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# Enhancing the Usability of Upper-Limb Assistive Robots with a Variable Admittance Controller

Manzano Maxime, Gallois Maël, Guégan Sylvain, Le Breton Ronan, Devigne

Louise, Babel Marie

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## ► To cite this version:

Manzano Maxime, Gallois Maël, Guégan Sylvain, Le Breton Ronan, Devigne Louise, et al. Enhancing the Usability of Upper-Limb Assistive Robots with a Variable Admittance Controller. JCJC 2025 - Colloque Jeunes Chercheurs, Jeunes Chercheuses 2025, Institut Fédératif de Recherche sur les Aides Techniques pour personnes Handicapées (IFRATH), Jun 2025, Aubervilliers (Campus Condorcet), France. pp.14-19. hal-05356901

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Distributed under a Creative Commons Attribution 4.0 International License
# Enhancing the Usability of Upper-Limb Assistive Robots with a Variable Admittance Controller

**Manzano Maxime**

Univ Rennes, INSA Rennes, Inria,
CNRS, IRISA - UMR 6074
Rennes, France

ORCID: 0009-0001-7535-2663

**Gallois Maël**

Univ Rennes, INSA Rennes, Inria,
CNRS, IRISA - UMR 6074
Rennes, France

mael.gallois@inria.fr

**Guégan Sylvain**

Univ Rennes, INSA Rennes,
LGCGM
Rennes, France

ORCID: 0000-0002-8797-6078

**Le Breton Ronan**

Univ Rennes, INSA Rennes,
LGCGM
Rennes, France

ORCID: 0000-0002-2663-9983

**Devigne Louise**

Univ Rennes, INSA Rennes, Inria,
CNRS, IRISA - UMR 6074
Rennes, France

ORCID: 0000-0002-9109-545X

**Babel Marie**

Univ Rennes, INSA Rennes, Inria,
CNRS, IRISA - UMR 6074
Rennes, France

ORCID: 0000-0001-6425-389X

**Abstract**—Assistive robots are crucial to foster independence and social participation of individuals with mobility impairments. In particular, upper-limb assistive robots enable individuals with severely impaired motor function to perform activities of daily living autonomously. However, despite their potential, these devices struggle to achieve widespread adoption, mainly due to significant usability limitations. Indeed, most active devices rely on admittance controllers to interpret user intention into motion, but their fixed-parameter configuration is hard to optimize across situations, often resulting in either instabilities or overly conservative settings that compromises user experience. To alleviate this issue, variable admittance controllers with adjustable parameters have been proposed. However, only few studies have applied to upper-limb wearable robots, and only for rehabilitation purposes rather than daily life assistance.

This paper introduces then a novel variable admittance con-
troller that ensures stable interaction while effectively assisting
upper-limb movements. An experimental evaluation involving
18 participants without upper-limb impairment shows that the
proposed variable admittance controller enhances assistance,
usability and acceptance compared to the conventional fixed
parameter approach.

***Keywords***—Assistive robots, exoskeletons, variable admittance, upper-limb, daily activities.

# I. INTRODUCTION

Upper-limb (UL) assistive robots have emerged as promis-
ing tools for assisting people in activities of daily life (ADLs),
offering potential benefits in terms of mobility and inde-
pendence for individuals with severe motor impairments [1].
However, their adoption remains limited due to challenges
related to their usability, particularly as they fail to ensure
intuitive interaction [2]. Improving the usability of these
devices requires then to properly detect the user intentions
[3]. For individuals with residual motor abilities, measuring
and interpreting natural physiological or biomechanical signals
helps encourage both cognitive and physical engagement in
movement, while also allowing for the detection of intended
actions through intuitive cues. A common approach to this is

the integration of force sensors at the human-robot interfaces,
offering a rich and non-invasive source of information on
the user intended motions, as the sensors are seamlessly
embedded within the robot's structure [4]. Afterward, this
detected intention is used to compute desired velocities for
the actuator to perform motion in line with the user intent.
This step is commonly achieved by an admittance controller
that acts as a mass-damper system to compute the exoskeleton
desired motion from the user forces [5]. Despite its widespread
use, admittance control faces limitations in adapting to the
dynamic human-exoskeleton system [6]. Indeed, this controller
often relies on constant parameters, which fails to capture the
complexity of human motor control, as individuals adjust their
impedance by co-activating muscles in response to different
tasks [7]. Thus, one common side effect of admittance-based
control is instability which occurs when the user increases their
stiffness. This effect is even amplified as the human central
nervous system tends to increase stiffness when interacting
with an unstable robotic system [8]. This is particularly
observed for new users that often experience apprehension
[9]. This side effect hinders the adoption of exoskeleton
technologies, as stability is crucial for assistive devices [10].

