Castellini, Claudio

Farina, Dario

Nowak, Markus

Vujaklija, Ivan

Institute of Transport Research:Publications

Highly Intuitive 3-DOF Simultaneous and Proportional Myocontrol ofWrist and HandMarkus Nowak, Ivan Vujaklija, Claudio Castellini, and Dario FarinaAbstract— While simultaneous and proportional activationof multiple degrees of freedom (DOFs) is supported by novelprosthetic hands, there are still no commercial controllers toappropriately enable it. Here, we test a ridge regression basedmyocontrol method in two real-time scenarios: 13 subjects withan extended high-density EMG electrode set (192 channels) and4 subjects with a reduced set of electrodes (16 channels). Ineach scenario, the algorithm was trained on 3 repetitions ofsingle DOF motions (rest, wrist flexion/extension and rotation,and hand closing) and then subjects were asked to reach 24on-screen goals consisting of one-DOF, two-DOF, and three-DOF targets. The results showed that participants were ableto reach all types of targets and that their one-DOF successrate remained high despite the simultaneous control of mul-tiple DoFs (95.9 ± 5.7%). Moreover, the performance did notsignificantly change when reducing the number of electrodes(97.6± 4.5% for 16 channels).I. INTRODUCTIONMulti-articulate prosthetic hands have been commerciallyavailable for over a decade. Even though they featuremultiple motorized degrees of freedom (DOFs), the mostcommonly provided control interface is a switching paradigmleaving users to progressively cycle through preset func-tions [1]. Recently, advanced prosthetic systems have beencontrolled with pattern recognition algorithms [2], [3] capa-ble of directly distinguishing individual gestures by classi-fying electromyographic (EMG) activity of user’s residualmuscles. While this is an important advance, the lack ofsimultaneous functions makes it still far from meeting theexpectations of natural control.Enabling simultaneous control over multiple DOFs wasattempted by extending the pattern recognition approachesto facilitate simultaneous classes [4]. While feasible, thisapproach remained bound to laboratory conditions. To over-come the inherent discrete nature of classifiers, a focus hasbeen put on regression approaches which aim to establisha continuous mapping between the observed EMGs andthe functional domain of prosthetic joints [5]. However,even after demonstrating that a high number of DOFs canbe concurrently controlled in this way in robotic systemsusing high-density EMG (HD-EMG) [6], the most recentThe study was partially funded by the DFG project Tact Hand (CA-1389/1-1) and the ERC Synergy project NaturalBionicS.M. Nowak and C. Castellini are with the German Aerospace Center(DLR), Institute of Robotics and Mechatronics, Wessling, Germany (e-mail:{markus.nowak, claudio.castelini}@dlr.de).I. Vujaklija, is with the Department of ElectricalEngineering and Automation, Aalto University (e-mail:ivan.vujaklija@aalto.fi).D. Farina is with the Department of Bioengineering, Imperial CollegeLondon (e-mail: d.farina@imperial.ac.uk).home-tested prosthetic systems are still bound to only twoDOFs [7].Here, we propose a regression-based approach whichfollows the recommendations on being highly intuitive andrequiring minimal calibration [8] to deliver 3-DOF simul-taneous and proportional control over the wrist and hand(wrist flexion/extension, wrist pronation/supination, and handclosing and rest). The approach was evaluated in real-timeand its robustness was examined by progressive channelreduction.II. MATERIAL AND METHODSA. Control StrategyWe acquired 192 monopolar EMG signals with three8-by-8 ELSCH064NM3 sensor matrices (EMG-USB2+ am-plifier, OTBioelettronica, Italy), which were placed aroundthe circumference of the forearm. A moving average (MAV)filter was applied to the rectified signal to extract the en-velope and a ridge regression (RR) machine learning (ML)method was applied to the EMG envelopes1.B. Experiment DescriptionTo investigate the combined capability of user andML method we recruited 13 able-bodied participants (age27.5± 3). The study was approved by the local ethicscommittee of Imperial College of London and all participantshave given their written consents before participation.The EMG sensor matrices were placed around the partici-pant’s forearm covering the entire circumference. Referenceelectrodes were placed at the wrist, and subjects were seatedcomfortably in front of a PC screen (Figure 1).The subjects performed three repetitions of the aforemen-tioned actions of the wrist and hand at a comfortable level for2s. These 3×6 = 18 recordings of the MAV of all availablechannels were used for training the RR algorithm. Thereafterthey performed 24 randomly presented goal reaching tasks(12 one-DOF, 8 two-DOF and 4 three-DOF goals, equallydistributed with respect to the origin). These tasks accountedfor different levels of activation, e.g. 30% of wrist flexioncombined with 80% of wrist pronation and 0% hand closurein a two-DOF goal. The subject had 20s to attempt and reacheach goal (15% of the normalised work space), and staywithin it for 0.3s.1The EMG was gathered at 2048Hz and treated with a 5th orderButterworth bandpass-filter (20Hz − 500Hz). Features were calculatedfrom the rectified, low-pass filtered signal (2nd order Butterworth at 2Hzcut-off), and averaged across a window of 100 samples with a 3/4 overlap.The regularization parameter in RR was kept at 1.0.Fig. 1: Experimental setup with EMG-USB2+ amplifier, PC screenwith prediction and target stimulus, and participant equipped withthree 8x8 sensor matrices* ****0%20%40%60%80%100%1 2 3TargetDoFsSuccessRateSensorNumberfullreducedFig. 