A Transparent 3-DoF Assistive Device for Upper-Limb Tremor and Ataxia Compensation: Design and Experimental Validation

Thomas Dobosz; Veronika Hofmann; Ahmed Barhoma; Ann-Kathrin Henke; Johannes A. Gaus; Hans Mees; Winfried Ilg; Isabell Wochner

cs.ROOct 22, 2026

A Transparent 3-DoF Assistive Device for Upper-Limb Tremor and Ataxia Compensation: Design and Experimental Validation

Authors: Thomas Dobosz, Veronika Hofmann, Ahmed Barhoma, Ann-Kathrin Henke, Johannes A. Gaus, Hans Mees, Winfried Ilg, Isabell Wochner,

Organizations: Institute of Industrial Manufacturing and Management, University of Stuttgart, Germany · Biomechatronic Systems, Fraunhofer IPA, Stuttgart, Germany · Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience, University of Tubingen, Germany · Center for Bionic Intelligence Tubingen Stuttgart (BITS), Germany ·

Abstract

Assistive devices for patients with neurodegenerative movement disorders must combine high mechanical transparency with the ability to generate corrective interaction forces. Achieving both properties simultaneously remains challenging. This paper presents the completed iAssistADL platform, a 3-DoF assistive device for future tremor and ataxia compensation during activities of daily living. The system employs a transparency-oriented mechanical architecture with all actuators located at the base, resulting in low moving mass, decoupled kinematics, and high mechanical transparency. To compensate for friction-induced transparency limitations along the vertical axis, a hybrid control strategy is proposed that combines open-loop force rendering in the horizontal plane with closed-loop force control in the vertical direction. The platform is experimentally evaluated through transparency measurements and static and dynamic force-rendering experiments at representative configurations. Results demonstrate resistive torques below 0.1 Nm in the horizontal plane, end-effector forces of up to approximately 10 N throughout the workspace, and accurate horizontal force rendering up to 4 Hz. While the vertical axis remains limited by friction-induced dynamics at higher frequencies, the proposed system successfully combines high transparency with controlled force generation, establishing a promising hardware platform for future tremor and ataxia compensation.

Figures & tables

Fig. 1 : User interacting with the iAssistADL platform. The device applies assistive forces to the forearm while preserving natural upper-limb motion.

Fig. 2 : iAssist prototype along with the power supply and real-time computer. Prototype end-effector connected to a 6-D force sensor on a rigid fixture.

Fig. 3 : Schematic of the assistive device with three active DoF. A prismatic joint along the z-axis (blue) actuated via a scissor mechanism, whose motion is constrained by the ball-spline mechanism, a first rotational joint along the ball-spline axis (red), and a second rotational joint bilaterally actuated using cables routed through the scissor mechanism (green). A passive spring mechanism (bottom left) is used to balance the weight of the arm. With joint angles

\theta_{1},\theta_{2}

\theta_{3}

and motor torques

\tau_{1}

\tau_{2}

\tau_{3}

. Adopted from [ 18 ]

					Force capability
Point	$(x,y,z)$ [cm]	$\theta_{1}$	$\theta_{2}$	$\theta_{3}$	$f_{z,\max}$ [N]	Guar. $\\|f_{xy}\\|$ [N]	Max $\\|f_{xy}\\|$ [N]	Anisotropy	$\det J_{xy}$
$P_{1}$ grasp bottle	$(0,50,30)$	$33.75^{\circ}$	$146.25^{\circ}$	$30.00^{\circ}$	$10.5$	$9.89$	$18.2$	$1.84$	$0.187$
$P_{2}$ fill glass	$(-10,60,25)$	$52.00^{\circ}$	$146.93^{\circ}$	$24.62^{\circ}$	$10.0$	$9.89$	$15.0$	$1.51$	$0.202$
$P_{3}$ to mouth	$(-10,30,30)$	$39.00^{\circ}$	$177.86^{\circ}$	$30.00^{\circ}$	$10.5$	$9.89$	$28.8$	$2.91$	$0.133$

TABLE I : Operating points, inverse-kinematic solution, and force-capability analysis. The guaranteed omnidirectional planar force is the shoulder-torque–limited lower bound

\min(a_{1},a_{2})

and is configuration-independent : it holds over the entire reachable workspace, not only at

P_{1}

–

P_{3}

. The pose-dependent quantities are the vertical capability

f_{z,\max}

, the maximum planar force (parallelogram diagonal), the anisotropy (max/guaranteed), and the Jacobian manipulability

\det J_{xy}

Fig. 4 : Test positions with glass (G) and bottle (B). F = Front, B = Bottom, L = Left, R = Right.