In response to these challenges, variable (or adaptive) admit-
tance control has been proposed in the literature which adapts
the admittance parameters to the human's varying impedance
[11]. Several strategies have been explored to adjust admit-
tance parameters dynamically, such as reducing damping for
free movements while increasing it during more precise tasks
[12]. Other strategies rely on a comparison between the user's
intention and the current direction of motion to facilitate
acceleration or deceleration [13]. Although these approaches
support task execution, they do not address the stabilization
of human-robot interaction. Previous works have proposed
variable admittance controllers for stable interaction [8], [14],
but they rely on detecting the onset of instability, which
limits user acceptance as they do not prevent instability from
<figure><img src="image_4.png" /><figcaption>Figure 1. Architecture of the assistive controller. The user intention is detected with force sensors. The proposed variable admittance translates this intention to desired actuator velocities, which are followed by a PI controller.</figcaption></figure>

occurring. Previous works have proposed variable admittance controllers to improve interaction stability, but they only attenuate instabilities rather than completely eliminate them [8], [14]. As a result, users can still perceive these instabilities, leading to a diminished experience. In addition, most of these studies focused on human-robot interaction at the end-effector level. Few variable admittance controllers have been applied to upper-limb wearable robots, and only in the context of rehabilitation, where, unlike in daily life assistance, the user is required to follow a predefined trajectory [15], [16].

This paper proposes then a variable admittance controller designed to stabilize human-robot interaction while providing effective assistance for ADLs. Sec. II details the design of the controller and its working principle. An experiment is then presented in Sec. III, where 18 participants without impairments performed pick-and-place tasks under various conditions. The results are presented and discussed in Sec. IV, and Sec. V concludes the paper.

## II. METHODS

### A. Generic architecture

The architecture of the assistive framework, directly derived from previous work [17], is depicted in Fig. 1. Three stages allow to properly follow the user intent. First, force sensors at the physical interfaces allow to estimate the user intent. To do so, passive UL forces $F_0$ are subtracted from the raw force measurements $F$ to retrieve the user voluntary forces $F_{vol}$. In this work, these passive forces are estimated by interpolating between measurements recorded at different poses $q$ while the user keeps the UL relaxed (see Sec. III-B). Then, user voluntary torques $\tau_{vol}$ are computed from these voluntary forces with the UL Jacobian matrix $J$ and supplied to the proposed variable admittance controller. Lastly, a low-level PI controller ensures that the exoskeleton actuators follow properly the desired velocity $\dot{q}_d$.

### B. Variable admittance controller

For a given joint $q$, a generic admittance controller computes the desired joint velocity $\dot{q}_d$ from user's voluntary torque as

$$ \dot{q}_d + T\ddot{q}_d = K\tau_{vol}, \quad (1) $$

with $T$ the time constant, and $K$ the gain of this controller. In the case of a classical admittance controller, both parameters

<figure><img src="image_5.png" /><figcaption>Figure 2. Variable admittance gain function $K(\tau_{vol}, \dot{q})$ with respect to the user voluntary torque $\tau_{vol}$ for different joint velocities $\dot{q}$ (Eq. 2). Parameters are set to $K_0 = 0.24\text{rad/sNm}$, $P = 4.7\text{W}$ and $\tau_0 = 3\text{Nm}$.</figcaption></figure>

are constant. To address the related instability issues, we propose to continuously vary the gain $K$ depending on both the user's voluntary torque and the current joint velocity $\dot{q}$ so that

$$ K(\tau_{vol}, \dot{q}) = K_0 e^{-\frac{\tau_{vol} + \text{sign}(\dot{q})\tau_0}{P}}, \quad (2) $$

with $K_0$ the zero-velocity gain, $\tau_0$ the torque offset and $P$ the scaling parameter. Fig. 2 shows this gain function with the parameters tuned for shoulder flexion joint (see Sec. III-A). This gain function is chosen to facilitate the triggering and stopping of movement, while ensuring stable interactions during motion. Indeed, if the joint is stationary (i.e. $\dot{q} = 0$), $K$ is set relatively high, allowing the user to trigger motion with low torque. This gain then evolves if $\dot{q} \neq 0$ as follows: if the user applies a torque in the direction of motion, $K$ is reduced to attenuate acceleration and thus stabilize motion, whereas if the torque is opposed to the current movement, $K$ is increased to facilitate deceleration. In addition, to keep a predictable and consistent behavior, the time constant $T$ is kept constant.

