Search Results (213)

Hand rehabilitation orthoses play an important role in restoring upper-limb function in individuals with impaired grasp ability; however, selecting an actuation mechanism that balances functional performance, manufacturing complexity, and production cost remains a significant engineering challenge. This study aimed to develop and compare three low-cost hand and forearm rehabilitation orthosis prototypes within a common subject-specific digital workflow based on three-dimensional (3D) scanning, computer-aided design (CAD), and fused deposition modeling (FDM). Three finger actuation mechanisms—a cable-driven mechanism powered by two DC motors, an arc-based mechanism driven by a DC motor, and an arc-based mechanism driven by a linear actuator—were integrated into subject-specific orthotic structures. The prototypes were evaluated on a single healthy subject by comparing manufacturing time, material consumption, weight, production cost, finger range of motion (ROM), and angular velocity, supplemented by a simplified theoretical mechanical analysis. Prototype 2 achieved the highest total finger ROM (186.66 ± 5.35°) and the greatest angular velocity during extension (177.71 ± 30.03°/s) and flexion (183.16 ± 81.16°/s), while maintaining a low production cost of €18.33. The proposed workflow enabled systematic comparison of the three implemented actuation mechanisms and identified Prototype 2 as providing the most favorable balance between kinematic performance, manufacturing requirements, and practical scalability under the tested conditions. Its simple, modular, and customizable architecture provides a suitable basis for subsequent multi-finger development. Full article

Background: Recent advancements in enabling technologies, including artificial intelligence and telemedicine, alongside robust clinical study outcomes, have led to significant progress in upper limb and hand exoskeletons utilised for post-stroke rehabilitation. Objectives: This review aims to synthesize the recent scientific literature (2010–2025) on post-stroke upper-limb and hand exoskeletons, with particular attention to the sensing architectures—sensing modalities, signal processing, sensor fusion, and sensor-driven control—that integrate technical and biomedical domains to examine device architecture, clinical context, and outcome selection. Methods: A search of PubMed and Scopus was conducted on 10 November 2025, cross-checked against IEEE Xplore, Web of Science, Embase, and ACM Digital Library. We included studies evaluating wearable exoskeletons or robotic orthoses for the upper limb/hand in post-stroke rehabilitation. Two independent reviewers screened records and extracted data, with disagreements resolved by consensus. Data were synthesised using a predefined label-based taxonomy. The review protocol was not registered. Results: From 1889 identified records, 219 studies met the inclusion criteria. The synthesis reveals a transition from rigid, laboratory-centered systems to lighter, soft, and home-oriented solutions. Available evidence suggests potential impairment-level benefits, particularly for proximal motor control, but certainty remains limited due to heterogeneity, small samples, blinding limitations, inconsistent dosing, and limited long-term follow-up; gains in hand/finger dexterity appear even more variable. Discussion: While exoskeleton-assisted therapy appears associated with impairment-level gains, transfer to activities of daily living (ADLs) and real-world function remains inconsistently documented and insufficiently powered to support firm conclusions. Full article

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

Wearable rehabilitation monitoring requires soft strain sensors with mechanical robustness, stable electromechanical responses, and intelligent motion recognition capability. Here, we report a poly(vinyl alcohol)/polyacrylamide (PVA/PAM) double−network hydrogel strain sensor for rehabilitation−oriented wearable monitoring. The hydrogel was prepared by ultraviolet ray (UV)−initiated acrylamide polymerization followed by freeze−thaw−induced PVA crystallization, forming a covalent PAM network interpenetrated with a physically crosslinked PVA network. The resulting hydrogel possessed a compact porous structure, improved stretchability, and stable deformation recovery. The optimized sensor exhibited a tensile strength of approximately 0.52 MPa, an elongation at break of approximately 480%, a response time of 0.12 s, and a recovery time of 0.17 s. It generated repeatable resistance signals under cyclic strain, finger bending, wrist motion, and grip training. Furthermore, the sensor enabled morse−code information transmission and support vector machine (SVM)−based recognition of rehabilitation−related hand states, including straight, bend, and clench. This work provides a soft hydrogel sensing platform for real−time rehabilitation−oriented hand motion, while morse−code encoding provides auxiliary assistance and an emergency communication function. Full article

