Design and Embedded Control of a Soft Elbow Exosuit

Design and Embedded Control of a Soft Elbow Exosuit Domenico Chiaradia1 , Michele Xiloyannis2 , Chris W. Antuvan2 , Antonio Frisoli1 , Lorenzo Masia2

Abstract— The use of soft materials to transmit power to the human body has numerous advantages, amongst which safety and kinematic transparency stand out. In previous work we showed that a tethered fabric-based exosuit for the elbow joint, driven by an electric motor through a Bowden cable transmission, reduces the muscular effort associated with flexion movements by working in parallel with its wearer’s muscles. We herein propose a refined design of the suit and present an untethered control architecture for gravity compensation and motion-intention detection. The architecture comprises four interconnected modules for power management, low-level motor control and high-level signal processing and data streaming. The controller uses a silicone stretch sensor and a miniature load cell, integrated in the fabric frame, to estimate and minimise the torque that its user needs to exert to perform a movement. We show that the device relieves its wearer from an average of 77% of the total moment required to sustain and move a light weight, with a consequent average reduction in muscular effort of 64.5%.

I. I NTRODUCTION Soft robots are thriving in the field of human motion assistance, paving the way towards a future where robotic devices for restoring and augmenting human performance will become an ordinary part of our daily lives. Among the reasons for such growing interest is the ability of soft structures to handle interactions in an intrinsically safe and gentle manner [1]: This property makes them particularly suitable to reinforce the human body without affecting the smoothness of natural movements. Moreover, using fabric and elastomers instead of rigid links allows to lower the weight, dimension and power consumption of the device, thus making it more practical for use on a daily basis. Soft exoskeletons, baptized “exosuits”, have indeed yielded promising results for both augmenting performance and compensating for neuromuscular deficiencies. The absence of a rigid structure in exosuits avoids the problem of misalignment between the robot’s and the user’s joints, which notoriously causes discomfort and upsets the natural kinematics of human movements [2]. This is achieved by either using pneumatic soft actuators directly mounted on the joints or transmitting power from a proximally-located DC motor with flexible Bowden cables. Exosuits have been proven to be successful in reducing the metabolic cost of human walking in both stroke patients [3] and healthy subjects [4], in lowering the muscular effort 1 Scuola Superiore Sant’Anna, TeCIP Institute, PERCRO Laboratory, Pisa, Italy (email: [email protected], [email protected]) 2 School of Mechanical and Aerospace Engineering, Nanyang Technological University (email: [email protected], [email protected])

required for upper limb movements [5], [6], [7] and sit-tostand transitions [8] and in aiding extension and flexion of the fingers in stroke and spinal cord injury patients [9], [10]. In [11], [12] we presented the design of a first prototype of a soft exosuit for assistance of the elbow joint: the device featured a fabric component, wrapped around the human arm and connected to two antagonistic Bowden cables, and a cable-driving unit carried in a backpack. Rotation of a DC motor in one direction would pull either cable, applying a flexing/extending moment on the human joint. The suit was controlled using a hierarchical scheme to decode the wearer’s intention and compensate for the non-linearities the transmission [13]. Despite being designed for portability, the system was still tethered to a PC and a real-time workstation. In this paper we present the design of a refined prototype of the elbow exosuit and describe an embedded system based on the FlexSEA control architecture [14]. The system, comprises a control unit that the wearer can carry in a backpack, and a fabric-based frame worn around the limb that can be easily donned on everyday clothes. We then propose a controller to compensate for the effect of gravity and understand the user’s intention of moving. To do so we use a silicone stretch sensor integrated in the fabric and a miniature load cell attached on the tendons of the actuator. The controller is based on the same rationale of the one presented in [5], yet it is here tweaked to run on an embedded system, hence simplicity and robustness are prioritised. We evaluate the efficacy of the controller on healthy subjects by assessing the change in human torque and the change in the biceps brachii muscle’s level of activation with and without the assistive device. This positive effect is shown to be maintained even for relatively fast movements. A final analysis on the power consumption of the device highlights the limitations of the proposed system and points towards the need of an optimised and more efficient design. II. S UIT ’ S D ESIGN The exosuit comprises an actuation stage, driving a pair of tendons, and a wearable component. The actuation stage is located proximally, i.e. worn as a backpack, and transmits power to the suit via Bowden cables. The tendon-driving unit (shown in Figure 1.c) comprises the following components: a brushless motor (Maxon EC-i, ∅ 40 mm, 70 W) coupled to a planetary gearhead (reduction of 28:1), whose angular position is sensed by a quadrature encoder (Maxon Encoder MR, 1000 CPR) and a spool around which two cables are coiled in opposite directions, so that rotation of the motor in one direction causes retraction of one

