SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics

Ceseracciu, Elena; Reggiani, Monica; Sawacha, Z.; Sartori, Massimo; Spolaor, Fabiola; Cobelli, Claudio; Pagello, Enrico

doi:10.1109/roman.2010.5598664

Cited by 31 publications

(31 citation statements)

References 16 publications

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“…This will allow estimating the force generated by the Illiacus and Psoas muscles thus increasing the hip flexion-extension moment prediction accuracy. We are also planning to integrate in the NMS model our previous work on pattern recognition [18] to extrapolate more information from the EMG signals. Finally, a study on a larger population will be conducted to further validate our methodology.…”

Section: Discussionmentioning

confidence: 99%

A neuromusculoskeletal model of the human lower limb: Towards EMG-driven actuation of multiple joints in powered orthoses

Sartori

Reggiani

Lloyd

et al. 2011

2011 IEEE International Conference on Rehabilitation Robotics

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This paper presents a novel neuromusculoskeletal (NMS) model of the human lower limb that uses the electromyo-graphic (EMG) signals from 16 muscles to estimate forces generated by 34 musculotendon actuators and the resulting joint moments at the hip, knee and ankle joints during varied contractile conditions. Our proposed methodology allows overcoming limitations on force computation shown by currently available NMS models, which constrain the operation of muscles to satisfy joint moments about one single degree of freedom (DOF) only (i.e. knee flexion-extension). The design of advanced human machine interfaces can benefit from the application of our proposed multi-DOF NMS model. The better estimates of the human internal state it provides with respect to single-DOF NMS models, will allow designing more intuitive human-machine interfaces for the simultaneous EMG-driven actuation of multiple joints in lower limb powered orthoses.

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Section: Discussionmentioning

confidence: 99%

A neuromusculoskeletal model of the human lower limb: Towards EMG-driven actuation of multiple joints in powered orthoses

Sartori

Reggiani

Lloyd

et al. 2011

2011 IEEE International Conference on Rehabilitation Robotics

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“…This pre-processing stage consists of applying different classes of filters, such as a notch filter to eliminate the power line noise. The noise generated by the skin layers is at frequencies above 500 Hz [1,2,[8][9][10][11][12][13][14][15][16][17][18][19][20][21] and at those below 10 Hz [1,9,11,12,14,18,20,22]. Some authors consider that there is noise also in higher frequencies, and thus they use filters that cancel up to 20 Hz [2,6,8,13,[15][16][17]19,21,23,24].…”

Section: Signal Acquisitionmentioning

confidence: 99%

“…Hz 10-500 20-450 20-500 25-500 [1] X [9] X [11] X [26] X [23] X [12] X [13] X [2] X [14] X [15] X [16] X [24] X [17] X [18] X [19] X [6] X [20] X [21] X [8] X Another issue found in the study of myoelectric signals is the frequency at which EMG signals should be sampled-a high frequency could give excess noise, and a lower one could lose a vast amount of information. The sampled frequency most commonly used is 1 kHz [1,9,10,12,13,18,19,[27][28][29]. Other authors (e.g., [2,11,22,26,30,31]) use a higher frequency of 1.5, 2, 3, 4, or 10 kHz.…”

Section: Referencementioning

confidence: 99%

“…Sampling Frequency [15,[32][33][34] 500 Hz [1,9,10,[12][13][14][17][18][19][20][21][27][28][29] 1 kHz [2] 1.5 kHz [7,8,11,16,30,[35][36][37] 2 kHz [22] 3 kHz [6,25,26] 4 kHz [31] 10 kHz…”

Section: Referencementioning

confidence: 99%

“…[25] 1 [16,17,20,26] 2 [24,31] 3 [1,[8][9][10]13,21,23,29,35,39,[44][45][46] 4 [19,36,40,41] 6 [2,7,11,15,22,30,32,34] 8 [37] 12 [33] 14 [12,14] 16 [47] 22 Table 4. Electrodes type and place of electrode placement body.…”

Section: Number Of Channelsmentioning

confidence: 99%

See 2 more Smart Citations

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

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This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

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Intent Recognition in a Powered Lower Limb Prosthesis Using Time History Information

et al. 2013

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New computerized and powered lower limb prostheses are being developed that enable amputees to perform multiple locomotion modes. However, current lower limb prosthesis controllers are not capable of transitioning these devices automatically and seamlessly between locomotion modes such as level-ground walking, stairs and slopes. The focus of this study was to evaluate different intent recognition interfaces, which if configured properly, may be capable of providing more natural transitions between locomotion modes. Intent recognition can be accomplished using a multitude of different signals from mechanical sensors on the prosthesis. Since these signals are non-stationary over any given stride, and gait is cyclical, time history information may improve locomotion mode recognition. The authors propose a dynamic Bayesian network classification strategy to incorporate prior sensor information over the gait cycle with current sensor information. Six transfemoral amputees performed locomotion circuits comprising level-ground walking and ascending/descending stairs and ramps using a powered knee and ankle prosthesis. Using time history reduced steady-state misclassifications by over half (p < 0.01), when compared to strategies that did not use time history, without reducing intent recognition performance during transitions. These results suggest that including time history information across the gait cycle can enhance locomotion mode intent recognition performance.

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SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics

Cited by 31 publications

References 16 publications

A neuromusculoskeletal model of the human lower limb: Towards EMG-driven actuation of multiple joints in powered orthoses

A neuromusculoskeletal model of the human lower limb: Towards EMG-driven actuation of multiple joints in powered orthoses

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Intent Recognition in a Powered Lower Limb Prosthesis Using Time History Information

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