In the following, this controller is compared to a classical admittance controller with a fixed gain $K = K_0$.

## III. EXPERIMENTAL ASSESSMENT

An experiment approved by the local Operational Committee for the assessment of Legal and Ethical risks (OCELER, Notice n°2024-40) involving 18 right-handed participants without UL impairments has been conducted to evaluate the proposed variable admittance controller. The experimental protocol follows literature recommendations on UL functions assessment [18]. The exoskeleton design and the tuning of its control parameters (Tab. II) were guided by clinicians from the Pôle Saint Hélier rehabilitation center (Rennes, France).

### A. Experimental conditions

In this experiment, a right UL exoskeleton prototype mounted on a fixed wheelchair is used to implement the proposed variable admittance controller. Participants were asked to perform pick-and-place tasks (Fig. 3(a)) under 3 conditions:

(VA) assisted by the proposed Variable Admittance controller;

(FA) assisted by a standard Fixed-parameters Admittance controller;

(F) in Free motions without the exoskeleton.
<figure><img src="image_6.png" /><figcaption>(a) Pictures of the experimental setup, showing a participant with the exoskeleton during a pick-and-place task.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(b) Kinematic diagram of the human-exoskeleton system. Actuated and passive joints as well as force sensors are shown.</figcaption></figure>

<caption>Figure 3. Experimental setup.</caption>

For practical reasons, condition F is always executed last, but VA and FA are randomly performed. Under each condition, the participant performs 3 repetitions of 5 pick-and-place tasks using a 100g 3D-printed object with a cylindrical handle (3.5 cm in diameter) and a flat base (5.5 cm in diameter). All tasks begin and end in a natural resting pose in the sagittal plane, with upper-arm vertical and forearm horizontal, making a 90° elbow angle. 4 of these tasks involve grasping the object at the Base (B), Top-Left (TL) or Top-Right (TR) target and releasing it onto one of these targets. A task mimicking drinking is also included: the participant grasps the object on target B, reaches and stabilizes for 3 seconds in a drinking pose and places the object back on B. The subtasks involved in each task are detailed in Tab. I.

The exoskeleton prototype assists shoulder abduction, shoulder flexion and elbow flexion. It is actuated by Maxon BLDC motors (EC45Flat, 1:169 and 1:100 reduction ratio respectively at shoulder and elbow). The exoskeleton pose is denoted $q = [q_{sa}, q_{sf}, q_e]$, where $q_{sa}, q_{sf}$, and $q_e$ correspond to the shoulder abduction, shoulder flexion, and elbow flexion angles respectively (Fig. 3(b)). The origin pose $q = [0, 0, 0]$ corresponds to the UL in frontal elevation. Shoulder internal

<table><caption>Table I</caption><thead><tr><th>Task</th><th>Base 2<br>Top-Right<br>(B2TR)</th><th>Base 2<br>Top-Left<br>(B2TL)</th><th>Top-Right<br>2 Base<br>(TR2B)</th><th>Top-Left<br>2 Base<br>(TL2B)</th><th>Drink</th></tr></thead><tbody><tr><td>Sub-task 1</td><td>Rest → B</td><td>Rest → B</td><td>Rest → TR</td><td>Rest → TL</td><td>Rest → B</td></tr><tr><td>Sub-task 2</td><td>B → TR</td><td>B → TL</td><td>TR → B</td><td>TL → B</td><td>B → Mouth (3sec)</td></tr><tr><td>Sub-task 3</td><td>TR → Rest</td><td>TL → Rest</td><td>B → Rest</td><td>B → Rest</td><td>Mouth → B</td></tr><tr><td>Sub-task 4</td><td>N.A.</td><td>N.A.</td><td>N.A.</td><td>N.A.</td><td>B → Rest</td></tr></tbody></table>

