Cable-driven robotic interface for lower limb neuromechanics identification

Arash Arami; Etienne Burdet; Hsien-Yung Huang; Ildar Farkhatdinov; Mohamed Bouri

REVIEW 3 major objections 4 minor 34 references

Cable-driven robotic interface for lower limb neuromechanics identification

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A cable-driven robotic interface can identify hip, knee, and ankle neuromechanics in an upright posture without constraining the joints.

desk verdict Genuinely new cable-driven lower-limb platform; the validation claim is undercut by the paper's own cable compliance identification, so treat the impedance numbers with caution. read the letter →

arxiv 1908.02689 v2 pith:446H7VNI submitted 2019-08-07 cs.RO cs.SYeess.SY

Hsien-Yung Huang , Ildar Farkhatdinov , Arash Arami , Mohamed Bouri , Etienne Burdet This is my paper · ORCID

classification cs.ROcs.SYeess.SY

keywords cable-drivenrobotneuromechanicsidentificationjointimpedancehipstiffnesslowerlimbpositionperturbationsystemendpoint-basedinterface

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that one cable-driven, endpoint-based robotic interface can identify the mechanical impedance—inertia, viscosity, and stiffness—of the hip, knee, and ankle in a natural upright posture, without locking the joint into a fixed axis. Existing motor-driven dynamometers constrain joint motion and cannot easily reach the hip under weight-bearing conditions, while exoskeletons add structural vibrations and constrain the joints. The authors build the Neuromechanics Evaluation Device (NED), in which a powerful actuator fixed outside the workspace drives a pretensioned cable attached at the ankle, and validate it with an 18 kg dummy leg and springs of known stiffness. A pilot on one human subject produces reproducible hip impedance estimates whose inertia matches an anatomical model. If the claim holds, researchers could measure lower-limb neuromechanics in the postures that matter for standing and walking, using a single device.

What carries the argument

The central object is NED itself, a cable-driven endpoint interface whose actuator sits on the floor, connected by a pretensioned steel cable loop to a foot fixture with two load cells; lockable pulleys and an open seat let the same rig address hip, knee, or ankle. The load-bearing kinematics is the planar mapping $\dot{\theta} = \rho_m \dot{\theta}_m / L$ of Eq. (2), which turns motor-pulley motion into joint rotation, and the impedance identity of Eq. (4), which turns differential cable force $(F_1-F_2)L$ and joint angle into $I$, $B$, and $K$. The capstan equation $T_{\text{load}} = T_{\text{hold}} e^{\mu\phi}$ sizes the cable wrap to prevent slippage, and a 200 N pretension limits sagging-induced force error to below 2.5%. A smooth ramp-and-hold perturbation with a constant-position plateau lets Eq. (5), $\Delta\tau = K\Delta\theta$, read stiffness directly at the plateau because acceleration and velocity vanish there.

What would settle it

Instrument the leg endpoint with a motion capture marker and repeat the spring and dummy-leg identifications; if the endpoint displacement differs from the motor-encoder displacement by more than the paper's 5% error bound, the rigid-cable assumption is violated. Alternatively, fit the same data with the measured cable stiffness placed in series with the identified joint and check whether the estimated stiffness shifts by more than the reported variance.

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Extended reading notes

Core claim

The core claim is that, once the cable dynamics in series with the leg are neglected, the leg's motion obeys a linear second-order joint model $\Delta\tau = I\Delta\ddot{\theta} + B\Delta\dot{\theta} + K\Delta\theta$, with $\Delta\tau = (F_1 - F_2)L$ read from two load cells, so a least-squares fit of a fast position perturbation returns the joint's inertia $I$, viscosity $B$, and stiffness $K$. Mechanical characterization shows the interface itself behaves as a stiff second-order system with stiffness above 500 N/m and low viscosity. Identification of a rigid dummy leg recovers its CAD-computed inertia of $1.84\,\mathrm{kg\,m^2}$, and spring tests recover known stiffness values, with accuracy improving at larger perturbation amplitudes. Applying the same perturbation on one relaxed human subject yields hip impedance estimates with small variance and inertia close to an anatomical prediction, supporting the claim that NED can identify single-joint neuromechanics in an upright posture without constraining the joint.

Load-bearing premise

The identification treats the cable transmission as effectively rigid and the leg as a rigid straight segment rotating in the sagittal plane, so the motor-side displacement is taken to equal the joint rotation; if the measured cable stiffness (around 500 N/m, comparable to the validation springs) cannot be neglected, the estimated joint stiffness would be biased.