2: Box plot with SR of all subjects, superimposed by a dot plotof the individual subject results. Brackets with asterisks representsignificant difference, brackets without asterisks represent grouping.Of the 13 subjects, four completed an additional roundof the same 24 tasks, but with a reduced number of EMGsensors - 16 of the original 192 sensors were used for trainingthe ML method. The reduced set featured eight electrodepairs (along the muscle fibre direction) evenly distributedaround the widest circumference of the forearm. We did notaim at targeting specific muscle groups.C. Statistical EvaluationFor evaluation purposes we compared the performance ofall 192 vs. the reduced 16 sensors. Also, we have consideredthe difference when dealing with one-DOF, two-DOF andthree-DOF goals. Therefore, we calculated the success rate(SR) per subject across these categories and performeda statistical analysis with the two factors Sensor Number[full, reduced] and TargetDOFs [1, 2, 3]. We used a two-way repeated-measures ANOVA with α = 0.05. Significantinteractions were followed up by a pair-wise comparison withBonferroni correction.III. RESULTSFigure 2 summarizes the results of the user study in termsof SR per category as well as the results of the statisticalanalysis. The factor TargetDOF was highly significant withF (2, 6) = 30.66, p < 10−3, while the factor Sensor Numbershowed no significant influence on the outcome measure withF (1, 3) = 0.00, p = 1.00. The interaction term showedno significance either with F (2, 6) = 2.51, p = 0.16.2A post-hoc of the significant factor TargetDOF revealedthat each level is significantly different from another withp ∈ [0.018, 0.00027].IV. DISCUSSIONResults show that although the participants have trainedthe algorithm only with single actions, they were capableof real-time command of two-DOF and even three-DOFcombined actions. To the best of our knowledge this is thefirst time that such a level of control is achieved usingintuitive control inputs. Although there was a significantperformance degradation when facing more difficult tasks(i.e. two-/three-DOF), this did not impact one-DOF control.Another remarkable finding is the fact that the channelreduction did not have a significant influence on the per-formance. A slight, yet not significant, improvement wasobserved when decreasing the number of EMG channels,which might potentially originate from an increase in signal-to-noise ratio for the reduced set.V. CONCLUSIONHere we demonstrated for the first time a successful real-time myocontrol over 3-DOFs in a concurrent and propor-tional fashion with natural inputs and minimal user andmachine training. We achieved this by relying on a simpleML method trained on only individual DOF data, yet thesystem remained intuitive enough to allow users to achievesimultaneous control by eliciting EMGs of concurrent DOFs.In future, we intend to test this approach with amputeesubjects and physical prosthetic systems.REFERENCES[1] I. Vujaklija, D. Farina, and O. C. Aszmann, “New developments inprosthetic arm systems,” Orthopedic research and reviews, vol. 8, p. 31,2016.[2] COMPLETE CONTROL Handbook, Coapt LLC, Chicago, IL, 2018.[3] Ottobock, “Technology for people 4.0: Ottobock at OTWorld2018,” https://www.ottobock.com/en/newsroom/news/technology-for-people-4-0-ottobock-at-otworld-2018.html, 2018, [Online; Accessed:25-May-2020].[4] M. Ortiz-Catalan, B. Håkansson, and R. Brånemark, “Real-time andsimultaneous control of artificial limbs based on pattern recognitionalgorithms,” IEEE Transactions on Neural Systems and RehabilitationEngineering, vol. 22, no. 4, pp. 756–764, 2014.[5] I. Vujaklija, “Novel control strategies for upper limb prosthetics,” inInternational Conference on NeuroRehabilitation. Springer, 2018, pp.171–174.[6] M. Ison, I. Vujaklija, B. Whitsell, D. Farina, and P. Artemiadis, “Si-multaneous myoelectric control of a robot arm using muscle synergy-inspired inputs from high-density electrode grids,” in 2015 IEEEInternational Conference on Robotics and Automation (ICRA). IEEE,2015, pp. 6469–6474.[7] J. M. Hahne, M. A. Schweisfurth, M. Koppe, and D. Farina, “Si-multaneous control of multiple functions of bionic hand prostheses:Performance and robustness in end users,” Science Robotics, vol. 3,no. 19, p. eaat3630, 2018.[8] C. Castellini, R. M. Bongers, M. Nowak, and C. K. van der Sluis,“Upper-limb prosthetic myocontrol: two recommendations,” Frontiersin neuroscience, vol. 9, p. 496, 2016.2Sphericity and normality of the residuals were tested with Mauchly’stest and with Shapiro-Wilk test, respectively. Furthermore, since repeatedmeasures ANOVA cannot deal with missing data, we additionally performeda multi-level linear analysis, which confirmed the findings.

Highly intuitive 3-DOF simultaneous and proportional myocontrol of wrist and hand

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Highly intuitive 3-DOF simultaneous and proportional myocontrol of wrist and hand

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