PICK-AND-PLACE TASKS PERFORMED DURING THE EXPERIMENT. ALL TASKS, EXCEPT FOR THE "DRINK" TASK, ARE DEPICTED FROM THE PARTICIPANT'S POINT OF VIEW, WHILE THE "DRINK" TASK IS DEPICTED FROM THE SAGITTAL PERSPECTIVE.

rotation is allowed by a passive revolute joint. The participant's upper-arm and forearm are attached to 3D-printed orthoses with foam and hook-and-loop fasteners. With this fastening solution, the forearm orthosis allows slight pronation-supination movements. Although the exoskeleton limb lengths and shoulder center of rotation are adjustable, misalignments between the participant and exoskeleton joints are unavoidable [19] and have been identified as a prominent source of hazards [20]. Thus, both orthoses are mounted on two passive joints: a slider joint parallel to the user's limb and a revolute joint perpendicular to it. The user intention is measured using three-axis force sensors (NF742, Naturoll) attached to the orthoses. This provides force measurements aligned with the user's upper-arm and forearm.

The VA and FA controllers use the same low-level PI speed controller. Their admittance parameters, set according to clinician feedback, are shown in Tab. II. $K_0$ and $P$ are defined as vectors corresponding to the shoulder abduction, shoulder flexion, and elbow flexion parameters respectively. To ensure a fair comparison between the two controllers, the FA and VA gains are adjusted so that their task execution times are similar. Specifically, since the VA gain is reduced once motion begins, using the same $K_0$ for both controllers would result in a lower average gain for the VA, causing slower motion. Thus, $K_0$ is set lower for the FA controller than for VA.

A motion capture system is used to track the trajectories of the user, the exoskeleton and the object. It consists of 9 IR-cameras (Miqus, Qualisys) tracking the position of reflective markers at a 300Hz frequency. The pelvis, trunk and right UL of the participant are tracked with markers set on the following bony landmarks defined in [21], [22]: ASIS (2), PSIS (2), PX, IJ, AC (left and right), EL, EM, RS and US. To better track the user trunk, markers are added on vertebrae C7 and T5, and one is added on the MCP joint of the middle finger to track the right hand. In addition, the muscular activities of 6

<table><caption>Table II</caption><thead><tr><th>Control strategy</th><th>K<sub>0</sub>(rad/(sNm))</th><th>T(s)</th><th>P(W)</th><th>τ<sub>0</sub>(Nm)</th></tr></thead><tbody><tr><td>Variable Admittance (VA)</td><td>[0.2 0.24 0.25]</td><td>0.2</td><td>[4 4.7 4.9]</td><td>3</td></tr><tr><td>Fixed Admittance (FA)</td><td>[0.1 0.12 0.13]</td><td>0.2</td><td>N.A.</td><td>N.A.</td></tr></tbody></table>

PARAMETERS FOR BOTH ADMITTANCE CONTROLLERS.
UL muscles are recorded with wireless EMG sensors (Trigno, Delsys). These are the Trapezius, Posterior Deltoid, Medial Deltoid, Anterior Deltoid, Biceps brachii and Triceps brachii.

## B. Experimental protocol

The participant is first equipped with reflective markers on the aforementioned bony landmarks, and EMG sensors are placed according to the SENIAM recommendations [23]. Once the EMG signals have been checked, the participant sits in the wheelchair and the exoskeleton is installed on their right UL. Then, the exoskeleton is adjusted to align its joints as closely as possible with those of the user.

As mentioned in Sec. II, a calibration is required to extract user's voluntary forces. It is performed by measuring the interaction forces $F$ for different UL poses while the participant is completely relaxed. To limit calibration time, measurements are taken only in the sagittal plane, holding the abduction joint angle at $q_{sa} = 0$. During the trials, the passive interaction forces $F_0$ are assumed to follow the direction of gravity and are estimated through interpolation between the measured values. Consequently, if the arm abducts ($q_{sa} \neq 0$), the forces are rotated since the force sensors are no longer aligned with the sagittal plane.

Then the 3 targets are installed (Fig. 3(a)). To get comparable range of motions between participants, the target positions are adapted to each individual. To do so, the exoskeleton is set at predefined joint angles, and the targets are positioned according to the resulting user's hand position. The B target is centered in front of the user while $q = [0°, -30°, 30°]$ such that the forearm is horizontal. The TL and TR targets are set with $q = [0°, 40°, 0°]$ and respectively aligned with the left and right user's shoulder. The targets are 3D-printed and form a flat disk of 10cm diameter. These experimental conditions have been chosen to make the pick-and-place tasks challenging due to the relatively small and slippery targets and high TL and TR positions, requiring precise movements to release the object without dropping it.

