The motor precision of today’s neuroprosthetic devices that use artificial generation of limb motion using Functional Electrical Stimulation (FES) is generally low. We investigate the adoption of natural co-activation strategies as present in antagonistic muscle pairs aiming to improve motor precision produced by FES. In a test in which artificial knee-joint movements were generated, we could improve the smoothness of FES-induced motion by 513% when applying co-activation during the phases in which torque production is switched between muscles – compared to no co-activation. We further demonstrated how the co-activation level influences the joint stiffness in a pendulum test.BMBF, 16SV7069K, Bewegungsfähigkeit und Mobilität wiedererlangen - BeMobi

Klauer, Christian

Ruppel, Mirjana

Schauer, Thomas

Current Directions in Biomedical Engineering

The motor precision of today’s neuroprosthetic devices that use artificial generation of limb motion using Functional Electrical Stimulation (FES) is generally low. We investigate the adoption of natural co-activation strategies as present in antagonistic muscle pairs aiming to improve motor precision produced by FES. In a test in which artificial knee-joint movements were generated, we could improve the smoothness of FES-induced motion by 513% when applying co-activation during the phases in which torque production is switched between muscles – compared to no co-activation. We further demonstrated how the co-activation level influences the joint stiffness in a pendulum test

Ruppel Mirjana

Klauer Christian

Schauer Thomas

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Enhancing the smoothness of joint motion induced by functional electrical stimulation using co-activation strategies

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 Current Directions in Biomedical Engineering 2017; 3(2): 155–159 Mirjana Ruppel, Christian Klauer* and Thomas Schauer Enhancing the smoothness of joint motion induced by functional electrical stimulation using co-activation strategies Abstract: The motor precision of today’s neuroprosthetic devices that use artificial generation of limb motion using Functional Electrical Stimulation (FES) is generally low. We investigate the adoption of natural co-activation strategies as present in antagonistic muscle pairs aiming to improve motor precision produced by FES. In a test in which artificial knee-joint movements were generated, we could improve the smoothness of FES-induced motion by 513% when applying co-activation during the phases in which torque production is switched between muscles – compared to no co-activation. We further demonstrated how the co-activation level influences the joint stiffness in a pendulum test.  Keywords: Antagonistic muscle pairs, Co-activation, FES, Neuroprosthetics  https://doi.org/10.1515/cdbme-2017-0033 1 Introduction Functional Electrical Stimulation (FES) is a well-established technology used to artificially activate muscles by recruiting the muscle’s motor units using electrical pulses. It is often applied to restore motor functions in paralyzed patients in therapy or to support daily-life activities. However, precise motor functions are often difficult to realize since the stimulation-effect is difficult to model and therefore to predict. Hence, commercial devices mainly focus on reliable, most commonly open-loop strategies often using pre-defined stimulation patters applied to a single muscle. In healthy joint-movements, however, motor precision is achieved by a simultaneous contraction of multiple antagonistic muscles. This phenomenon leads to an increase of the mechanical stiffness in the joints and therefore enhanced motor precision and an increased stability of postures [1].   Control of antagonistic muscle pairs with FES is typically performed using a switching strategy that distributes a one-dimensional actuation variable to two activation levels for both muscles (e.g. [2]). Commonly only one muscle is activated at the same time. Co-activations that allow the modulation of the mechanical impedances can also be artificially induced using FES. It is expected that this has a great potential to improve FES-induced motion [3]. Indeed, more than twenty years ago it was demonstrated, that the involvement of co-contractions can significantly increase the performance of control systems that use FES for actuation [2].  In the mid-nineties, Zhou et al. [4] investigated different co-activation strategies under isometric conditions to realize sinusoidal and linear shaped joint torques. Their results show an improved torque tracking performance as the level of co-activation increases.  ______ *Corresponding author: Christian Klauer: Control Systems Group, Technische Universität Berlin, Germany, e-mail: klauer@control.tu-berlin.de Mirjana Ruppel, Thomas Schauer: Control Systems Group, Technische Universität Berlin, Germany Figure 1: The experimental setup as described in Sec. 2. Recruitment ( -) controlled FES is applied to the antagonistic muscle pair formed by the quadriceps and the hamstring muscle. Using a feedback of the evoked EMG, the stimulation intensities    and    are adjusted such that the effects of muscle fatigue are compensated. The knee angle is measured using an inertial sensor attached to the lower leg. Bereitgestellt von | Technische Universität BerlinAngemeldetHeruntergeladen am | 31.08.18 15:16156   As muscle fatigue increases, the level of co-activation is likely to decrease as time progresses for the same stimulation levels. Most commonly, the individual contribution of each involved muscle cannot be estimated as only measurements of the summation of all individual muscle contraction-effects are typically available. Therefore, a reliable generation of a desired level of co-activation is difficult to realize. Thus, a periodically repeated re-calibration of the control system would be required to adapt the generation of co-activation to the current muscle fatigue state. None of the existing systems consider the difficulty of properly maintaining the level of co-activation in relation to muscle fatigue.  In this publication, we investigate the feasibility of co-activations to improve motor precision in the FES-based control of the knee joint-angle. To overcome issues with fatiguing muscles, we use an underlying feedback of the FES-induced muscle activation obtained by evoked electromyography (eEMG) measurements [5]. The closed loop enforces an almost linear muscle behavior and further compensates the effects of muscle fatigue until the control signal (stimulation intensity) saturates. The results described in [5] apply to the shoulder deltoid muscle and we assume that this similarly applies to the quadriceps- and hamstrings muscle.   In a fundamental investigation, we demonstrate in a pendulum test how the joint stiffness increases as the level of co-activation increases. We further compare the approaches already investigated for torque realization in [4] to control the knee joint angle using different approaches that employ co-activation. A significant improvement in motor precision was observed in the two best-performing approaches compared to the simple switching-strategy that does not use co-activation.        