Bowden sheaths

(a)

proximal chest strap

(b) tendon stretch sensor actuation & control

distal forearm strap load cell

(c)

gearhead spool

encoder brushless motor

ball bearings

casing

Fig. 1: The soft exosuit for assistance of the elbow comprises a wearable component and an actuation stage. (a-b) The frame of the exosuit is composed of a proximal component, strapped around the chest and arm, and a distal one, anchored to the forearm using a Boa lacing system. A silicone stretch sensor (StretchSense) and a miniature load cell (Futek, LLB210) are integrated in the suit to monitor the joint angle and interaction forces, respectively. (c) The actuator comprises a brushless motor (Maxon EC-i, 70W), combined with a 28:1 reduction planetary gearhead and an incremental encoder (Maxon MR,1000 CPR) that drives a spool around which two tendons (superelastic NiTi wire, ∅ 0.5 mm) are wrapped in opposite direction. A plastic casing and 3 ball bearings keep the tendons from derailing when they’re slack.

tendon and releases the other, in an antagonistic fashion. The two tendons, made of superelastic NiTi wire, are routed from the actuator unit on the harness to the elbow joint through a Bowden sheath. The whole mechanism is enclosed in a 3D-printed case and carried in a plastic backpack with the electronics and power supply. The exosuit for the elbow (shown in Figure 1.a-b) is made of two straps: A proximal one anchored to the arm and chest and a distal one tightened around the forearm with a Boa lacing mechanism. The proximal part of the suit was designed by modifying a commercially available passive orthosis (MASTER03, Reh4mat). It is made of a 3-layered fabric assembly: an external layer used to attach hard components (buckles and webbing strips), an intermediate ethylene-vinyl acetate (EVA) foam that cushions high loads and avoids peaks of pressure on the skin and an internal 3D polyamid structure that provides high air permeability and moisture absorption. Additionally the arm band is lined with a silicone pattern at the interface with the skin to reduce migration of the fabric upon the application of forces. Load paths, i.e. the directions along which forces are transmitted through the suit, need to be as stiff as possible to maximize force-transmission efficiency. They are made of webbing, i.e. nylon fibers woven in a flat strip, which is virtually inextensible and able to support high loads. Buckles attached on the webbing strips, moreover, allow to slightly adjust the suit to different sizes and to pre-tension it around the user’s body. The proximal component of the suit covers the shoulder and encircles the chest, this allows it to rely on forces perpendicular to the body to reduce migration of the fabric

frame when it’s tensioned. Shear forces, as a matter of fact, should be avoided as they are not only source of discomfort for the user but also of unreliable transmission. The distal strap of the suit, anchored to the forearm, is made of an external network of webbing bands, intertwined at a 45 deg angle in a “chinese finger trap” fashion. This arrangement causes it to tighten around the forearm as it is tensioned along its main axis, thus reducing slippage. A middle layer made of a a thin layer of flexible PVC plastic sheet allows a more uniform distribution of forces on the body and a 5 mm of high density EVA foam interfaces it with the skin to increase comfort. The strap can be pre-tensioned around the forearm using a Boa lacing system. Finally, to route the tendons along the load paths we sewed 3D printed components on the webbing network on each sides of the joint. These serve as artificial ligaments, anchoring the tendons to the body; the distal anchor points houses a subminiature load-cell (Futek, LLB210, 220 N) to sense the tension on the tendons (detail in Figure 1.a). A silicone capacitive stretch sensor (Stretchsense, New Zealand), is attached to an elastic band aligned with the elbow joint on the frontal plane, so as to stretch proportionally to the joint angle (detail in Figure 1.a). III. C ONTROLLER ’ S D ESIGN A. Embedded Architecture The proposed controller is implemented on a modular and scalable system architecture called FlexSEA, depicted in Figure 2, and positioned in a plastic casing, worn as a backpack. The FlexSEA controllers allow faster prototyping and easy scalability compared to commercially available motor drivers [15].

