Search Results (64)

Bilateral morphological asymmetry of the upper limbs may influence grip strength even in semi-active young adults. Understanding this relationship is important for identifying early neuromuscular imbalances with implications for ergonomics and rehabilitation. This study aimed to examine associations between upper limb anthropometric characteristics and grip strength in non-athlete students, considering gender and manual dominance. The sample included 192 healthy university students (110 females, 82 males; mean age 19.92 ± 1.4 years) without prior sports training. Thirteen bilateral anthropometric parameters of the upper limbs were assessed, including hand and palm dimensions, segmental lengths, and arm and forearm circumferences, along with grip strength measured by dynamometry in two positions: arm extended and arm flexed at 90°. Statistical analysis revealed significant differences in forearm length, arm and forearm circumferences, and grip strength (p < 0.001). The dominant limb consistently demonstrated higher grip strength, with mean differences of approximately 2 kg. Male participants showed higher absolute values for all morphological and functional variables, whereas stronger correlations between distal upper-limb morphology and grip strength were observed in females. These findings indicate that, despite largely symmetric skeletal dimensions, moderate functional asymmetries exist and grip strength is influenced primarily by local muscular development rather than overall limb size. Full article

Osteoarthritis (OA) is a progressive musculoskeletal disorder that affects not only older adults but also younger populations, often leading to chronic pain, joint stiffness, functional impairment, and a decline in quality of life. Non-invasive physical rehabilitation plays a critical role in slowing disease progression, alleviating symptoms, and maintaining joint mobility. However, rehabilitation tools such as compression gloves and manual exercise aids are typically passive and provide minimal real-time feedback to patients or clinicians. Others, such as exoskeletons and soft-actuated devices, can be costly or complex to use. This study presents the design and development of an electrically actuated glove integrated with force and flex sensors, intended to assist individuals diagnosed with Stage 2 OA in performing guided finger exercises. The system integrates a digital front-end application that offers real-time feedback and data visualization, enabling more personalized and trackable therapy sessions for both patients and healthcare providers. Preliminary results from an initial human trial with healthy participants demonstrate that the glove enables naturalistic movement without imposing excessive restriction or augmentation of motion. These findings support the glove’s potential in preserving hand coordination and dexterity, key objectives in early-stage OA intervention, and suggest its suitability for integration into home-based or clinical rehabilitation protocols. Full article

This paper investigates the effect of tactile feedback on the power efficiency and timing of controlling a remote robotic arm using a custom-built haptic glove. The glove integrates flex sensors to monitor finger movements and vibration motors to provide tactile feedback to the user. Communication with the robotic arm is established via the ESP-NOW protocol using an Arduino Nano ESP32 microcontroller (Arduino, Turin, Italy). This study examines the impact of tactile feedback on task performance by comparing precision, completion time, and power efficiency in object manipulation tasks with and without feedback. Experimental results demonstrate that tactile feedback significantly enhances the user’s control accuracy, reduces task execution time, and enables the user to control hand movement during object grasping scenarios precisely. It also highlights its importance in teleoperation systems. These findings have implications for improving human–robot interaction in remote manipulation scenarios, such as assistive robotics, remote surgery, and hazardous environment operations. Full article

This research presents a novel, real-time Pakistani Sign Language (PSL) recognition system utilizing a custom-designed sensory glove integrated with advanced machine learning techniques. The system aims to bridge communication gaps for individuals with hearing and speech impairments by translating hand gestures into readable text. At the core of this work is a smart glove engineered with five resistive flex sensors for precise finger flexion detection and a 9-DOF Inertial Measurement Unit (IMU) for capturing hand orientation and movement. The glove is powered by a compact microcontroller, which processes the analog and digital sensor inputs and transmits the data wirelessly to a host computer. A rechargeable 3.7 V Li-Po battery ensures portability, while a dynamic dataset comprising both static alphabet gestures and dynamic PSL phrases was recorded using this setup. The collected data was used to train two models: a Support Vector Machine with feature extraction (SVM-FE) and a Long Short-Term Memory (LSTM) deep learning network. The LSTM model outperformed traditional methods, achieving an accuracy of 98.6% in real-time gesture recognition. The proposed system demonstrates robust performance and offers practical applications in smart home interfaces, virtual and augmented reality, gaming, and assistive technologies. By combining ergonomic hardware with intelligent algorithms, this research takes a significant step toward inclusive communication and more natural human–machine interaction. Full article

Due to their limited mobility and vocal limitations, paralysed individuals frequently struggle with communication and health monitoring. This work introduces an Internet of Things (IoT)-based system that combines continuous health monitoring with a sensor-based smart glove to enhance patient care. The glove detects falls, sends emergency messages via hand gestures, and monitors vital indicators, including SpO₂, heart rate, and body temperature. The smart glove uses Arduino UNO (RoboCraze, Bengaluru, India) and ESP8266 (RoboCraze, Bengaluru, India) modules with MPU6050 (RoboCraze, Bengaluru, India), MAX30100 (RoboCraze, Bengaluru, India), LM35 (Bombay Electronics, Mumbai, India), and flex sensors for these functions. MPU6050 detects falls precisely, while MAX30100 and flex sensors measure gestures, SpO₂, heart rate, and body temperature. The flex sensor interprets hand motions as emergency alerts sent via Wi-Fi to a cloud platform for remote monitoring. The experimental results confirmed the superiority and validated the efficacy of the suggested module. Scalability, data logging, and real-time access are guaranteed by IoT integration. The actual test cases were predicted using a Support Vector Machine, achieving an average accuracy of 81.98%. The suggested module is affordable, non-invasive, easy to use, and appropriate for clinical and residential use. The system meets the essential needs of disabled people, enhancing both their quality of life and carer connectivity. Advanced machine learning for dynamic gesture detection and telemedicine integration is a potential future improvement. Full article

Home-based rehabilitation supports neuromuscular patients while minimising the need for extensive clinical supervision. Due to a growing number of stroke survivors, this approach appears to be more practical for patients across diverse demographics. Although existing hardware-based assistive devices are pretty common, they have limitations in terms of usability, wearability, and safety, as well as other technical constraints such as bulkiness and torque-to-weight ratios. To overcome these issues, soft actuator–based assistance prioritises user safety and ergonomics, as it is more wearable and lightweight, offering overall support while reducing the social stigma associated with disability. Among the existing soft actuation techniques and related materials, shape memory alloys (SMA) present a feasible option, offering current-controlled actuation and compatibility with integration into flexible textiles, thereby enhancing the wearability of the device. This paper presents a compact, SMA-driven assistive device designed to support natural motion, reduce muscle fatigue, and enable daily therapy. Embedded in a glove, the device allows mirrored control, where one hand’s movement assists the other, using flex sensors for feedback. The functionality of the electromyography (EMG) sensor is also used to evaluate the activation of the SMA wire; however, it is not employed for detecting individual finger motions in the assistive hand. Polyurethane foam insulation minimises thermal effects while maintaining lightweight wearability. Experimental results demonstrate a reduction in actuation time at higher voltages and the effective lifting of light to moderate weights. The device shows strong potential for affordable, home-based rehabilitation and everyday assistance. Full article

Over the last decade, soft robotic gripper systems, such as grippers, have been used in a variety of applications, particularly in human rehabilitation. This study aims to enhance the rehabilitation process by creating a mirroring system glove for hand paralysis patients due to injury, stroke, hemiplegia, and others. A soft and flexible liquid silicone rubber (LSR) was used to develop and build a pair of gloves to improve comfort and safety compared with rigid rehabilitation equipment. The non-affected hand’s sensory glove, equipped with flex sensors, detects motion by measuring the bending angle at each finger. The other glove uses Arduino and a pneumatic system to help the afflicted hand accomplish training exercises. The new design of a gripper is important for manufacturing gloves that provide acceptable gripping behavior. Full article

Mouthguards are recommended for all sports that may cause injuries to the head and oral cavity. Custom mouthguards, made conventionally in the thermoforming process from ethylene vinyl acetate (EVA), face challenges with thinning at the incisor area during the process. In contrast, additive manufacturing (AM) processes enable the precise reproduction of the dimensions specified in a computer-aided design (CAD) model. The potential use of filament extrusion materials in the fabrication of custom mouthguards has not yet been explored in comparative studies. Our research aimed to compare five commercially available filaments for the material extrusion (MEX) also known as fused deposition modelling (FDM) of custom mouthguards using a desktop 3D printer. Samples made using Copper 3D PLActive, Spectrum Medical ABS, Braskem Bio EVA, DSM Arnitel ID 2045, and NinjaFlex were compared to EVA Erkoflex, which served as a control sample. The samples underwent tests for ultimate tensile strength (UTS), split Hopkinson pressure bar (SHPB) performance, drop-ball impact, abrasion resistance, absorption, and solubility. The results showed that Copper 3D PLActive and Spectrum Medical ABS had the highest tensile strength. DSM Arnitel ID 2045 had the highest dynamic property performance, measured with the SHPB and drop-ball tests. On the other hand, NinjaFlex exhibited the lowest abrasion resistance and the highest absorption and solubility. DSM Arnitel ID 2045’s absorption and solubility levels were comparable to those of EVA, but had significantly lower abrasion resistance. Ultimately, DSM Arnitel ID 2045 is recommended as the best filament for 3D-printing mouthguards. The properties of this biocompatible material ensure high-impact energy absorption while maintaining low fluid sorption and solubility, supporting its safe intra-oral application for mouthguard fabrication. However, its low abrasion resistance indicated that mouthguards made from this material may need to be replaced more frequently. Full article

This paper presents a comparative study for detecting the activities of daily living (ADLs) using two distinct sensor systems: the FlexTail wearable spine tracker and a camera-based pose estimation model. We developed a protocol to simultaneously record data with both systems and capture eleven activities from general movement, household, and food handling. We tested a comprehensive selection of state-of-the-art time series classification algorithms. Both systems achieved high classification performance, with average $F_1$ scores of 0.90 for both datasets using a 1-second time window and the random dilated shapelet transform (RDST) and QUANT classifier for FlexTail and camera data, respectively. We also explored the impact of hierarchical activity grouping and found that while it improved classification performance in some cases, the benefits were not consistent across all activities. Our findings suggest that both sensor systems recognize ADLs. The FlexTail model performs better for detecting sitting and transitions, like standing up, while the camera-based model is better for activities that involve arm and hand movements. Full article

Raynaud’s Phenomenon (RP), characterized by episodic reductions in peripheral blood flow, leads to significant discomfort and functional impairment. Existing therapeutic strategies focus on pharmacological treatments, external heat supplementation and exercise-based rehabilitation, but fail to address biomechanical contributions to vascular dysfunction. We introduce a computational approach rooted in topological transformations of hand prehension, hypothesizing that specific hand postures can generate transient geometric structures that enhance thermal and hemodynamic properties. We examine whether a flexed hand posture—where fingers are brought together to form a closed-loop toroidal shape—may modify heat transfer patterns and blood microcirculation. Using a combination of heat diffusion equations, fluid dynamics models and topological transformations, we implement a heat transfer and blood flow simulation to examine the differential thermodynamic behavior of the open and closed hand postures. We show that the closed-hand posture may preserve significantly more heat than the open-hand posture, reducing temperature loss by an average of 1.1 ± 0.3 °C compared to 3.2 ± 0.5 °C in the open-hand condition (p < 0.01). Microvascular circulation is also enhanced, with a 53% increase in blood flow in the closed-hand configuration (p < 0.01). Therefore, our findings support the hypothesis that maintaining a closed-hand posture may help mitigate RP symptoms by preserving warmth, reducing cold-induced vasoconstriction and optimizing peripheral flow. Overall, our topologically framed approach provides quantitative evidence that postural modifications may influence peripheral vascular function through biomechanical and thermodynamic mechanisms, elucidating how shape-induced transformations may affect physiological and pathological dynamics. Full article

Background: In reverse shoulder arthroplasty (RSA), the neck–shaft angle (NSA) has trended downward from 155° to 135° to reduce scapular notching, but concerns about instability persist. To assess superior–lateral stability, we developed the intraoperative two-hand lever test (2HLT). The primary objective was to evaluate the effectiveness of the 2HLT, analyze the learning curve in this first study reporting on the new Perform stem, and compare the liner characteristics of 155° and 135° systems. Methods: In a single-surgeon learning curve study, 81 RSA procedures with the new Perform stem (Stryker) were included. The outcomes included the 2HLT test applied in 65 cases, early dislocations, stem alignment, stem length, liner type/thickness, and complications. The early dislocation rate was compared to 167 prior Ascend Flex RSA procedures (Stryker). The liner characteristics of three 135° systems (Perform/Stryker, Univers/Arthrex, and Altivate/Enovis) were compared to traditional 155° Grammont systems (Delta Xtend/DePuy, Affinis Metal/Mathys, SMR 150/Lima, and Aequalis Reversed/Stryker), focusing on jump height (JH) and the liner stability ratio (LSR). Results: In 63% (31/49) of the cases, the 2HLT detected superior–lateral instability, necessitating a retentive 135° liner. The early dislocation rate in the Perform cohort was 4.9% (0% for retentive liners, 8% for standard liners) versus 0% in the Ascend Flex cohort. The mean effective NSA was 133° (127–144°) for short Perform stems and 135° (129–143°) for long stems. Long Perform stems significantly reduced varus outlier density below 132° and 130° (p = 0.006, 0.002). The 36 mm Perform 135° standard liner has a JH of 8.1 mm and an LSR of 152%, markedly lower than the Altivate (10.0 mm/202%) and Univers (9.7 mm/193%) and similar to traditional 155° Grammont liners (8.1–8.9 mm/147–152%). Perform retentive liners have LSR values of 185–219%, comparable to the established 135° design standard liners (195–202%). In the Perform cohort, early complications included four superior–lateral dislocations (all standard liners, LSR 147–152%) requiring four revisions. Conclusions: Perform standard liners have a lower LSR than the established 135° designs. Retentive Perform liners (LSR > 184%) are comparable to standard liners of established 135° designs and effectively mitigate instability. We recommend discontinuing non-retentive Perform standard liners (NSA 135°, LSR < 158%) due to the 63% superior–lateral instability rate detected with the novel 2HLT, necessitating retentive liners, the documented LSR-NSA implant mismatch, and an early clinical dislocation rate of up to 8%. Full article

This study investigates the mutual interaction between self- and partner-induced actions in determining pressure distribution during a handshake and proposes a tele haptic handshake system based on these findings. To achieve this, experiments were conducted to examine how pressure distribution in face-to-face handshakes is influenced by mutual actions. Based on the experimental results, an interaction force model was developed to calculate stimulus intensities, incorporating region-specific weights for different parts of the hand. Additionally, a tele haptic handshake system was designed, integrating flex sensors to measure finger joint angles and a distributed haptic stimulus presentation device to provide tactile feedback. While this study lays the foundation for understanding the dynamics of handshake interactions and their application in remote environments, further validation of the system’s effectiveness in replicating real-world handshake experiences remains a subject for future work. Full article

Hand−tracking systems are widely employed for telemanipulating grippers with high degrees of freedom (DOFs) such as an anthropomorphic robotic hand (ARH). However, tracking human thumb motion is challenging due to the complex motion of the carpometacarpal (CMC) joint. Existing hand−tracking systems can track the motion of simple joints with one DOF, but most fail to track the motion of the CMC joint, or to do so, there is a need for expensive and intricately set up hardware systems. This research introduces and realizes an affordable and personalizable tracking device to capture the CMC joint Flexion/Extension and Abduction/Adduction motions. Tracked human thumb motion is mapped to a robot thumb in a hybrid approach: the proposed algorithm maps the CMC joint motion to the first two joints of the robot thumb, while joint mapping is established between the metacarpophalangeal and interphalangeal joints to the last two joints. When the tracking device is paired with a flex glove outfitted with bend sensors, the developed system provides the means to telemanipulate an ARH with a four-DOF thumb and one-DOF underactuated digits. A three-stage framework is proposed to telemanipulate the fully actuated robot thumb. The tracking device and framework were evaluated through a device operation and personalization test, as well as a framework verification test. Two volunteers successfully personalized, calibrated, and tested the device using the proposed mapping algorithm. One volunteer further evaluated the framework by performing hand poses and grasps, demonstrating effective control of the robot thumb for precision and power grasps in coordination with the other digits. The successful results support expanding the system and further evaluating it as a research platform for studying human–robot interaction in grasping tasks or in manufacturing, assistive, or medical domains. Full article

Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove’s soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to $85.3\%$, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient’s attempted movements using pure thinking through a non-intrusive brain–computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings. Full article

Wearable motion capture gloves enable the precise analysis of hand and finger movements for a variety of uses, including robotic surgery, rehabilitation, and most commonly, virtual augmentation. However, many motion capture gloves restrict natural hand movement with a closed-palm design, including fabric over the palm and fingers. In order to alleviate slippage, improve comfort, reduce sizing issues, and eliminate movement restrictions, this paper presents a new low-cost data glove with an innovative open-palm and finger-free design. The new design improves usability and overall functionality by addressing the limitations of traditional closed-palm designs. It is especially beneficial in capturing movements in fields such as physical therapy and robotic surgery. The new glove incorporates resistive flex sensors (RFSs) at each finger and an inertial measurement unit (IMU) at the wrist joint to measure wrist flexion, extension, ulnar and radial deviation, and rotation. Initially the sensors were tested individually for drift, synchronisation delays, and linearity. The results show a drift of 6.60°/h in the IMU and no drift in the RFSs. There was a 0.06 s delay in the data captured by the IMU compared to the RFSs. The glove’s performance was tested with a collaborate robot testing setup. In static conditions, it was found that the IMU had a worst case error across three trials of 7.01° and a mean absolute error (MAE) averaged over three trials of 4.85°, while RFSs had a worst case error of 3.77° and a MAE of 1.25° averaged over all five RFSs used. There was no clear correlation between measurement error and speed. Overall, the new glove design proved to accurately measure joint angles. Full article