Then, each participant performs the tasks, assisted by one control strategy, then the other. For both controllers, they perform free movements before starting the trial to familiarize themselves with the assistance. They have as much time as required, but each task is performed at least once. When ready, the 3 repetitions of the 5 tasks are completed in a random order provided by the experimenter. For each task, participants wait for a signal to start moving. The experimenter gives this signal verbally and simultaneously records it using a push-button. Afterwards, they subjectively assess their perceived usability and acceptance of the assistive device by answering the QUEAD questionnaire from the literature [24]. Lastly, the participants perform the tasks without the exoskeleton ("F" condition). Personal feedback is collected after the session.

## C. Data processing and analysis

To analyse collected data, as suggested by [24], dividing the tasks into motor primitives allows more accurate analyses of

experimental results. Thus, the five tasks (Tab. I) are divided and gathered into 8 motor primitives as follows:

* Reach-And-Grasp at Base (RAGB): from resting pose to object grasp at B target. This refers to subtask 1 of tasks B2TR, B2TL and Drink;

* Transport And Release Top (TART): from object grasp at B target to object release on TL or TR target. This refers to subtask 2 of tasks B2TR and B2TL;

* Reach-And-Grasp at Top (RAGT): from resting pose to object grasp at TL or TR target. This refers to subtask 1 of tasks TR2B and TL2B;

* Base To Mouth (B2M): this only refers to subtask 2 of the Drink task, from object grasp on B target to stabilizing at the mouth;

* Transport And Release Base (TARB): from object grasp at TL or TR target to object release on target B. This refers to subtask 2 of tasks TR2B and TL2B;

* Mouth To base (M2B): this only refers to subtask 3 of the Drink task, from stabilizing at the mouth to object release on B target;

* REposition From Top (REFT): from object release at TL or TR target to reaching back the rest pose;

* REposition From Base (REFB): from object release at B target to reaching back the rest pose.

Each motor primitive time interval is defined by the time between two successive actions: start (when the push button is pressed), grasp (when the object begins to move), drink (when the object is stabilized in front of the mouth), release (when the object stops moving), and reposition (when the participant returns to the rest pose).

Qualisys Track Manager (QTM) software is used to post-process the motion capture data to fill gaps and label markers. Then, OpenSim software is used to estimate the user's joint trajectories during all trials. An osteoarticular model from the literature [25] is scaled to each participant and joint angles are estimated using OpenSim's Inverse Kinematics (IK) tool. This process allows to compute the participant's hand trajectory. This trajectory and its first two derivatives are then computed and filtered in Matlab software with a 0.1s robust local regression filter ("loess") as shown in [26]. Lastly, the log dimensionless jerk is computed to evaluate and compare motion smoothness between the three tested conditions [26].

Muscular activity measurements are post-treated following literature recommendations [27]. The smooth rectified envelopes (SRE) of EMG signals are computed by applying a moving root-mean-square filter with a 0.1s window. These envelopes are used to compute the mean effort index (MEI) to assess the participant's muscular effort required to perform the tasks. For a given motor primitive $mp$, MEI is computed as

$$ MEI_{mp} = \frac{1}{6} \sum_{m=1}^{6} \frac{\overline{SRE}_{m,mp}}{\max SRE_{m,mp,F}}, \quad (3) $$

with $m$ the muscle index, $mp$ the motor primitive index and $F$ refers to the "Free motions" condition. $\overline{SRE}_{m,mp}$ is the mean value of the SRE of muscle $m$ during motor primitive
<figure><img src="image_8.png" /><figcaption>Figure 4. Completion time results for each motor primitive under the three conditions. Motor primitives involving UL elevation are colored in green whereas purple encodes downward motions.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 5. Motion smoothness results: log dimensionless jerk for each motor primitive under the three conditions. Lower value means smoother motion. Motor primitives involving UL elevation are colored in green whereas purple encodes downward motions.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure 6. Muscular effort results: MEI for each motor primitive under the three conditions. Motor primitives involving UL elevation are colored in green whereas purple encodes downward motions.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Figure 7. Qualitative evaluation results for both VA and FA controllers from the QUEAD questionnaire. High score means positive assessment.</figcaption></figure>

mp. Therefore, the MEI represents the average muscle activity of all recorded muscles during the motor primitive *mp*, normalized by the peak activity observed in this motor primitive during free movement.