Wi-Fi BeagleBone Black

FlexSEA Execute

FlexSEA Manage

Actuation

SPI

RS485 Maxon EC-i 70W

Signal Processing Wireless Data Logging Storage (µSD Card) Storage & Communication

Load Cell Stretch Sensor Incremental Encoder

22.2V LiPo Battery (1500mAh) Power Supply

Data Acquisition

FlexSEA Battery

Fig. 2: Diagram showing the embedded modules used to control the soft exosuit. The FlexSEA controllers, design specifically for wearable robotic applications, are modular boards that enable fast prototyping and easy scalability [15]. A commercially-available single board computer (Beaglebone Black Wireless) allows to wirelessly interface the exosuit with a portable PC, for data logging and tuning of control parameters.

The system is composed of a low-level layer devoted to motor control and power management and a high-level layer for data storage, streaming and high-level control strategy settings. The low-level layer includes the FlexSEA Execute and the FlexSEA Battery boards. The Execute board drives the brushless motor at a frequency of 10 kHz, taking care of motor commutation, data acquisition (motor encoder, stretch sensor and load cell) and it runs the gravity compensation control proposed in this work at a frequency of 1 kHz. The entire system is powered by a 22.2 V LiPo Battery, interfaced via a FlexSEA Battery board, that controls the battery’s voltage level, preventing damages due to under-voltages and limiting the output current. The high-level layer is composed by the FlexSEA Manage and the BeagleBone Black Wireless (BBBW) boards. We use the FlexSEA Manage simply as as a communication bridge between the Execute and the BBBW for this application, yet the Manage allows to connect up to 4 Execute boards, hence scaling the architecture for controlling more Degrees Of Freedom (DOFs) very efficiently. The BBBW has two main functions: wireless streaming of data to a portable PC and management of control parameters. B. Control Strategy We propose a control strategy designed to assist users in elbow flexion/extension movements whilst compensating for the forearm’s weight. This is achieved by combining a component that delivers an assistive torque dependent on the forearm’s mass with a simple intention-detection procedure. The rationale behind the design of such controller consists in estimating the torque applied by the exosuit on the user’s joint and the torque due to gravity. If these two match, the suit will hold the position, if they don’t, it will assist the wearer accordingly. An additional velocity-dependent contribution facilitates initiation of movement.

Such framework is shown in Figure 3 and consists of a low-level closed-loop velocity control and a high-level assistance estimator. The objective of the high-level controller is to evaluate the amount of assistive torque τˆa requested by the user and consequently provide an active reference motion for the elbow as a desired velocity θ˙m,d . The closed-loop velocity control computes the control signal u and sends it to the brushless motor that, in turn, delivers an assistive torque τa to the human-exosuit system. We chose to use a low-level velocity control instead of a traditional current control loop because of the lower sensitivity of the former to non-linear phenomena such as friction and backlash, amply present in the planetary gearhead, Bowden cables and soft frame of the suit. It has been shown that for a current controller to be performing in a soft exosuit, is requires an accurate and adaptive compensation algorithm, capable of accounting for configuration-dependent and non-stationary phenomena in the transmission [5]. In this work, we trade the accuracy of a compensated current controller for the simplicity and θ˙m Speed Controller θ˙m,d Kg

Ks

u

Motor Driver

τa

Motor

τˆa Assistive τ Estimator

f

τˆg

φe

φˆ˙e

τh

Gravity Estimator d dt

Fig. 3: Control schematics for gravity compensation and subject’s intention detection. The tension on the flexor tendon and the elbow’s position are used to estimate the torque applied by the exosuit and the torque on the joint caused by gravity. If these two match, the suit will hold the position, if they don’t, it will assist the wearer accordingly. An additional velocity-dependent contribution facilitates initiation of movement.

where τ is the resulting torque of the human action τh and the assistive torque from the exosuit τa , while φe , φ˙e and φ¨e denote the elbow angular position, velocity, and acceleration respectively; be is the viscous damping constant; and g = 9.81m/s2 represents the gravity constant. For slow and smooth movement (quasi-static condition, i.e. mglc sin φe  2 2 ¨ ˙ 3 ml φe + be φe is verified), Equation 5 can be simplified as follow: τ = τh + τa ≈ mglc sin φe (6)

h f (φe ) he (φe )

l lc b R

a φe mg

Fig. 4: Model of the human elbow and tendon routing. The elbow joint is modelled as a revolute joint on the sagittal plane, the geometry of the tendon routing can be used to find the extension functions and estimate the assistive torque.

robustness of a velocity controller. 1) Assistive Torque Estimator: The assistive torque is estimated from the tension measured by load cell on the suit’s tendons. Figure 4 shows a dynamic model of the human arm, depicting the cable routing and a descriptive extension function h(φe ), which maps the cables’ displacement to the joint angle φe . The flexor and extensor cables have, respectively, an extension function h f (φe ) and he (φe ) defined as:  a φ  p e h f (φe ) = 2 a2 + b2 cos tan−1 + − 2b (1) b 2 he (φe ) = Rφe

(2)

where a is half of the width of the arm, b is the distance from the joint centre of rotation to the anchor points, R is the radius of the elbow joint, and φe is the joint angle. From the two extension functions h(φe ) we can compute the relationship between the cable tensions f , recorded by the load cells and the torque τba delivered to the elbow. By defining the matrix J as: J(φe ) =

∂ hT ∂ φe

(φe )

(3)

 T where h = h f (φe ) he (φe ) represents the vector of cable extensions. The estimated assistive torque delivered at the joint,τba , is obtained by the equation: τba = J(φe ) f

(4)

where f is the measured cable tensions obtained by the load cell. This model assumes that the position of the anchor points is fixed. It neglects deformation of the fabric and soft tissues upon the application of a force from the tendons. 2) Desired Velocity Computation: At this level the assistive torque, computed by the high-level controller, is converted into a desired velocity of the joint, θ˙m,d . In order to do this we starts with a an inverse dynamic model of the elbow, used to estimate the torque required to follow the measured trajectory: 2 τ = τh + τa = ml 2 φ¨e + be φ˙e + mglc sin φe 3

(5)

The torque deriving from the human muscles τh can be obtained from (6), i.e., τbh ≈ mglc sin φe − τba

(7)

where τba is the estimated assistive torque obtained from equation (4). τba is then converted into a velocity reference for the elbow using a gain, Kg . θ˙m,d = Kg (τˆa − τˆg ) + Ks φˆ˙e .

(8)

From the Equation it can be seen that θ˙m,d is given by the sum of two terms, one deriving from the torque error τˆa − τˆg and a second one given by a positive damping contribution, proportional to the measured elbow velocity. The positive damping contribution, shown in Figure 3 as Ks φˆ˙e , is commonly used to compensate for stick-slip friction phenomena, similarly, it here facilitates initiation of movement, making the intention-decoding more transparent. θ˙m,d is tracked by a feedback PID controller using the motor’s encoder, running on the Execute board at a frequency of 10 kHz. IV. VALIDATION A. Experiments To evaluate the designed exosuit and the proposed controller, 2 healthy subjects (males, age 27.5 ± 0.7 years, no reported injuries or impairments in the upper limbs) were asked to move perform repetitive movements with and without the assistance of the exosuit. The aim of the experiments was to investigate the change in muscular activation of the biceps brachii when lifting a light load with the exosuit. During the experiment, subjects were instructed to flex/extend their elbow, following a sinusoidal motion showed as reference on a screen, in order to standardize the range of motion and speed, while holding a 1.25 kg load in their hand. The experiments were conducted in two distinct phases, randomised to avoid potential order effects: with the exosuit (“exo” condition) and without it (“no exo” condition). Between the two phases the subjects rested for at least 5 minutes to avoid muscle fatigue. For each phase we used three maximum speeds to evaluate the performance of the assistive control with increasing speeds of movement. The speeds were chosen to be 20, 30 and 60% of the average elbow velocity in ADLs: 20 deg/s, 25 deg/s and 50 deg/s respectively [16]. Subjects performed 10 repetitions with

τa

τh

τh

τtotal

τtotal

(a)

(b)

Fig. 5: Moments on the elbow without and with the exosuit. (a) Four consecutive elbow flexion/extension movements and estimated moment on the joint required to perform them; without the exosuit all of the required torque, τtotal , comes from the subject. (b) When assisted by the exosuit, part of the load is borne by the device: the assistive torque, τa , in blue, and the human torque,τh , in grey, work in parallel to provide the total required moment.

