Search Results (54)

Upper limb disabilities resulting from stroke affect millions worldwide, yet current rehabilitation systems face limitations in portability, cost-effectiveness, and multi-joint integration. This study presents a cable-driven parallel exoskeleton integrating elbow, wrist, and finger assistance into a single portable device. The design strategically separates actuation components, housing all motors in a backpack unit, while limb-mounted modules serve as cable routing guides, achieving seven degrees of freedom within practical constraints of portability (1.2–1.5 kg) and cost-effectiveness (3D-printed components). The device incorporates seven servo motors controlled via Arduino with IMU feedback and PID algorithms. Kinematic and dynamic analyses informed mechanical design, while ARMAX system identification enabled controller optimization achieving 87.96% model fit. Experimental validation with eight healthy participants performing four upper limb exercises demonstrated consistent trends toward reduced activation in four monitored agonist muscles with exoskeleton assistance (21.3% average reduction, p = 0.087), with moderate effect sizes for proximal muscles (Cohen’s d = 0.70–0.79) and significant reductions in brachioradialis during radial/ulnar deviation (23.4%, p = 0.045). These findings provide preliminary evidence of the device’s potential to reduce muscular effort during assisted movements, warranting further clinical validation with patient populations. Full article

Schmitt Triggers are essential building blocks in noise-resilient systems and are useful in managing switching behavior in low-power designs. Yet, as CMOS technologies scale down, their designs become increasingly challenging. This paper presents a comprehensive investigation into the performance and reliability of multiple Schmitt Trigger topologies across two CMOS technology nodes (180 nm and 45 nm), with a particular focus on transistor sizing and layout optimization through multi-finger transistor structures. A series of pre-layout and post-layout simulations reveal that fingered implementations significantly enhance hysteresis robustness, switching speed, and delay consistency in PVT variations. Notably, post-layout results in 45 nm technology demonstrate remarkable improvements in both speed and power efficiency. This highlights the inadequacy of schematic-level models to predict the true behavior of fingered transistor configurations. Additionally, we explored the implications of finger designs on reliability concerns including electromigration and IR drop to determine the tradeoff between interconnect reliability optimization and internal routing. The findings establish practical design guidelines for optimizing number of fingers based on device width and technology node, offering new insights into layout-aware Schmitt Trigger design for high-performance and area-constrained applications. Full article

Cardiovascular disease (CVD) remains the leading cause of global mortality, highlighting the need for continuous vital sign monitoring. Photoplethysmography (PPG) is well suited for wearable devices. Smart rings, benefiting from dense capillary distribution and minimal tissue interference, can capture high-quality PPG signals with comfort, making them a promising next-generation wearable. However, ring rotation relative to the finger alters the optical path, especially for multi-wavelength light, thus reducing accuracy. This paper proposes a rotation-robust PPG sensor for smart rings. Monte Carlo simulations analyze photon transmission under different LED–photodiode (PD) angles, showing that at ±60°, green, red, and infrared light achieve optimal penetration into the microcirculation layer. Considering non-ideal conditions, the green-light angle is adjusted to ±30°, and a symmetrical sensor design is adopted. A prototype smart ring is developed, capable of recording 4-channel PPG, 3-axis acceleration, and 4-channel temperature signals at 100, 25, and 0.2 Hz, respectively. The system achieves reliable PPG acquisition with only 0.59 mA average current consumption. In continuous testing, heart rate estimation reached mean absolute errors of 0.82, 0.79, and 0.78 bpm for green, red, and IR light. The results provide a reference for future smart ring development. Full article