Stroke and other neurological injuries impair hand function. Although rehabilitation therapists encourage reintegration of the affected hand into daily activities, there are few tools that can be worn during everyday life that provide quantitative feedback on how much or how effectively the hand is used. Wearable sensors that can accurately track hand movements and are easily applied and removed can present intuitive feedback that could motivate hand use similarly to how pedometers encourage walking. While tracking all the hand and finger joints is needed for scientific studies, under-sensorization, or using fewer sensors than required for tracking all degrees of freedom, may suffice for providing users feedback about hand use in everyday life. Further, it may enable a wearable device to be more easily donned and doffed, more power efficient, and more cost efficient. Here we develop a low-cost, multi-sensor, wireless wearable system for tracking selected hand and wrist movements during everyday life. The system includes fabricated soft, stretchable “e-skin” bend sensors and off-the-shelf inertial measurement units (IMUs) that accurately measure finger bend angles and wrist movements. The system also includes an application and removal protocol that enabled naïve unimpaired participants to apply and remove the system in ~5 min and ~4 min, respectively. The system cost was $111 per device, with prices falling to an estimate of $55 when manufactured at scale. This hand wearable demonstrates accurate kinematic tracking and user-friendly donning/doffing workflows for unimpaired participants, making it a promising platform for everyday hand tracking. Future work will extend this platform to the movement-impaired population for neurorehabilitation applications. Full article

This work presents the design and preliminary experimental validation of deformable sensors based on time-domain reflectometry (TDR) for rehabilitation-oriented interaction monitoring. Three architectures were investigated: a planar multilayer sensor and two coaxial configurations based on foam and engineered TPU–Hilbert structures. Controlled indentation tests were performed at different positions and deformation levels, extracting two TDR-derived features: the minimum reflection coefficient ${\rho }_{min}$, related to deformation intensity, and the perturbation time $t_{\mathrm{pert}}$, related to contact localization. Preliminary calibration curves and two-dimensional maps were used to analyze the coupled dependence of the response on position and indentation depth. Application-oriented manual tests confirmed the different suitability of the three geometries for localized finger pressing, distributed two-hand grasping, and controlled single-hand squeezing. Overall, the results support TDR-based deformable sensors as low-complexity and geometry-adaptable tools for spatially resolved monitoring of motor rehabilitation interactions. Full article

The hand is essential for performing everyday activities due to its structural and functional complexity. Injuries and various disorders affecting the hand may lead to significant motor deficits and reduced quality of life. In this context, we developed a system designed to support hand rehabilitation through both evaluation and therapy. The proposed solution combines a hardware component, based on accelerometer and gyroscope sensors connected to an Arduino Mega platform, with a software component consisting of two applications. The system records finger mobility comparing the collected data with physiological parameters and transmits the results via Bluetooth to a mobile application, which provides visual feedback regarding movement deficits. In addition, a secondary application includes a therapeutic game intended to support rehabilitation in an engaging interactive manner. Full article

Background: Potential vision rehabilitation in patients with hypotony and pre-phthisis remains one of the most challenging issues of ophthalmology. Boston type I keratoprosthesis (KPro) combined with pars plana vitrectomy (PPV) and silicone oil injection (SOI) represents a potential approach to this problem in patients believed to have recoverable vision. This study presents a case series of three patients who underwent Boston type I KPro implantation with simultaneous silicone oil tamponade at our center. Methods: We retrospectively reviewed medical records of three patients with prephthisical changes who underwent Boston type I KPro and PPV/SOI. Preoperative and postoperative visual acuity (VA), anatomical stability, and postoperative complications were assessed over a follow-up period. Three eyes of three patients were included. All eyes had severe corneal pathology with sufficient wet surface. Two of three eyes had corneal decompensation secondary to intraocular inflammation, one eye had penetrating trauma-related corneal scarring. All three eyes underwent simultaneous KPro implantation with combined PPV/SOI. Results: Postoperatively, all eyes demonstrated improvement in best-corrected visual acuity (BCVA) from hand motion (HM) to 20/1000, with final BCVA ranging from counting fingers at 2 feet (CF2) to 20/150 and no evidence of progressive phthisical changes over a follow-up period of 23–66 months. Conclusions: Boston type I KPro implantation combined with PPV/SOI can be a viable approach for preserving anatomical integrity and improving visual function in patients with prephthisical eyes and perceived recoverable-vision potential. Full article