Editorial extensions

If this is right

One device can map hip, knee, and ankle neuromechanics by relocating pulleys and adjusting the seat, with the same actuator and control chain.
A single perturbation protocol yields all three impedance parameters: stiffness from the plateau, inertia and viscosity from the transient fit.
The interface supports isometric, isokinetic, and brief high-speed perturbations up to 750 mm/s, covering protocols used for reflex and spasticity assessment.
Because body weight is carried by an independent structure and the joint is not mechanically fixed, measured mechanics correspond to upright, natural postures.
Hip viscoelasticity, previously accessible mainly through less accurate multi-joint perturbations, can now be probed with a precise single-joint position displacement.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct way to test the rigid-cable assumption is to include the identified cable stiffness as a series spring in the impedance model and see whether estimated joint stiffness changes by a detectable amount.
Adding motion capture of the actual joint angle, rather than the motor encoder, would check the planar rigid-leg kinematic mapping and could extend the method to multi-joint perturbations.
The same endpoint cable concept could scale to other joints or to patient populations, since the independent support removes the need for the subject to balance during measurement.
The pilot compares only inertia to an anatomical model; a same-subject comparison of stiffness and viscosity against an independent method would confirm human accuracy.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Couple of things to know. The hardware concept is genuinely new and worth knowing about: a cable-driven, endpoint-based platform that can perturb hip, knee, or ankle in an upright posture without constraining the joint. That fills a real gap. The paper also does honest system identification work on the cable itself. But the central validation argument has a soft spot that runs straight through it. Their own identified cable stiffness is around 500 N/m (Section III-C, Fig. 6), which is not rigid in any meaningful sense. Yet Section IV-A just asserts that "the cable dynamics in series with the leg could be neglected." That assumption is doing load-bearing work. If the cable compliance is in series with the leg, then using the motor encoder as the leg's displacement in Eqs. (4) and (5) biases all three impedance parameters. The stress-test note makes the calculation: with Kx ~500 N/m and leg length 0.7 m, the rotational series compliance is ~245 Nm/rad, the same order as the joint stiffnesses being estimated. The spring validation does not refute this because the paper never reports the actual spring constants—so the estimated stiffness could be the series combination, not the spring alone. The dummy leg inertia matches CAD, which is a nice check, but a matching inertia does not rule out a series compliance bias; it just means the fitted inertia happens to come out right, and the spring test is the place to catch it if ground truth were reported. The human pilot is one subject and only hip, with no gold-standard comparison. Knee and ankle are claimed but not validated at all. So the paper is a solid hardware contribution with a validation plan that is not yet airtight. The math and the citations look fine. The cable identification itself is a good piece of work. I would send it to peer review, but I would insist the authors either measure the endpoint position directly or correct for the identified cable compliance before claiming accurate impedance identification. For a reader it is a useful paper to know about, but I would not use the reported impedance numbers yet.

Referee Report

3 major / 4 minor

Summary. The paper describes NED, a cable-driven endpoint-based robotic interface for identifying single-joint neuromechanics (inertia, viscosity, stiffness) of the hip, knee, and ankle in an upright, unconstrained posture. The authors characterize the device's kinematics, force measurement sensitivity, and cable transmission dynamics, then validate impedance identification on a rigid dummy leg (inertia compared with CAD) and on linear springs of known stiffness, and finally present a single-subject pilot for hip impedance. The central claim is that the device can accurately identify the mechanical impedance of a limb, with the identification model given in Eq. (4): Δτ = IΔθ¨ + BΔθ˙ + KΔθ.

Significance. If the accuracy claim holds, NED would be a useful addition to lower-limb neuromechanics research, because existing dynamometers constrain joints and exoskeletons have limited rigidity; the ability to test hip, knee, and ankle in a natural upright posture without joint constraints fills a practical gap. The paper provides careful mechanical characterization, multiple validation conditions including a dummy leg and springs, and a human pilot, which is more than many device papers offer. The validation targets are independent (CAD inertia, known spring constants, Winter's anatomical model), which is a strength. However, the central claim of accurate impedance identification rests on the assumption that the cable transmission is effectively rigid, and the manuscript's own cable identification data challenge that assumption. The result is therefore promising but not yet established.

major comments (3)

[Section IV-A, Eq. (4)] The identification model Eq. (4) treats the motor-encoder displacement as the leg's angular displacement, neglecting the series cable compliance. The paper's own cable characterization in Section III-C reports a cable stiffness around 500 N/m (Fig. 6b) with poles at 1.6 and 17.2 Hz. This stiffness is the same order of magnitude as the spring constants validated in Section IV-B and, after conversion to a rotational stiffness for a ~0.7 m leg, is comparable to the joint impedances being estimated. With a series compliance Kx in series with the leg impedance, the stiffness seen by the motor is not the leg stiffness alone; for Kx = Ks, the apparent stiffness is halved. No independent measurement of the leg endpoint displacement (e.g., optical tracking) and no series-compliance correction are reported, so the quantitative accuracy claim in the abstract is not supported as it stands.
[Section IV-B, Eq. (5)] The spring stiffness validation in Eq. (5) also uses motor-side displacement ΔX and measured force difference ΔF. If the spring is attached beyond the cable and the load-cells measure the cable tension, then the system identified includes the cable stiffness in series with the spring, and the motor-side stiffness is Kx*Ks/(Kx+Ks) rather than Ks. The paper does not state where the load-cells are located relative to the spring or the cable, does not report an independently measured spring constant, and does not compare motor displacement with spring endpoint displacement. Without these details, the claim that the estimated stiffness is 'similar to the spring constant' does not confirm that the device measures the spring's actual stiffness.
[Section IV-C, Fig. 9] The human pilot applies the same rigid-transmission assumption when fitting Eq. (4) to motor-encoder data. The perturbation uses a 150 ms plateau, which contains spectral content near the 1.6 Hz cable pole identified in Section III-C. Since no motion capture or other endpoint measurement is used, the reported hip inertia, viscosity, and stiffness values may contain contributions from the cable dynamics. The small variance and the agreement of the inertia estimate with Winter's model are encouraging, but they do not resolve the transmission-identification confound. A definitive validation would require either endpoint motion measurement or inclusion of the identified cable dynamics in the identification model.

minor comments (4)

[Section III-A, Eq. (2)] In Eq. (2), ρ_m is described as the motor pulley diameter, but the relation ˙x = ρ_m ˙θ_m is correct only if ρ_m is the pulley radius. Please clarify which quantity is used.
[Section IV-C] The pilot subject is described as female, but the text later says 'asked to support his body weight' and 'his leg'; please make the pronouns consistent.
[Section III-C, Fig. 6b] The identified cable stiffness values in Fig. 6b appear to vary considerably across speeds (roughly 0 to 600 N/m), yet the text states 'low variance' without reporting quantitative variance measures. Please provide error bars or confidence intervals for the identified parameters.
[Section II-C] The text notes that adding a motion capture system 'would enable to measure hip joint rotations directly and could improve the leg displacement measurement relative to the current estimation from the motor encoder.' This is a relevant limitation of the current study and should be discussed explicitly in Section V, not only in the hardware description.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the impedance validations are checked against CAD, known springs, and the Winter anatomical model, not against the model's own fitted inputs.

full rationale

The paper's central claim that NED can identify single-joint neuromechanics accurately is validated against independent references rather than against its own fitted inputs. The dummy leg inertia estimate is compared to the CAD value of 1.84 kg m^2 (Fig. 7), the spring experiments use springs with known elasticity and compare the estimated stiffness to the known spring constants (Fig. 8 caption and results), and the human pilot's inertia estimate is compared to the anatomical model of Winter (Fig. 9b). The cable transmission is modeled as a second-order system (Eq. 3) from its own identification data, but this model is used only to justify neglecting series cable dynamics in Section IV-A; it does not define or constrain the limb impedance parameters fitted in Eq. (4). The stiffness estimation method of [33] is an externally published method, not an unverified self-citation, and the self-citations [32] and [34] provide design details and future application respectively rather than load-bearing evidence. The rigid-transmission assumption of Eq. (2) is a modeling assumption whose failure would bias estimates, but that is a correctness risk rather than a circular derivation. No equation is defined in terms of the target result, and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the assumption that the cable transmission is rigid enough to be ignored in the impedance estimation. The fitted cable stiffness (~500 N/m) is the main free parameter that could bias results if inaccurate. No new physical entities are postulated. The domain assumptions about planar leg rotation and perpendicular cable alignment are standard for single-joint identification but are not independently verified in this paper.

free parameters (2)

Cable stiffness Kx = ~500 N/m (estimated using tfest)
Fitted to system identification data in Section III-C; used to justify the claim that NED is rigid and that cable dynamics can be neglected for limb impedance estimation.
Cable viscosity Bx = low, speed-dependent (Fig. 6b)
Fitted in the same system identification; part of the second-order cable model.

assumptions (5)

standard math Capstan equation Tload = Thold * exp(mu*phi) holds for the cable-pulley contact (Eq. 1)
Used in Section II-B to determine the number of cable turns needed to prevent slippage.
domain assumption The leg is a rigid segment rotating in the sagittal plane about the hip, with the knee locked, so that motor speed relates to joint angular velocity by Eq. 2
Assumed in Section III-A for kinematics and in the dummy leg/human identification (Eq. 4).
domain assumption The cable is perpendicular to the leg and the endpoint force is tangent to the leg's arc, so that joint torque equals (F1-F2)L
Assumed in Eq. 4 and in the off-plane error analysis; pulley positions are adjusted to maintain perpendicularity.
ad hoc to paper The cable is effectively rigid: motor-encoder displacement equals leg endpoint displacement and cable dynamics can be neglected when estimating limb impedance
Stated in Section IV-A; this is the key assumption that is only weakly supported by the ~500 N/m cable stiffness.
domain assumption The subject is relaxed and does not produce voluntary reactions during the 150 ms perturbations, so the measured torque-angle relation reflects passive joint mechanics
Assumed in the human pilot study (Section IV-C).

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Cite this review

Pith. "Pith review of Cable-driven robotic interface for lower limb neuromechanics identification." pith.science (2026). https://pith.science/paper/446H7VNI

@misc{pith2026190802689,
  author       = {Pith},
  title        = {Pith review of: Cable-driven robotic interface for lower limb neuromechanics identification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/446H7VNI}},
  note         = {Machine review of arXiv:1908.02689}
}

read the original abstract

This paper presents a versatile cable-driven robotic interface to investigate the single-joint joint neuromechanics of the hip, knee and ankle. This endpoint-based interface offers highly dynamic interaction and accurate position control, as is typically required for neuromechanics identification. It can be used with the subject upright, corresponding to natural posture during walking or standing, and does not impose kinematic constraints on a joint, in contrast to existing interfaces. Mechanical evaluations demonstrated that the interface yields a rigidity above 500N/m with low viscosity. Tests with a rigid dummy leg and linear springs show that it can identify the mechanical impedance of a limb accurately. A smooth perturbation is developed and tested with a human subject, which can be used to estimate the hip neuromechanics.

Figures

Figures reproduced from arXiv: 1908.02689 by the authors.

**Figure 1.** As shown in Fig. 1a, the subject is half seated on a rigid chair (of length 0.55 m, width 0.7 m and height 1.5 m) with one leg suspended in the workspace and attached to the system via a foot fixture. The leg is moved by the motor (AM8061, Beckhoff, Germany) located at the bottom of the workspace, via a steel cable (7x7 galvanised steel with PVC coating). Two load-cells (TAS510, HT sensors, China) are placed between… view at source ↗

**Figure 2.** The control system of NED. using a knee brace (T-scope, Breg), which enables us to study the influence of the knee angle. By adjusting the seat, it becomes also possible to study the knee neuromechanics as shown in Fig. 1c. In order to select an actuator based upon our design criteria, motors produced by Beckhoff, ETEL and Infranor were evaluated depending on their technical specifications including motor peak torqu… view at source ↗

**Figure 1.** Neuromechanics Evaluation Device (NED). Panel (a) shows how the subject seated in a rigid chair with an open design allowing the leg movement. The motor force transmitted by the cable is measured by load-cells on both sides of the ankle fixture (front force F1, rear force F2). ˙θm and τm are the speed and torque at the motor, ˙θ the hip joint angular velocity, x˙ the cable linear movement speed and L the measured le… view at source ↗

Figures from the paper (5 more)

**Figure 3.** Figure 3: Laser safety system composed of a laser emitter box (with a focusing lens) and a receptor box. Any obstacle blocking the laser transmission will immediately shut down the power supply to the motor controller. (AX5112, Beckhoff) and motor (AM8061, Beckhoff). The cable s…

**Figure 5.** Figure 5: Cable sagging. The weight of the cable system (i.e. cable, harness, load-cells and turnbuckles) deforms the cable as illustrated in (a). (b) During a back and forth motion, the measured force will not change monotonically, which is marked by a red circle. Cable sagging…

**Figure 6.** Figure 6: Identification results of NED as a linear second order system. Panel (a) depicts the perturbation profiles at different speed with estimated impedance values shown in panel (b). Panel (c) is the Bode plot of the system (with average values) that contains two poles at 1…

**Figure 7.** Figure 7: Identification of the mechanics of a 18 kg dummy leg at joint angles between 15◦ - 55◦ and speed 20 - 750 mm/s, with 20 trials at each condition. Panel (a) shows the CAD drawing of the dummy leg, Panel (b) the boxplot of the estimated inertia, damping and stiffness val…

**Figure 9.** Figure 9: Estimation result of a pilot study with one subject. Panel (a) is an example of the profile with the position at the top and the force feedback at the bottom. Panel (b) shows the estimated joint impedance. The estimated inertia is close to the estimation from the anato…

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A. Fujii, S. Oda, S. Komada, and J. Hirai, “A muscle tension esti- mation method by using mechanical impedance of human knee joint during training by manipulator,” in Mechatronics,IEEE International Conference on Mechatronics . IEEE, 2007, pp. 1–6

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[31]

The effect of transmission design on force- controlled manipulator performance,

W. T. Townsend, “The effect of transmission design on force- controlled manipulator performance,” Ph.D. dissertation, Mas- sachusetts Institute of Technology, USA, 1988

1988
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Development of the neuromechanics evaluation device (NED) for subject-speciﬁc lower limb modelling of spinal cord injury,

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2019
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A method for measuring endpoint stiffness during multi- joint arm movements

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The inﬂuence of posture, applied force and perturbation direction on hip joint viscoelasticity,

H.-Y . Huang, A. Arami, I. Farkhatdinov, D. Formica, and E. Burdet, “The inﬂuence of posture, applied force and perturbation direction on hip joint viscoelasticity,” IEEE Transactions on Neural Systems and Rehabilitation Engineering , 2020

2020

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

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Musculo-skeletal modelling of NMES-evoked knee extension in spinal cord injury,

P. J. Sinclair, G. M. Davis, and R. M. Smith, “Musculo-skeletal modelling of NMES-evoked knee extension in spinal cord injury,” Journal of Biomechanics , vol. 39, no. 3, pp. 483–92, 2006

2006

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Knee and ankle joint torque-angle relationships of multi-joint leg extension,

D. Hahn, M. Olvermann, J. Richtberg, W. Seiberl, and A. Schwirtz, “Knee and ankle joint torque-angle relationships of multi-joint leg extension,” Journal of Biomechanics , vol. 44, no. 11, pp. 2059–65, 2011

work page 2011

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Effects of spinal cord injury on lower-limb passive joint moments revealed through a nonlinear viscoelastic model

K. Amankwah, R. J. Triolo, and R. F. Kirsch, “Effects of spinal cord injury on lower-limb passive joint moments revealed through a nonlinear viscoelastic model.” Journal of Rehabilitation Research and Development , vol. 41, no. 1, pp. 15–32, 2004

2004

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Re- producibility for isometric and isokinetic maximum knee ﬂexion and extension measurements using the IsoMed 2000-dynamometer,

J. Dirnberger, H.-P. Wiesinger, A. K ¨osters, and E. M ¨uller, “Re- producibility for isometric and isokinetic maximum knee ﬂexion and extension measurements using the IsoMed 2000-dynamometer,” Isokinetics and Exercise Science , vol. 20, no. 3, pp. 149–53, 2012

work page 2000

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Inter- machine reliability of the Biodex and Cybex isokinetic dynamometers for knee ﬂexor/extensor isometric, concentric and eccentric tests,

J. B. de Araujo Ribeiro Alvares, R. Rodrigues, R. de Azevedo Franke, B. G. C. da Silva, R. S. Pinto, M. A. Vaz, and B. M. Baroni, “Inter- machine reliability of the Biodex and Cybex isokinetic dynamometers for knee ﬂexor/extensor isometric, concentric and eccentric tests,” Physical Therapy in Sport , vol. 16, no. 1, pp. 59–65, 2015

work page 2015

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Reliability and validity of the Biodex system 3 pro isokinetic dynamometer velocity, torque and position measurements,

T. C. Valovich-mcLeod, S. J. Shultz, B. M. Gansneder, D. H. Perrin, and J. M. Drouin, “Reliability and validity of the Biodex system 3 pro isokinetic dynamometer velocity, torque and position measurements,” European Journal of Applied Physiology , vol. 91, no. 1, pp. 22–9, 2004

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Clinical as- sessments performed during robotic rehabilitation by the gait training robot Lokomat,

L. L ¨unenburger, G. Colombo, R. Riener, and V . Dietz, “Clinical as- sessments performed during robotic rehabilitation by the gait training robot Lokomat,” in Proceedings of the IEEE International Conference on Rehabilitation Robotics , 2005, pp. 345–8

2005

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Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation,

J. F. Veneman, R. Kruidhof, E. E. Hekman, R. Ekkelenkamp, E. H. Van Asseldonk, and H. Van Der Kooij, “Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 15, no. 3, pp. 379–86, sep 2007

work page 2007