Tracking hand kinematics is essential for numerous clinical and scientific applications. Markerless motion capture devices have advantages over other modalities in terms of calibration, set up, and overall ease of use; however, their accuracy during dynamic tasks has not been fully explored. This study examined the performance of two popular markerless systems, the Leap Motion Controller 2 (LM2) and MediaPipe (MP), in capturing joint motion of the digits. Data were compared to joint motion collected from a marker-based multi-camera system (Vicon). Eleven participants performed six tasks with their dominant right hand at three movement speeds while all three devices simultaneously captured the position of hand landmarks. Using these data, digit joint angles were calculated. The root mean squared error (RMSE) and correlation coefficient (r) relative to the Vicon angles were computed for LM2 and MP. LM2 achieved a lower error (p < 0.001, mean RMSE = 14.8°) and a higher correlation (p = 0.007, mean r = 0.58) than the MP system (mean RMSE = 22.5°, mean r = 0.45). Greater movement speed led to significantly higher RMSE (p < 0.001) and lower r (p < 0.001) for MP but not for LM2. Error was substantially greater for the proximal interphalangeal joint than for other finger joints, although r values were higher for this joint. Overall, the LM2 and MP systems were able to capture motion at the joint level across digits for a variety of tasks in real time, although the level of error may not be acceptable for certain applications. Full article

Monitoring forearm muscle contraction force in home-based rehabilitation remains challenging. Electromyography (EMG), as a standard technique, is considered impractical and complex for independent use by patients at home, which poses a risk of device misattachment and inaccurate recorded data. Considering the muscle-related modality, several studies have demonstrated an excellent correlation between stretch sensors and EMG, which provides significant potential for addressing the monitoring issue at home. Additionally, due to its flexible nature, it can be attached to the finger, which facilitates the logging of the kinematic mechanisms of a finger. This study proposes a method for estimating forearm muscle contraction in a cylinder grasping environment during home-based rehabilitation using a stretch-sensor glove. This study employed support vector machine (SVM), multi-layer perceptron (MLP), and random forest (RF) to construct the estimation model. The root mean square (RMS) of the EMG signal, representing the muscle contraction force, was collected from 10 participants as the target learning for the stretch-sensor glove. This study constructed an experimental design based on a home-based therapy protocol known as the graded repetitive arm supplementary program (GRASP). Six cylinders with varying diameters and weights were employed as the grasping object. The results demonstrated that the RF model achieved the lowest root mean square error (RMSE) score, which differed significantly from the SVM and MLP models. The time series waveform comparison revealed that the RF model yields a similar estimation output to the ground truth, which incorporates the contraction–relaxation phases and the muscle’s contraction force. Additionally, despite the subjectivity of the participants’ grasping power, the RF model could produce similar trends in the muscle contraction forces of several participants. Utilizing a stretch-sensor glove, the proposed method demonstrated great potential as an alternative modality for monitoring forearm muscle contraction force, thereby improving the practicality for patients to self-implement home-based rehabilitation. Full article

Wearable sensors have rapidly advanced, enabling applications such as human activity monitoring, electronic skin, and biomimetic robotics. To meet the growing demands of these applications, multifunctional sensing has become essential for wearable devices. However, most existing studies predominantly focus on enhancing single-function sensing capabilities. This study introduces a multifunctional sensor that combines high stretchability for strain and pressure detection with ultraviolet (UV) sensing capability. To achieve simultaneous detection of strain, pressure, and UV light, a multi-sensing approach was employed: a capacitive method for strain and pressure detections and a resistive method utilizing a pn-heterojunction diode for UV detection. In the capacitive method, polyaniline (PANI) served as parallel-plate electrodes, while silicon-based elastomer acted as the dielectric layer. This configuration enabled up to 100% elongation and enhanced operational stability through encapsulation. The sensor demonstrated a strong linear relationship between capacitance value changes reasonably based on the area of PANI, and showed a good linearity with an R-squared value of 0.9918. It also detected pressure across a wide range, from low (0.4 kPa) to high (9.4 kPa). Furthermore, for wearable applications, the sensor reliably captured capacitance variations during finger bending at different angles. For UV detection, a pn-heterojunction diode composed of p-type silicon and n-type zinc oxide nanorods exhibited a rapid response time of 6.1 s and an on/off ratio of 13.8 at −10 V. Durability under 100% tensile strain was confirmed through Von Mises stress calculations using finite element modeling. Overall, this multifunctional sensor offers significant potential for a variety of applications, including human motion detection, wearable technology, and robotics. Full article