2 Experimental set-up As shown in Fig. 1, a healthy subject sits on an elevated surface with his/her leg free to swing. FES is applied to the quadriceps (channel A) and the hamstring (channel B) muscle using a current-controlled stimulator (Rehastim, Hasomed GmbH, Germany) and self-adhesive electrodes (ValuTrode CF4090 (4x9 cm), Axelgaard Manufacturing Co., USA). A stimulation frequency of 25 Hz is used and corresponds to the sampling frequency of the control system (sampling index  ).  The control signals       and             are the normalized pulse charges varying between zero and the maximally tolerable charges       and      . Current amplitude and pulse width are computed using the charge control method as described in [5].  The abduction angle      is measured at 25 Hz by means of an inertial sensor (MPU 9250, InvenSense Inc., San Jose, USA). The orientation is estimated using the approach given in [6]. When FES is active, EMG is measured at 2048 Hz using separate Ag/AgCl electrodes (Ambu Neuroline 720, Ambu A/S, Denmark) and recorded using a signal amplifier embedded into a device combining FES and EMG (RehaMovePro, [7], Hasomed GmbH, Germany). The EMG electrodes are placed outside the stimulation electrodes, as indicated in Fig. 1. By filtering the stimulation evoked EMG, the estimated muscle recruitment levels       and       of the preceding stimulation period are obtained at stimulation frequency as described in detail in [5]. The recruitment controller adjusts the stimulation intensities       and       to produce the desired recruitment levels        and       . The control algorithms are implemented using OpenRTDynamics (www.openrtdynamics.sf.net) considering all realtime critical parts and Scilab to automate calibration procedures. Figure 2: Left: The structure of the co-activation controller manipulating the inputs to both  -controllers (c.f. Fig. 1). Middle: Two options [1a] and [1b] define how the manually adjusted co-activation level is modulated depending on   . Right: The switching strategy [2A]/[2B] realizes the joint angle tracking, while also introducing muscle co-activation for    . Bereitgestellt von | Technische Universität BerlinAngemeldetHeruntergeladen am | 31.08.18 15:163 Methods An open-loop control strategy to control the knee-joint angle   according to a reference    that adjusts the desired recruitment levels        and        is used. Different co-activation methods can be applied.  Because of the underlying recruitment controllers, the level of recruitment    can be assumed to relate linearly to the resulting steady-state angle   :                                        (1) Herein,         and         are the angles obtained when applying the maximal possible recruitment levels         ,         , respectively. These parameters are automatically determined during the calibration process of the  -controllers, as described in . We further assume that the contributions    and    to the joint torque         produced by each muscle   and   is also linearly dependent on the recruitment level       ,       , respectively, as described by:                                 (2) The co-activation torque (amount of counteracting torque) that is expected to modulate mechanical impedances is then defined as follows:                                      .   (3)  Here,   is a scaling factor. The maximal possible co-activation torque is then given by                                                       (4) The used controller is depicted in Fig. 2. Two online-adjustable parameters are provided: A reference angle    and the level of co-activation  . The factors    and    of the linear models (1) and (2) are cancelled by their inverses         and        , respectively. A desired joint angle    is then realized by the switching strategy [2A]/[2B] as outlined in Fig. 2. The factor       allows the respective counteracting muscle to be activated to a desired extent. A further modulation of the co-activation is possible by choosing      . For      the maximal possible co-activation torque         (c.f. Eq. (4)) is obtained because   is scaled by                  . Two options [1a] and [1b] are available: In option [1a] the level of co-activation is constantly applied to the muscles. In option [1b], the co-activation is modulated depending on the reference angle    as illustrated in Fig. 2: Within a range               the level of co-activation is increased to its maximum for      . Otherwise no co-activation is present. This shall lead to an increased joint stiffness during the phases when muscle activation switches occur.  4 Experimental results All tests were performed in a healthy subject (age 23, female) asked to always relax. Prior to the respective experiments, the recruitment controllers were calibrated by the automated procedure described in [5].  4.1 Pendulum test With this experiment, we wanted to investigate the influence of the co-activation level on the knee-joint's stiffness properties. The subject's leg is flexed to approximately    . Then it is released and the swinging process is recorded. This is done for multiple levels of co-activation              and option [1a]. Fig. 3 exemplarily shows the results for     (no co-activation) and       . The duration     of the swinging is determined by the duration the envelope of   takes to enter  % of its initial amplitude. As we see in Fig. 3,     decreases as the level of co-activation increases. 4.2 Motor precision Using the controller described in Sec. 3, a piecewise linear movement in a range                shall be realized. Different parameter-combinations are investigated. For this, we generate the reference    by means of a two-point controller that alternates between linearly inc.-/decreasing    using a given slope        . The slope changes its direction when the measured knee angle   crosses the thresholds          and          . We want to achieve a motion that transitions steadily through the switching of the activation between hamstrings and the quadriceps (     ). Therefore, we tested the following combinations: (A) No co-activation:         , (B) Simultaneous muscle activation:          , (C) Constant co-activation: option [1a]:            , (D)   -modulated co-activation: option [1b],           , (E) Combining (B) and (D): option [1b],             . Bereitgestellt von | Technische Universität BerlinAngemeldetHeruntergeladen am | 31.08.18 15:16158   To evaluate the smoothness during the switching of activation, a linear model was fitted to the measured angles in a range           . The RMS-error between this model and the measured data then yields the degree of smoothness  . Further, ideally, the resulting movements are triangularly shaped. The results are given in Fig. 3.  Variants (A) and (B) result in jerky movements, especially when switching between muscles occurs. Using constant co-activation (C), the smoothness improves strongly. Even better results are obtained in (D) when co-activation is induced when switching occurs. Expectations on a triangular movement were mostly fulfilled in (D) and (E). 5 Conclusion Good results were obtained for (C), (D) and (E), in which the co-activation level ([1a] or [1b]) was activated       . Otherwise performance was low. The best results were obtained for variants (D) and (E). Using (E), we expect a decreased maximal range of movement and a fast progression of muscle fatigue as the level of muscle activation is higher in average. Therefore, we prefer (D). Using (D), motion smoothness could be significantly (by 513%) improved compared to no co-activation. We expect the results to also apply to e.g. the control of the elbow-joint angle and the finger and wrist extension/flexion. These investigations aim as a basis for future neuroprostheses to restore paralyzed functions.  Author’s Statement Research funding: This work was conducted within the research project BeMobil, which is supported by the German Federal Ministry of Research and Education (FKZ 16SV7069K), and the used stimulation electrodes were donated by Axelgaard Manufacturing Co., USA. Conflict of interest: Authors state no conflict of interest. Informed consent: Informed consent has been obtained from all individuals included in this study. Ethical approval: The research related to human use complies with all the relevant national regulations, institutional policies and was performed in accordance with the tenets of the Helsinki Declaration, and has been approved by the ethics committee of Charité Universitätsmedizin Berlin (Number EA2/015/13).. References [1] N. Hogan, “Adaptive control of mechanical impedance by coactivation of antagonist muscles,” IEEE Transactions on Automatic Control, vol. 29, no. 8, pp. 681–690, 1984. [2] W. Durfee, “Task-based methods for evaluating electrically stimulated antagonist muscle controllers,” IEEE Transactions on Biomedical Engineering, vol. 36, pp. 309–321, 1989. [3] E. Burdet, R. Osu, D. W. Franklin, T. E. Milner, and M. Kawato, “The central nervous system stabilizes unstable dynamics by learning optimal impedance,” Nature, vol. 414, no. 6862, pp. 446–449, 2001. [4] B. H. Zhou, R. Baratta, M. Solomonow, L. Olivier, G. Nguyen, and R. D’Ambrosia, “Evaluation of isometric an- tagonist coactivation strategies of electrically stimulated mus- cles,” IEEE transactions on biomedical engineering, vol. 43, no. 2, pp. 150–160, 1996. [5]  . Klauer,  .  errante, E. Ambrosini,  .  hiri,  . D hne, I.  chmehl, A. Pedrocchi, and T.  chauer, “A patient- controlled functional electrical stimulation system for arm weight relief,” Medical Engineering & Physics, 2016. Figure 3: 3.1: The duration     of the swinging process (option [1a] active) decreases with rising co-activation levels as explained in Sec. 4.1. 3.2: Swinging process for 0% co-activation. 3.3: Swinging process for 15% co-activation. 3.4-3.8: (A) no co-activation leads to discontinuities of the resulting angle. 3.5: Variant (B) results in pronounced discontinuities. 3.6: (C) a constant co-activation causes fairly smooth transitions during muscle switching. 3.7: (D) leads to very smooth transitions. 3.8: (E) also causes smooth transitions and further yields a nearly triangular movement. The smoothness   improved by 513% when comparing (A) to (D). Bereitgestellt von | Technische Universität BerlinAngemeldetHeruntergeladen am | 31.08.18 15:16[6] T.  eel and  . Ruppin, “Eliminating the Effect of Magnetic Disturbances on the Inclination Estimates of Inertial Measurement  nits,” in Proc. of IFAC World Congress 2017 (accepted), 2017. M. Valtin, K. Kociemba, C. Behling, B. Kuberski, S. Becker, and T.  chauer, “Rehamovepro: A versatile mobile stimula- tion system for transcutaneous FES applications,” European Journal of Translational Myology, vol. 26, no. 3, 2016.Bereitgestellt von | Technische Universität BerlinAngemeldetHeruntergeladen am | 31.08.18 15:16

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Enhancing the smoothness of joint motion induced by functional electrical stimulation using co-activation strategies

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