## IV. RESULTS AND DISCUSSION

First of all, all participants successfully completed the five tasks under the three conditions, and both controllers achieved similar completion times, showing that they are effectively comparable (Fig. 4). However, in line with clinicians' recommendations, the controller gains were set to achieve moderate-speed movements, according to the needs of the target population. Therefore, task execution takes longer when using the exoskeleton (FA or VA) than during free movement (F). Focusing on motion smoothness (Fig. 5 Free motion than with the exoskeleton, regardless of the controller used. However, the FA controller resulted in lower log dimensionless jerk, suggesting slightly smoother movements compared to VA. This difference can be attributed to participants exerting non-constant torques, even when their intentions were coherent with the ongoing movement, causing variations in VA gain and thus slight fluctuations in joint velocities.

Regarding muscular effort (Fig. 6), the exoskeleton successfully assisted participants with both VA and FA controllers, as their MEI is reduced for almost all motor primitives compared to free motions. However, higher MEIs are observed with both controllers during repositioning from any target (REFT and REFB). This can be explained by an increase in Posterior Deltoid activity during shoulder extension, as participants needed to push against the exoskeleton to return to the resting position, whereas in free motion, they only have to control the free fall of their UL. Comparing both conditions with the exoskeleton, the proposed VA controller outperforms the FA

controller in reach-and-grasp (RAGB, RAGT) and mouth-to-base (M2B) motor primitives, while achieving similar MEIs in the other ones. In addition, only the VA controller reduces the MEI during M2B compared to free motion. These results show that the proposed VA controller induces better interaction, as it shows greater reduction in MEI compared to the FA controller. Particularly, VA appears to better assist extension motions from the head as observed in the M2B motor primitive, which is a commonly challenging task for individuals with UL impairments [28].

The subjective assessment of both controllers show an overall preference for the VA controller (Fig. 7). Although motions were globally less smooth with the VA controller (Fig. 5), participants felt more comfortable and experienced more positive emotions when using it. This preference is likely due to greater confidence in the device, as the VA controller responds more cautiously, even when participants exert high voluntary torques. Furthermore, this controller demonstrated better usability as reflected in the QUEAD results in terms of perceived ease of use and usefulness.

In addition to these metrics, the subjective comments from participants revealed notable variability in their experiences, due to several factors. First, some participants had to exert a constant effort to maintain static pose, typically observed around the mouth, which explains the variability in MEIs during B2M motor primitive (see Fig. 6). This effect is probably due to calibration issues. Moreover, this variability could stem from a mismatch between applied gains and individual motor abilities, suggesting that a more personalized tuning process of the controller could enhance human-robot interaction. Indeed, some participants experienced uncontrolled movements (i.e., instability) with the FA controller and reported fewer jerky motions with VA which might have been more apparent using alternative metrics to log dimensionless jerk. Interestingly,
some participants felt more agility with the FA controller because of its lack of speed limitation, while others reported a sense of rigidity with it. In contrast, some participants struggled to notice any substantial difference between the two controllers. Overall, many participants appreciated the ease of movement enabled by the VA controller for UL elevation tasks, though some reported difficulties with arm extension.

These findings are promising and highlight several avenues for enhancing the usability and acceptance of UL exoskeletons. Improving the intention detection calibration process could lead to more accurate intention recognition. Additionally, the lack of a universally optimal gain setting suggests that an individualized gain adaptation procedure could significantly improve human-robot interaction. Finally, next trials involving people with UL impairments will be conducted to assess the suitability of the proposed strategy for target users.

## V. CONCLUSION

In this paper, a novel variable admittance (VA) controller for assistive upper-limb (UL) exoskeletons has been proposed and evaluated. The VA controller is designed to enhance the usability and acceptance of UL assistive devices by providing more cautious and stable motions while effectively assisting the UL. Its performances have been assessed in an experiment involving 18 participants without UL impairments, showing promising results. Overall, the VA controller offered better assistance, and participants reported enhanced usability and acceptance compared to a classical fixed-parameter admittance controller. However, significant variability among participants suggests that further improvements could be achieved through personalized assistance tailored to each user's motor functions.

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