Mechanized chili harvesting is essential for improving efficiency, reducing costs, and alleviating labor intensity in production. However, issues such as low harvesting efficiency, high rates of breakage, and contamination continue to severely hinder the development of mechanized chili harvesting. This study provides an overview of global chili production regions and varieties, examining three harvesting approaches: single-pass, multi-stage, and multi-pass approaches. It describes the operational principles of key harvesting mechanisms, including the helical spiral-type, drum finger-type, long-rod comb-type, and belt-mounted comb finger-type mechanisms, and summarizes research progress in major producing countries, such as the United States and China. The paper evaluates both airflow-based and mechanical cleaning–separation devices, highlighting the combined airflow mechanical systems as the most promising approach and reviews their current development status. It also addresses structural challenges in chassis, frameworks, and conveyance systems. Finally, the paper analyzes solutions to the existing challenges, emphasizing the integration of intelligent technologies to resolve mechanical issues, and outlines the future prospects of intelligent development in mechanized chili harvesting. Full article

This paper presents a wide-tuning-range, low-power tunable active inductor (AI) designed and fabricated using 130 nm CMOS technology with six metal layers. To achieve high performance with a relatively small silicon area and low power consumption, the AI structure is carefully designed and optimized using a cascode stage, a feedback resistor, and multi-gate finger transistors. In the proposed circuit topology, inductance tuning is realized by adjusting both the bias current and the feedback resistor. The performance of the circuit is evaluated in terms of tuning range, quality factor, power consumption, and chip area. The functionality of the fabricated device is experimentally validated, and the fundamental characteristics of the active inductor are measured over a wide frequency range using a Cascade GSG probe, with results compared to simulations. Experimental measurements show that, under a 1 V supply, the AI achieves a self-resonant frequency (SRF) of 3.961 GHz and a quality factor (Q) exceeding 1586 at 2.383 GHz. The inductance is tunable between 6.7 nH and 84.4 nH, with a total power consumption of approximately 2 mW. The total active area, including pads, is 345 × 400 µm². Full article

Sorting particles or cells of specific sizes in complex systems has long been a focus of many researchers. Acoustic surface waves, which generate acoustic radiation forces on particles or cells and, thus, influence their motion, are commonly used for the non-destructive separation of particles or cells of specific sizes. In previous studies, the frequency of acoustic surface wave generation has been limited by the interdigitated transducer (IDT). To extend the effective operating frequency range of the IDT, a slanted-finger interdigitated transducer (SFIT) with a wide acoustic path and multiple operating frequencies was designed. Compared with traditional acoustic sorting devices, which suffer from a limited frequency range and narrow acoustic paths, this new design greatly expands both the operating frequency range and acoustic path width, and enables adjustable operating frequencies, providing a solution for sorting particles or cells with uneven sizes in complex environments. The optimal resonance frequency is distributed within the 32–42 MHz range, and the operating frequencies within this range can generate a standing wave acoustic path of approximately 200 $\mathsf{\mu }$m, thus enhancing the effectiveness of the operating frequencies. The microfluidic sorting device based on SFIT can efficiently and accurately sort polystyrene (PS) with particle sizes of 20 $\mathsf{\mu }$m, 30 $\mathsf{\mu }$m, and 50 $\mathsf{\mu }$m from mixed PS microspheres (5, 10, 20 $\mathsf{\mu }$m), (5, 10, 30 $\mathsf{\mu }$m), and (5, 10, 50 $\mathsf{\mu }$m), with a sorting efficiency and purity exceeding 96%. Additionally, the device is capable of sorting other types of mixed microspheres (5, 10, 20, 30, 50 $\mathsf{\mu }$m). This new wide-acoustic-path, multi-frequency sorting device demonstrates the ability to sort particlesin a high-purity, label-free manner, offering a more alternative to traditional sorting methods. Full article

Sign language plays a crucial role in bridging communication barriers within the Deaf community. Recognizing sign language sentences remains a significant challenge due to their complex structure, variations in signing styles, and temporal dynamics. This study introduces an innovative sign language sentence recognition (SLSR) approach using Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) specifically developed for single-handed wearable glove devices. The system relies on sensor data from a glove with six-axis inertial sensors and five-finger curvature sensors. The proposed HGE-ACN framework integrates graph-based embeddings to capture dynamic spatial–temporal relationships in motion and curvature data. At the same time, the Adaptive Convolutional Networks extract robust glove-based features to handle variations in signing speed, transitions between gestures, and individual signer styles. The lightweight design enables real-time processing and enhances recognition accuracy, making it suitable for practical use. Extensive experiments demonstrate that HGE-ACN achieves superior accuracy and computational efficiency compared to existing glove-based recognition methods. The system maintains robustness under various conditions, including inconsistent signing speeds and environmental noise. This work has promising applications in real-time assistive tools, educational technologies, and human–computer interaction systems, facilitating more inclusive and accessible communication platforms for the deaf and hard-of-hearing communities. Future work will explore multi-lingual sign language recognition and real-world deployment across diverse environments. Full article

Exoskeletons play a crucial role in joint healthcare by providing targeted support and rehabilitation for individuals with musculoskeletal diseases. As an assistive device, the accurate monitoring of the user’s joint signals and exoskeleton status using wearable sensors is essential to ensure the efficiency of conducting complex tasks in various scenarios. However, balancing sensitivity and stretchability in wearable devices for exoskeleton applications remains a significant challenge. Here, we introduce a wearable strain sensor for detecting finger and knee joint motions. The sensor utilizes a stretchable elastic conductive network, incorporating multi-walled carbon nanotubes (MWCNTs) into Ecoflex. The concentration of MWCNTs has been meticulously optimized to achieve both a high gauge factor (GF) and stability. With its high sensitivity, the sensor is enabled to be applied in the angle monitoring of finger joints. By integrating the sensor with human knee joints and an exoskeleton device, it can simultaneously detect the flexion and extension movements in real-time. This sensor holds significant potential for enhancing exoskeleton performance and improving joint healthcare technologies. Full article

This study designs and develops a wearable exoskeleton piano assistance system for individuals recovering from neurological injuries, aiming to help users regain the ability to perform complex tasks such as playing the piano. While soft robotic exoskeletons have proven effective in rehabilitation therapy and daily activity assistance, challenges remain in performing highly dexterous tasks due to structural complexity and insufficient motion accuracy. To address these issues, we developed a modular division method based on multi-domain mapping and a top-down process model. This method integrates the functional domain, structural domain, and user needs domain, and explores the principles and methods for creating functional construction modules, overcoming the limitations of traditional top-down approaches in design flexibility. By closely combining layout constraints with the design model, this method significantly improves the accuracy and efficiency of module configuration, offering a new path for the development of piano practice assistance devices. The results demonstrate that this device innovatively combines piano practice with rehabilitation training and through the introduction of ontological modeling methods, resolves the challenges of multidimensional needs mapping. Based on five user requirements (P), we calculated the corresponding demand weight (K), making the design more aligned with user needs. The device excels in enhancing motion accuracy, interactivity, and comfort, filling the gap in traditional piano assistance devices in terms of multi-functionality and high adaptability, and offering new ideas for the design and promotion of intelligent assistive devices. Simulation analysis, combined with the motion trajectory of the finger’s proximal joint, calculates that 60° is the maximum bending angle for the aforementioned joint. Physical validation confirms the device’s superior performance in terms of reliability and high-precision motion reproduction, meeting the requirements for piano-assisted training. Through multi-domain mapping, the top-down process model, and modular design, this research effectively breaks through the design flexibility and functional adaptability bottleneck of traditional piano assistance devices while integrating neurological rehabilitation with music education, opening up a new application path for intelligent assistive devices in the fields of rehabilitation medicine and arts education, and providing a solution for cross-disciplinary technology fusion and innovative development. Full article

The realization of hand function reengineering using a manipulator is a research hotspot in the field of robotics. In this paper, we propose a multimodal perception and control method for a robotic hand to assist the disabled. The movement of the human hand can be divided into two parts: the coordination of the posture of the fingers, and the coordination of the timing of grasping and releasing objects. Therefore, we first used a pinhole camera to construct a visual device suitable for finger mounting, and preclassified the shape of the object based on YOLOv8; then, a filtering process using multi-frame synthesized point cloud data from miniature 2D Lidar, and DBSCAN algorithm clustering objects and the DTW algorithm, was proposed to further identify the cross-sectional shape and size of the grasped part of the object and realize control of the robot’s grasping gesture; finally, a multimodal perception and control method for prosthetic hands was proposed. To control the grasping attitude, a fusion algorithm based on information of upper limb motion state, hand position, and lesser toe haptics was proposed to realize control of the robotic grasping process with a human in the ring. The device designed in this paper does not contact the human skin, does not produce discomfort, and the completion rate of the grasping process experiment reached 91.63%, which indicates that the proposed control method has feasibility and applicability. Full article

Immersive human–machine interaction relies on comprehensive sensing and feedback systems, which enable transmission of multiple pieces of information. However, the integration of increasing numbers of feedback actuators and sensors causes a severe issue in terms of system complexity. In this work, we propose a pressure-sensing and feedback glove that enables multi-dimensional pressure sensing and feedback with a minimalist design of the functional units. The proposed glove consists of modular strain and pressure sensors based on films of liquid metal microchannels and coin vibrators. Strain sensors located at the finger joints can simultaneously project the bending motion of the individual joint into the virtual space or robotic hand. For subsequent tactile interactions, the design of two symmetrically distributed pressure sensors and vibrators at the fingertips possesses capabilities for multi-directional pressure sensing and feedback by evaluating the relationship of the signal variations between two sensors and tuning the feedback intensities of two vibrators. Consequently, both dynamic and static multi-dimensional pressure communication can be realized, and the vibrational actuation can be monitored by a liquid-metal-based sensor via a triboelectric sensing mechanism. A demonstration of object interaction indicates that the proposed glove can effectively detect dynamic force in varied directions at the fingertip while offering the reconstruction of a similar perception via the haptic feedback function. This device introduces an approach that adopts a minimalist design to achieve a multi-functional system, and it can benefit commercial applications in a more cost-effective way. Full article

Conductive multi-walled carbon nanotubes (MWCNTs) as well as piezoelectric zinc oxide (ZnO) nanoparticles are frequently used as a single additive and dispersed in polyvinylidene fluoride (PVDF) solutions for the fabrication of piezoelectric composite films. In this study, MWCNT/ZnO binary dispersions are used as spinning liquids to fabricate composite nanofibrous films by electrospinning. Binary additives are conducive to increasing the crystallinity, piezoelectric voltage coefficient, and consequent piezoelectricity of as-spun films owing to the stretch-enhanced polarization of the electrospinning process under an applied electric field. PCZ–1.5 film (10 wt. % PVDF/0.1 wt. % MWCNTs/1.5 wt. % ZnO nanoparticles) contains the maximum β-phase content of 79.0% and the highest crystallinity of 87.9% in nanofibers. A sensor using a PCZ–1.5 film as a functional layer generates an open-circuit voltage of 10 V as it is subjected to impact loads with an amplitude of 6 mm at 10 Hz. The piezoelectric sensor reaches a power density of 0.33 μW/cm² and a force sensitivity of 582 mV/N. In addition, the sensor is successfully applied to test irregular motions of a bending finger and stepping foot. The result indicates that electrospun PVDF/MWCNT/ZnO nanofibrous films are suitable for wearable devices. Full article