Metric	$F_{x}$	$F_{y}$	$F_{z}$
Rise time $t_{r}$ (10–90 %)	$16$ ms	$15$ ms	$3.04$ s
Overshoot	$33\%$	$33\%$	$28\%$
Settling time	$1.35$ s	$1.24$ s	$6.95$ s
Steady-state error	$-0.04$ N	$0.08$ N	$-0.12$ N
(relative)	$2.1\%$	$5.5\%$	$-16\%$

TABLE II : Step-response metrics (mean over all cases).

$f$	Metric	$F_{x}$	$F_{y}$	$F_{z}$
$2$ Hz	$A_{r}$	$0.91$	$0.92$	$0.98$
	$\Delta t$ (ms)	$142$	$155$	$191$
	RMSE ϕ (N)	$0.36$	$0.40$	$1.24$
$4$ Hz	$A_{r}$	$0.95$	$0.97$	$1.87$
	$\Delta t$ (ms)	$127$	$124$	$176$
	RMSE ϕ (N)	$0.68$	$0.57$	$3.37$

TABLE III : Sinusoidal tracking (mean): amplitude ratio

A_{r}

, transport delay

\Delta t

, phase-compensated RMSE.

Fig. 5 : Open-loop step response of the

F_{x}

axis. Reference (dashed) versus measured force (solid); shaded region is the

\pm 5\%

settling band.

Fig. 6 : Stick–slip limit cycle on the vertical axis for the

F_{z}=5

N step reference at pose P3. The commanded control torque (top) ramps under integral action while the ball-spline carriage is stuck, breaks away and overshoots on slip, then re-sticks at a new offset, producing a sustained oscillation of the measured force (bottom) about the reference (dashed). The series compliance of the lightweight distal links stores and discharges elastic energy on each slip event, so the integrator hunts across the stiction dead-band without converging to a unique equilibrium.

Fig. 7 : Sinusoidal force tracking on the horizontal

F_{x}

axis. Top: the commanded reference (dashed), the raw measured force (grey), and the phase-compensated response (blue). The raw signal reproduces the reference amplitude but is shifted by the near-constant transport delay (

\approx 140

ms), which at this frequency manifests as a large phase lag; removing this delay recovers the phase-compensated trace, which closely overlays the reference. Bottom: instantaneous tracking error before (grey) and after (blue) phase compensation, showing that the raw error is dominated by the pure time delay—its removal collapses the error to the residual amplitude- and shape-tracking component (phase-compensated RMSE

=0.36

N at

2

Hz), showing that the raw RMSE is dominated by the measured transport delay.

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Assistive robotics develops robots and wearable devices that support mobility, daily activities, rehabilitation, and care.

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ATP: Anatomical Torque with Passivity-based Control Framework for Safe Upper-Limb Exoskeleton Assistance

Yu Chen, Gong Chen, Xiang Li

Department of Automation, Tsinghua University, China · Shenzhen MileBot Robotics Co., Ltd, China

Ankle Exoskeleton Control Feedback Control Assistive Robotics Passivity-Based Control Upper-Limb Exoskeletons

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Providing assistance across diverse movements is a central objective of exoskeletons, and anatomical knowledge can enable responsive support that generalizes across tasks. However, anatomical assistance has mainly been studied for lower-limb exoskeletons, where periodic, weight-bearing motions impose lower demands on torque precision. Extending such assistance to complex, nonperiodic upper-limb movements remains challenging. This paper proposes Anatomical Torque with Passivity-Based Control (ATP) for safe upper-limb exoskeleton assistance. First, a scalable musculoskeletal simulation framework trains a unified reinforcement-learning muscle controller that generalizes across upper-limb movements and generates anatomical reference torques without complex biomechanical computations. Second, an online torque-refinement scheme adapts the reference to diverse movements, suppresses tendon-induced spikes, and incorporates a learned anomaly score for safe and comfortable assistance. Third, an interaction torque controller delivers assistance through a cable-driven compliant exoskeleton without constraining motion to predefined trajectories, while an energy tank preserves passivity with theoretical guarantees on torque tracking and system passivity. Simulations and real-world experiments show accurate tracking of long-duration motion sequences and generalization to real-time human movements. The controller achieves accurate torque tracking while preserving passivity and resumes tracking after energy-tank replenishment. An EMG study with five participants further shows reduced target-muscle activity during static and dynamic tasks compared with gravity compensation and open-loop assistance, with reductions of up to 48% relative to movement without the exoskeleton in a dynamic multi-joint task.

Abstract

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