Background: Drop finger may occur in patients with C7 and/or C8 cervical radiculopathy caused by cervical spondylosis. Although surgical decompression of the affected nerve roots is performed in patients with drop finger refractory to conservative treatment, postoperative recovery of drop finger is often unsatisfactory. Furthermore, no effective rehabilitation strategy for improving drop finger has yet been established. Methods: Here, we report a patient with drop finger who underwent a novel postoperative rehabilitation program. A 64-year-old man presented with drop finger of the left hand caused by left C7 and C8 radiculopathy and underwent cervical foraminotomy. For postoperative rehabilitation, we applied the single-joint Hybrid Assistive Limb (HAL), a wearable robotic suit. The patient underwent a total of 21 sessions of metacarpophalangeal HAL training, which assisted voluntary flexion and extension movements of the metacarpophalangeal joints, and 6 sessions of wrist abduction HAL training, which assisted ulnar-direction wrist abduction movements. Results: As a result, improvement in the left-sided drop finger was achieved. In this case, the use of HAL enabled voluntary motor training within the normal range of motion of the fingers and wrist even during the early postoperative phase, when sufficient neurological recovery had not yet been achieved. Conclusions: This successful motor experience may have facilitated the reacquisition of normal movement patterns, thereby contributing to improvement in drop finger. Full article

Over the past decade, serious games and virtual reality have gained increasing relevance in upper-limb rehabilitation, yet desktop virtual reality solutions often suffer from reduced spatial correspondence and limited sensory feedback. This work presents the design and preliminary evaluation of a desktop virtual reality-based serious game that combines Leap Motion Controller hand tracking with a custom wireless vibrotactile wearable device to support upper-limb rehabilitation training. Three training scenarios were implemented to target pronation/supination, pinch grip, ulnar/radial deviation, and wrist, elbow, and finger flexion/extension. Usability (System Usability Scale, SUS), user experience (short AttrakDiff), and perceived workload (Raw NASA-TLX), together with functionality and perception questionnaires, were collected from healthy participants randomly assigned to two groups (Group 1: $n=13$, LMC only; Group 2: $n=9$, LMC plus wearable). Across all instruments, the configuration including the wearable device tended to obtain higher usability ratings, more desirable pragmatic and hedonic quality scores, and lower overall workload means than the LMC-only configuration, with moderate effect sizes but limited statistical power due to the small samples. Participants in the wearable condition also reported clearer feedback, a perceived improvement in movement precision, and a stronger perceived alignment between real and virtual actions. These findings suggest that the proposed system may serve as a promising user-centered prototype for desktop VR-based upper-limb rehabilitation and provide preliminary design evidence to support future clinical and kinematic validation studies with larger cohorts. Full article

Objective: To systematically evaluate the rehabilitation effect of brain-computer interface (BCI)-controlled hand robot training on post-stroke motor functions, especially upper limb functions. Methods: PubMed, Embase, Web of Science, Cochrane Library, CNKI, SinoMed, WanFang Data, and VIP Database were searched from inception to 13 March 2026. Randomized controlled trials (RCTs) with dose-matched designs were included, where the test group underwent BCI-controlled hand robot training and the control group received either pure hand robot training or routine rehabilitation. Meta-analysis was performed on RevMan 5.4. Results: Totally 11 RCTs involving 380 patients were included. Compared with hand robot training alone, BCI-controlled hand robot training significantly improved Fugl-Meyer Assessment for Upper Extremity (FMA-UE) scores (MD = 4.87, 95% CI: 1.04 to 8.69) and FMA-UE proximal scores (MD = 4.44, 95% CI: 0.15 to 8.74), and significantly reduced finger flexor spasticity (MD = −0.44, 95% CI: −0.68 to −0.21), but showed no significant difference in distal upper limb motor function or Action Research Arm Test (ARAT) scores. Compared with routine rehabilitation, BCI-controlled hand robot training significantly improved FMA-UE scores (MD = 6.55, 95% CI: 3.49 to 9.61). Conclusions: BCI-controlled hand robot training can effectively improve overall upper limb and proximal motor function after stroke and alleviate finger flexor spasticity, but the evidence for distal hand function and long-term efficacy remains limited. Full article

