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A multi-joint soft exosuit improves shoulder and elbow motor functions in individuals with spinal cord injury

Nature Machine Intelligence volume 7, pages 1390–1402 (2025)Cite this article

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Abstract

Spinal cord injury (SCI) disrupts neuromuscular control, severely affecting independence and quality of life. Although upper limb wearable robots hold considerable promise for functional restoration, most existing prototypes have been validated minimally in people with SCI and target almost exclusively hand opening and closing. We introduce a lightweight, modular assistive soft exosuit that simultaneously and automatically supports shoulder abduction and elbow flexion or extension movements using lightweight fabric-based pneumatic actuators, controlled through inertial sensors. The individual elbow modules were first validated in 11 healthy volunteers, and subsequently tested, together with the shoulder module, in 15 individuals with cervical SCI (C4–C7, AIS A–D). In the SCI participants, exosuits assistance resulted in increased static endurance time (by more than 250%), and lower activity of the primary muscles involved in dynamic tasks (by up to 50%). The two SCI participants retaining prehensile capability also improved their scores in the box and block test when assisted. Moreover, the soft actuation provided a safe, comfortable and easy-to-use solution that was positively appreciated by the participants. Collectively, these results provide encouraging evidence that exosuits can augment upper limb motor performance, and may ultimately translate into greater functional independence and quality of life for the SCI population.

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Fig. 1: Overview of the modular upper limb soft exosuit.
Fig. 2: Static and dynamic exercises on healthy participants.
Fig. 3: Static and dynamic exercises on SCI participants.
Fig. 4: Performance of simulated ADLs.
Fig. 5: Example behaviours of SCI participants performing ADLs assisted by the exosuit.
Fig. 6: Results from BBT, SUS and QUEST.

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Data availability

Source data are provided with this paper.

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Acknowledgements

This work was supported by the Bertarelli Foundation (S.M.), by #NEXTGENERATIONEU (NGEU) and by the Italian Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP) with two projects: project THE (IECS00000017)—Tuscany Health Ecosystem (DN. 1553 11.10.2022, awarded to S.M.); and project MNESYS (PE0000006)—a multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022, awarded to S.M.). We thank all the participants for volunteering their time and contributions to this study.

Author information

Author notes

  1. These authors contributed equally: Silvestro Micera, Tommaso Proietti.

Authors and Affiliations

  1. The Biorobotics Institute and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy

    Roberto Ferroni, Gaetano D’Avola, Jacopo Carpaneto, Silvestro Micera & Tommaso Proietti

  2. Spinal Unit, Careggi University Hospital, Firenze, Italy

    Giorgia Sciarrone, Gabriele Righi, Claudia De Santis & Giulio Del Popolo

  3. Department of Mechanical Engineering, Politecnico di Milano, Milano, Italy

    Marta Gandolla

  4. Translational NeuroEngineering Laboratory, Neuro-X Institute, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Silvestro Micera

  5. Modular Implantable Neuroprostheses (MINE) Laboratory, Università Vita-Salute San Raffaele, Milano, Italy

    Silvestro Micera & Tommaso Proietti

Authors
  1. Roberto Ferroni
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  2. Gaetano D’Avola
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Contributions

T.P., R.F. and G.D.A. built the robot (textile, mechatronics and software). All authors designed the study protocol. R.F., G.D.A., G.S., G.R. and C.D.S. ran the protocol. T.P., R.F. and G.D.A. processed the data and ran the statistical analysis. T.P., R.F. and S.M. wrote the original manuscript. All authors reviewed the manuscript and provided critical feedback.

Corresponding author

Correspondence to Tommaso Proietti.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks Wanru Duan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Design of the extensor and flexor actuators.

Both actuators are fabricated from two TPU-coated Nylon sheets (grey); an internal air chamber is created by inserting heat-resistant paper (light blue) between the layers prior to heat-sealing. A polyurethane tube is then heat-sealed to the chamber opening. For the extensor actuator (a) longitudinal incisions permit attachment to the forearm with Velcro straps. For the flexor actuator (b) Velcro straps are routed through the incisions to anchor the actuator’s ends across proximally and distally relative to the elbow joint. For more details about the manufacturing process of this module, refer to48.

Extended Data Fig. 2 Mechanical characterization of the extensor actuator.

a) Torque-pressure characterization. As expected, the torque behavior is linearly dependent on the pressure, achieving an average maximum of 22.1 ± 0.8 Nm (standard error) when at 135º and 69 kPa. b) Torque-angle characterization. The behavior is non-linear, with the clear peak of torque occurring approximately at 135º. All curves are average values and standard errors out of three measurements from three different actuators (thus 9 values) mounted on a custom 3D printed mannequin, measured with a universal material tester (Instron 5965, Instron Corporation, USA) mounting a 1kN load cell. c) Pictures of the test setup at different mannequin, thus actuator, angles.

Extended Data Fig. 3 Mechanical characterization of the flexor actuator.

a) Force-pressure characterization. As expected, the force behavior is linearly dependent on the pressure, achieving an average maximum of 40.4 ± 3.5 N (standard error) when fully closed (0% of contraction ratio) and 69 kPa. b) Force-contraction ratio characterization. The behavior is non-linear with a peak of force occurring approximately at 0% of contraction ratio and then a second local peak at approximately 60%. All curves are average values and standard errors out of three static measurements from three different actuators (thus 9 values) with a universal material tester (Instron 5965, Instron Corporation, USA) mounting a 1kN load cell. c) Pictures of the test setup (from left to right, 0, 40, 60, 100% CR).

Extended Data Fig. 4 SCI Subgroup Analysis – Dual Effect.

Participants with greater impairment (lower baseline ROM, thus low x values) exhibited higher active ROM improvements (higher y values), whereas less impaired individuals (higher baseline ROM, higher x values) largely maintained their range of motion, benefiting instead from reduced muscular effort. This dual effect underscores the assistive potential of the system across a spectrum of impairments. Positive y-values indicate increased ROM when assisted (that is, ROM with the robot ON greater than with the robot OFF). Blue circles indicate reduction in muscle activity as % of baseline EMG. a) Analysis of elbow flexion movements during ROM task. b) Analysis of the elbow extension movements during ROM task. Each circle represents data from one SCI participant. An exponential model captured the observed trends, with R² = 0.85 for flexion and R² = 0.62 for extension.

Extended Data Table 1 Characteristics of the healthy participants
Full size table
Extended Data Table 2 Characteristics of the SCI participants
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Extended Data Table 3 Mechanical transparency
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Extended Data Table 4 Cost estimates for manufacturing the exosuit
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Supplementary information

Reporting Summary

Supplementary Video 1

Healthy participants.

Supplementary Video 2

SCI participants.

Supplementary Video 3

Exosuit overview.

Supplementary Video 4

Full-system integration proof of concept.

Source data

Source data for Figs. 1–6

Processed data used for plots in Figs. 1–6 (bar plots and time series), organized in separate sheets.

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Ferroni, R., D’Avola, G., Sciarrone, G. et al. A multi-joint soft exosuit improves shoulder and elbow motor functions in individuals with spinal cord injury. Nat Mach Intell 7, 1390–1402 (2025). https://doi.org/10.1038/s42256-025-01105-8

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