and 10 without the exosuit for each velocity, following a reference trajectory of the joint shown on a screen. This allowed us to standardise the movement, thus permitting comparisons among subjects. Muscular effort was estimated from the Root Mean Square (RMS) of the Electromyography (EMG) of the main muscle involved in performing elbow flexion movements, i.e. the biceps brachii. We positioned the electrodes according to the SENIAM standards [17] . The raw EMG was acquired using an external sensor that was not part of the embedded system, i.e. Trigno wireless EMG sensors (Delsys Inc.). It was acquired by a Quanser Quarc real-time workstation running at 1kHz refresh rate, then pre-processed in Matlab Simulink using a full-wave rectification and low-pass filtered by a second-order Butterworth filter with a 8 Hz cut-off frequency. The real-time post-processed signal was then sent to the embedded system in order to collect synchronized data. All the collected data were streamed from the BBBW board to a host PC sharing an ad-hoc wireless LAN. 1) Sensors’ Calibration: The stretch sensor varies its capacitance linearly with its elongation; modelling the elbow as a revolute joint and assuming that no slippage occurs during movement, we can expect the capacitance of the sensor to be linearly dependent on the bending angle φe . Yet, because of the variability in arm size among subjects, we require a subject-specific calibration procedure. At the beginning of each experiment we recorded the sensor’s output at 0 deg and 90 deg (the ground truth was measured with a goniometer) and used a first-order polynomial function to map it to the elbow angle. An amplifier (CAV444, Analog Microelectronics) transduced the capacitance to a voltage that was read by the ADC of the Execute board. Similarly, a linear characteristic was used to calibrate the load cell, relating the voltage to the cable’s tension. This procedure was only applied once for the load cell.

The measured angular position φe and the measured force f were conditioned with a first-order Butterworth lowpass filter with a cut-off frequency of 25 Hz before further processing.

Fig. 6: Average moments acting on the elbow at three different velocities of movement, averaged over two subjects. The exosuit relieves the subject from nearly 77% of the total moment required to perform the movement. This ratio does not change for higher velocities.

B. Results Figure 5 shows a comparison of the estimated moments at the elbow, for one subject, between the “exo” and the “no exo” condition. Moments were calculated using the inverse dynamic model described in Equation 5. In the “no exo” condition (Figure 5.a) the whole moment, τtotal , shown in gray, is exerted by the subject, while in the “exo” condition (Figure 5.b) the exosuit works in parallel with the subject’s own muscles, providing an assistive torque,

over repetitions and subjects just below 20%.

−64.5%

Fig. 7: Bar plots showing the average and standard error of the change in muscular activity between the “exo” and “no exo” case in the biceps brachii muscle. Muscular effort was measured in terms of the Electromyography’s (EMG) Root Mean Square (RMS) and shown in percentage of the ”no exo” case. The reduction in effort is stable even for higher velocities, with an average value of 64.5%.

τa , shown in light blue, estimated from the load cell readings. The remaining torque, shown in light grey, is the one exerted by the wearer. For the computation of whole moment τtotal the parameters m, l and be were empirically measured and tuned on each individual subject following the rules listed in [18]. Averaging the moments at the joint over repetitions and subjects yields the results shown in Figure 6. The contribution of the soft exosuit, accounting for nearly 77% of the total torque required to move the elbow, is stably maintained from slow (20 deg/s) to high (50 deg/s) speeds of movement. These results are consistent with the analysis of the level of activation of the biceps brachii muscle, responsible for flexing the elbow. Figure 7 shows the percentage reduction in muscular activity between the “exo” and the “no exo” condition, where muscular activity is measured as the Root Mean Square (RMS) of the muscle’s EMG level. Similarly to the results found for the joint torques (Figure 6), the decline in muscular activation, amounting to a mean of 64.5%, is maintained for increasing velocities. Figure 8 shows the typical appearance of the electrical power consumed by the motor and the output power delivered at the elbow joint for a flexion/extension movement of the elbow. The black shaded area shows the mechanical power delivered by the exosuit at the elbow, computed as Pelbow = τa φ˙e . This quantity reaches a maximum of 4 W and has a symmetrical behaviour in the ascending and descending phases. The electrical power consumed by the motor, computed as Pmotor = IV , with I and V being the current and voltage on the motor’s windings, is shown in grey. It reaches a peak around 17 W just before motion inversion, it then drops to zero and shows a small negative contribution in the descending phase, induced by the counter-electromotive force due to gravity. The big mismatch between Pelbow and Pmotor highlights the low efficiency of the device, an average

Fig. 8: Power transfer characteristic of the soft exosuit. Typical curve of the electrical power consumption of the motor (grey) and output mechanical power at the elbow joint (black) for a flexion/extension movement of the elbow. The disparity between the two curves highlights the low efficiency of the device, just below 20%.

V. D ISCUSSION Soft exosuits, adopting clothing-like materials instead of rigid frames, are not bounded by the structural complexity and portability constrains of their hard counterparts. Trading limited force output and control accuracy for comfort, wearability and portability, soft robots have the potential to succeed in becoming a ubiquitous part or our daily lives. We had previously proposed the design and control of a soft exosuit for assistance of the elbow and tested a hierarchical controller that decoded human motion intention and compensated for backlash and friction in the transmission. The device showed encouraging results, yet was still tethered to a stationary workstation. In this study we presented a refined version of the exosuit and proposed an embedded architecture running a control paradigm based on a similar rationale. The architecture can be scaled to control more DOFs by simply adding more motor drivers. The controller compensates for the gravitational force acting on the forearm whilst following the subject’s movement. This is achieved by a combination of a torquedependent term and a velocity dependent term that facilitates initiation of movement. Testing the device on two healthy subjects showed that the exosuit delivers an average of 77% of the total moment required to perform the movements and that this result is maintained for speeds up to 50 deg /s, hence fast enough for most activities of daily living. The reduction in muscular effort deriving from the exosuit’s assistance follows the same trend: wearing the exosuit reduces the activation of the biceps muscle by an average of 64.5% compared with the “no exo” condition, suggesting that the device could be used to delay the onset of fatigue in a healthy individual or to assist movements of subjects

suffering from neuromuscular impairments such as, among others, stroke and spinal cord injury. Comparing this with our previous results, we obtained a greater reduction in muscular effort than with our previous design, which yielded an average 48% drop in the amplitude of the biceps’ EMG signal. A bigger sample size is needed to test this significantly. Despite these reassuring results, it must be mentioned that the limitations of our soft exosuit are not few. First and foremost our controller assumes that the arm is aligned with gravity and does not account for movements of the shoulder. More sensors would be needed to make the model valid for a more general case. The controller’s performance, furthermore, is limited by the reliability of the elbow’s position sensor: being integrated in the fabric, the silicone stretch sensor migrates as the suit is repeatedly loaded and unloaded; this leads to low repeatability and need for re-calibration (at the beginning of each trial). The model’s performance is dependent on empiricallychosen parameters (Ks , Kg ) which must be tuned for each subject. An on-board algorithm to adjust these variables so as to optimise the assistance level, such as the one proposed in [19], would make the device more practical and efficient. The analysis on the power consumed by the motor and delivered at the joint, shown in Figure 8, highlighted the low efficiency of the device. For a system designed for portability this is an issue that cannot be neglected. Various factors come into play here: Friction in the Bowden cables, backlash and stiffness of the suit all contribute to losses in force and motion between the actuator and the joint. Novel data-driven approaches to measure and optimise its design can help: Yandell et al. [20] have measured the power absorption/release on the anchor points of a soft asssitive device, highlighting the importance of considering the dynamics of the human-robot interface in the design process; similarly, Quinlivan et al. [21] have measured and compared the suit-human series stiffness of interfaces of different geometry and material, pointing out how this affects the power transfer between the actuator and the wearer. Quantitative approaches to drive the design of a wearable device are not only important for improving its efficiency: measuring its the level of comfort could suggest design features to improve its ergonomics. Modelling and predicting skin damage due to the interface with an assistive robot, for example, is a topic of great concern, all the more so for users with sensory disabilities due to nerve damage or neuromuscular impairment. Some work has been done towards this end by measuring the pressure distribution on the human skin [21], yet we are still missing a standardised framework to address this need. R EFERENCES [1] A. T. Asbeck, S. M. M. De Rossi, I. Galiana, Y. Ding, and C. J. Walsh, “Stronger, smarter, softer: Next-generation wearable robots,” IEEE Robot. Autom. Mag., vol. 21, no. 4, pp. 22–33, dec 2014. [2] N. Jarrass´e and G. Morel, “Connecting a human limb to an exoskeleton,” IEEE Trans. Robot., vol. 28, no. 3, pp. 697–709, jun 2012.

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