Lipid keratopathy is an uncommon corneal disorder characterized by stromal lipid deposition that may cause progressive corneal opacity and visual impairment. We report a case of advanced bilateral lipid keratopathy with severe visual-axis involvement. At presentation, best-corrected visual acuity (BCVA) was counting fingers in the right eye and 0.1 Snellen (1.0 logMAR) in the left eye. Slit-lamp examination and anterior segment optical coherence tomography (AS-OCT) demonstrated dense stromal lipid deposits involving the visual axis in both eyes. The patient underwent staged bilateral penetrating keratoplasty, with procedures performed three months apart. Postoperatively, corneal transparency improved in both eyes. At 6 months, BCVA was 0.5 Snellen (0.3 logMAR) in the right eye and 0.7 Snellen (0.15 logMAR) in the left eye. Residual visual limitation was attributed mainly to coexisting cataract, and sequential cataract surgery was planned. Together, the clinical photographs and AS-OCT scans illustrate an uncommon presentation of visually disabling bilateral lipid keratopathy, characterized by dense central stromal lipid deposition involving both visual axes and profound preoperative visual loss. The case is clinically noteworthy because it combines severe bilateral disease, close clinical–tomographic correlation, and sequential penetrating keratoplasty performed as a staged visual rehabilitation strategy, resulting in restoration of graft clarity and meaningful visual improvement during postoperative follow-up. Full article

Objective: The aim of this study will be to evaluate the effectiveness of a task-oriented training program in improving functional performance and health outcomes in patients with MS. Methods: A pilot randomized clinical trial will be conducted according to SPIRIT guidelines. Participants will be randomly assigned to the experimental group or the control group. Assessment and treatment will take place at patient association facilities or research center. Participants diagnosed with MS by a neurologist and meeting the inclusion criteria will be invited to participate voluntarily. The experimental group will undergo an 8-week TOT intervention, twice weekly, 45 min. The control group will maintain usual care and be given a fatigue management pamphlet. The main variable in this study will be the Canadian Occupational Performance Measure. The level of fatigue will be assessed with the Modified Impact Fatigue Scale and the Fatigue Severity Scale, upper limb strength using the Arm Curl Test, hand and pinch dynamometer, motor speed with the Finger Tapping Test, manual dexterity with the Nine Hole Peg Test, Purdue Pegboard Test and the Coin Rotation Test. Satisfaction and adherence with the intervention will be recorded with the Sport Injury Rehabilitation Adherence Scale. Results: The results will be published as a peer-reviewed article. Conclusions: The present protocol aims to fill a relevant gap in the literature, offering a structured intervention based on task-oriented training principles, specifically tailored to the functional and occupational needs of people with MS. Full article

Wearable data gloves often suffer from electromagnetic interference, insufficient substrate stability, and limited capability for multi-degree-of-freedom motion measurement. To address these limitations, a flexible glove incorporating a hybrid POF-FBG sensing scheme was designed and fabricated. Plastic optical fibers (POFs) were side-polished and patterned with long-period gratings to improve sensitivity to wrist flexion-extension and abduction-adduction. Then fiber Bragg gratings (FBGs) were embedded in a polydimethylsiloxane substrate and encapsulated using thermoplastic polyurethane fixtures to reduce the influence of skin stretching and improve measurement accuracy of finger-joint angle. Moreover, a thermoplastic polyurethane skeleton with an adaptive sliding-rail structure was 3D printed to maintain the stability of the sensor placement at the joints. Experimental results demonstrated the mean absolute errors of 4.06°, 1.38° and 1.70° for wrist flexion-extension, abduction-adduction and finger-joint bending, respectively, along with excellent gesture classification using a support vector machine algorithm, which indicates great potential in virtual reality interaction and hand rehabilitation applications. Full article

Flexible resistive bend sensors are essential for monitoring human movement in smart rehabilitation and soft robotics. However, widespread adoption is currently hindered by a trade-off between the high cost of metal-film technologies and the performance degradation (significant hysteresis and non-linearity) of low-cost carbon/polymer composites. This study presents a geometrically customizable bending sensor fabricated from conductive thermoplastic polyurethane (TPU) using Fused Deposition Modeling (FDM) technology as an accessible alternative to commercial sensors. By parametrically optimizing physical dimensions—including trace width, layer thickness, and pattern geometry—the sensors were tailored to achieve target resistance values within a target window of 20–50 kΩ (achieved: ~44 kΩ nominal) for specific finger-joint applications. Electromechanical characterization revealed a negative gauge factor (GF), where resistance decreases upon bending or elongation due to conductive pathway formation and densification within the polymer matrix. This behavior cannot affect sensor operation, and required bend-resistance responses were acquired using geometrical optimization. To compensate for inherent viscoelastic-induced hysteresis and non-linear behavior, a third-degree polynomial modeling approach was implemented. This modeling approach yielded a coefficient of determination (R²) of approximately 0.90. Compared to standard commercial sensors, the proposed FDM-printed design successfully overcomes geometric limitations while offering a cost-effective, high-performance solution for tailor-made wearable technologies and smart rehabilitation gloves. Full article