; Rudigkeit, Nina; Gramlich, Bernhard; Herrmann, Michael; Farina, Dario

doi:10.1109/embc.2013.6610327

Cited by 52 publications

(47 citation statements)

References 16 publications

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“…Results show that training on the first 5 sessions caused mean errors higher than 20%, while training on the latest 5 or all previous sessions lowered the mean error at 10-12%. In [21], 5 subjects performed 8 gestures over 5 days and intra-day accuracy was evaluated by random 5-fold cross validation, while inter-day accuracy was evaluated training with a whole day's session and testing on all the others. In this case, the authors observed that the classification performance decreased monotonically on average by 4.1% a day.…”

Section: Related Workmentioning

confidence: 99%

Exploring Arm Posture and Temporal Variability in Myoelectric Hand Gesture Recognition

Milosevic¹,

Farella²,

Benaui

2018

2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

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Hand gesture recognition based on myoelectric (EMG) signals is an innovative approach for the development of intuitive interaction devices, ranging from poliarticulated prosthetic hands to intuitive robot and mobile interfaces. Their study and development in controlled environments provides promising results, but effective real-world adoption is still limited due to reliability problems, such as motion artifacts and arm posture, temporal variability and issues caused by the re-positioning of sensors at each use. In this work, we present an EMG dataset collected with the aim to explore postural and temporal variability in the recognition of arm gestures. Its collection of gestures executed in 4 arm postures over 8 days allows to evaluate the impact of such variability on classification performance. We implemented and tested Stateof-the-Art (SoA) recognition approaches analyzing the impact of different training strategies. Moreover, we compared the computational and memory requirements of the considered algorithms, providing an additional evaluation criteria useful for real-time implementation. Results show a decrease in the recognition of inter-posture and inter-day gestures up to 20%. The provided dataset will allow further exploration of such effects and the development of effective training and recognition strategies.

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Section: Related Workmentioning

confidence: 99%

Exploring Arm Posture and Temporal Variability in Myoelectric Hand Gesture Recognition

Milosevic¹,

Farella²,

Benaui

2018

2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

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“…In [28], three force levels were measured (30%, 60% and 90% of the maximum long term voluntary contraction) for only intact-limbed subjects. All force levels were included in the training and testing.…”

Section: Introductionmentioning

confidence: 99%

“…This calls for a new method to control the force in measurements with amputees rather than relying on traditional methods from the literature. Previous research also necessitated training the classifiers with features from all anticipated force levels that the subject may exert during real-time testing [13], [26], [28], [30]. However, such a scheme has not been fully explored with different feature extraction methods while collecting the EMG signals from amputees.…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance Against Force Variation of EMG Controlled Multifunctional Upper-Limb Prostheses for Transradial Amputees

Al-Timemy

Khushaba

Bugmann

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

249

182

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We investigate the problem of achieving robust control of hand prostheses by the electromyogram (EMG) of transradial amputees in the presence of variable force levels, as these variations can have a substantial impact on the robustness of the control of the prostheses. We also propose a novel set of features that aim at reducing the impact of force level variations on the prosthesis controlled by amputees. These features characterize the EMG activity by means of the orientation between a set of spectral moments descriptors extracted from the EMG signal and a nonlinearly mapped version of it. At the same time, our feature extraction method processes the EMG signals directly from the time-domain to reduce computational cost. The performance of the proposed features is tested on EMG data collected from nine transradial amputees performing six classes of movements each with three force levels. Our results indicate that the proposed features can achieve significant reductions in classification error rates in comparison to other well-known feature extraction methods, achieving improvements of ≈ 6% to 8% in the average classification performance across all subjects and force levels, when training with all forces.

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“…Several factors could generate non-stationarities in the EMG signals that fed the system limiting a robust performance. The most common problems are: limitations of EMG signal acquisition process [34][35][36][37], arm positioning [38,39], electrode shifting [39][40][41], skin conditions [25], fatigue [42] or time degradation [43]. These factors affected the reliability of modern prosthesis control methods over time and conditions of use.…”

Section: Introductionmentioning

confidence: 99%

Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control

et al. 2020

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New upper limb prostheses controllers are continuously being proposed in the literature. However, most of the prostheses commonly used in the real world are based on very old basic controllers. One reason to explain this reluctance to change is the lack of robustness. Traditional controllers have been validated by many users and years, so the introduction of a new controller paradigm requires a lot of strong evidence of a robust behavior. In this work, we approach the robustness against donning/doffing and arm position for recently proposed linear filter adaptive controllers based on myoelectric signals. The adaptive approach allows to introduce some feedback in a natural way in real time in the human-machine collaboration, so it is not so sensitive to input signals changes due to donning/doffing and arm movements. The average completion rate and path efficiency obtained for eight able-bodied subjects donning/doffing five times in four days is 95.83% and 84.19%, respectively, and for four participants using different arm positions is 93.84% and 88.77%, with no statistically significant difference in the results obtained for the different conditions. All these characteristics make the adaptive linear regression a potential candidate for future real world prostheses controllers.

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Long term stability of surface EMG pattern classification for prosthetic control

Cited by 52 publications

References 16 publications

Exploring Arm Posture and Temporal Variability in Myoelectric Hand Gesture Recognition

Exploring Arm Posture and Temporal Variability in Myoelectric Hand Gesture Recognition

Improving the Performance Against Force Variation of EMG Controlled Multifunctional Upper-Limb Prostheses for Transradial Amputees

Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control

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