An Ergonomic, Customizable Soft Robotic Glove toward Personalized Hand Rehabilitation
Authors: Rui Chen, Firman Isma Serdana, Domenico Chiaradia, Xianlong Mai, Elena Losanno, Gabriele Righi, Claudia De Santis, Federica Serra, Vincent Mendez, Cristian Camardella, Daniele Leonardis, Giulio Del Popolo, Silvestro Micera, Antonio Frisoli
Organizations: Institute of Mechanical Intelligence and Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna (SSSA), 56127, Pisa, Italy · The Biorobotics Inst. and Dept. of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy · CAS Key Laboratory of Mechanical Behavior and Design of Materials, Institute of Humanoid Robots, School of Engineering Sciences, University of Science and Technology of China, Hefei, China · Modular Implantable Neuroprostheses (MINE) Laboratory, Universit`a Vita-Salute San Raffaele & Sant’Anna School of Advanced Studies, Milan, Italy · Careggi University Hospital, 50134 Florence, Italy · Bertarelli Fndn. Chair in Translational Neuroengineering, Neuro X Institute, ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Laussane, Switzerland
Hand impairment following neurological disorders substantially limits independence in activities of daily living, motivating the development of effective assistive and rehabilitation strategies. Soft robotic gloves have attracted growing interest in this context, yet persistent challenges in customization, ergonomic fit, and user comfort constrain their clinical utility. Here, we present an ergonomic, customizable fabric-based soft robotic glove whose actuators can be tailored to individual finger-joint geometry. The glove comprises five dual-action actuators supporting finger flexion and extension, together with a dedicated thumb abduction actuator. Leveraging computer numerical control heat sealing technology, we fabricated symmetrical-chamber actuators that adopt a concave outer surface upon inflation, thereby increasing finger contact area and improving comfort. Characterization confirmed joint moment and grasping force sufficient for ADL-relevant tasks. In ten healthy subjects, active assistance significantly reduced forearm muscle activity during manipulation, and a pilot study in three individuals with cervical spinal cord injury showed more natural grasp patterns and reduced reliance on tenodesis grasp.
Figures & tables
Figure 1: Glove system overview. (A) Comparison of finger contact between single-chamber and symmetrical-chamber actuators upon inflation. (B) and (C) Design of the dual-action actuator and soft glove. (D) Complete system comprising the control box, dual-action soft glove, and smartphone web interface. (E) and (F) Glove in fully extended and fully flexed configurations, respectively.
Figure 2: Actuator design and fabrication. (A) and (B) Customized flexion and extension actuators fabricated via CNC heat sealing. (C) and (D) dual-action actuator assembly by heat sealing flexion chambers, extension chambers, and a bottom constraint layer. (E) and (F) Actuator deflection toward the target flexion and extension postures upon selective chamber inflation. (G) Thumb abduction actuator fabrication process.
Figure 3: Thumb actuator characterization. (A) Extension and flexion actuator operating configurations. (B) Moment characterization experimental setup. (C) and (D) Effects of fold angle and inflation pressure on flexion bending moment. (E) and (F) Fingertip blocking force measurement setup and results. (G) Extension actuator moment characterization setup. (H) and (I) Effects of fold angle and inflation pressure on extension bending moment.
Figure 4: Glove grasping performance characterization. (A) Directional grasp force measurement setup. (B) and (C) Directional grasp force at different inflation pressures. (D) and (E) Normal grasp force measurement configuration. (F) Normal grasp force as a function of inflation pressure. (G) Frictional grasp force measurement setup. (H) and (I) Effects of inflation pressure and cylinder diameter on frictional grasp force.
Figure 5: Protocol and results of the preliminary healthy subjects study. (A)–(C) Participants performing six transport tasks with the sEMG sleeve under three glove conditions (no glove, passive, and active). (D)–(F) Averaged sEMG amplitude across 31 active channels for Object 2, illustrating differences between conditions. (G)–(H) Mean muscle activity and task completion time across six objects under the three conditions.
Figure 6: Pilot clinical feasibility study. (A) Experimental setup for clinical testing. (B) Overview of the seven evaluation tasks. (C)–(J) Task-level results for three individuals with cervical SCI under assisted and unassisted conditions.
Department of Mechanical Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA · Faculty of Mechanical Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA
Department of Information Engineering and Mathematics - University of Siena. · Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague