Search Results (911)

Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies. Full article

The development of multifunctional conductive hydrogels with rapid self-healing capabilities and powerful sensing functions is crucial for advancing wearable electronics. This study designed and prepared a polyvinyl alcohol (PVA)–borax hydrogel incorporating carbon nanotubes (CNTs) and biomass carbon nanospheres (CNPs) as dual-carbon fillers. This hydrogel exhibits excellent conductivity, mechanical flexibility, and self-recovery properties. Serving as a highly sensitive piezoresistive sensor, it efficiently converts mechanical stimuli into reliable electrical signals. Sensing tests demonstrate that the CNT/CNP/PVA–borax hydrogel sensor possesses an extremely fast response time (88 ms) and rapid recovery time (88 ms), enabling the detection of subtle and rapid human motions. Furthermore, the hydrogel sensor also exhibits outstanding cyclic stability, maintaining stable signal output throughout continuous loading–unloading cycles exceeding 3200 repetitions. The hydrogel sensor’s characteristics, including rapid self-healing, fast-sensing response/recovery, and high fatigue resistance, make the CNT/CNP/PVA–borax conductive hydrogel an ideal choice for multifunctional wearable sensors. It successfully monitored various human motions. This study provides a promising strategy for high-performance self-healing sensing devices, suitable for next-generation wearable health monitoring and human–machine interaction systems. Full article

Exercise is an important component in the treatment and improvement of function in populations with or at risk of lower limb injury. Cycling is the most common exercise modality used by these populations. However, reduced lower limb joint excursion and/or range of motion (ROM) during cycling might limit the optimization of functional improvements. Increased hip flexion gait (HFgait) is a new exercise modality that might result in larger lower limb joint excursions compared to cycling. The purpose of this study was to compare lower limb kinematics between HFgait and cycling. Twelve healthy individuals participated in the study. Each participant performed cycling and HFgait. Hip, knee, and ankle kinematics in the sagittal, frontal, and transverse planes were analyzed with and without phase offset reduction (POR). Discrete and continuous analyses were performed. Discrete analysis indicated differences for at least one of the variables analyzed (maximum, minimum, and ROM) for the hip (p ≤ 0.041), knee (p ≤ 0.008), and ankle (p ≤ 0.040) across all planes. For the continuous analysis, differences between HFgait and cycling kinematics were observed during the cycles for the hip, knee, and ankle sagittal (hip: original: 85%; with POR: 77%; knee: original: 93%; with POR: 76%; ankle: original: 14%; with POR: 14%), frontal (hip: original: 93%; with POR: 98%; knee: original: 41%; with POR: 12%; ankle: original: 4%; with POR: 5%), and transverse (hip: original: 66%; with POR: 0%; knee: original: 14%; with POR: 0%; ankle: original: 3%; with POR: 0%) planes. HFgait resulted in larger hip (+60.2°) and knee (+38.2°) sagittal plane ROM while maintaining the hip in a more neutral position in the frontal plane compared with cycling. These findings can support the development of rehabilitation strategies with the goal of improving function and joint range of motion while also receiving the health benefits of exercise. Full article

This paper presents the development of a fuzzy-PID control able to adapt to several robot–patient interaction modes by monitoring patient evolution during the rehabilitation procedure. This control system is designed to provide targeted rehabilitation therapy through three interaction modes: passive; active–assistive; and resistive. By integrating a fuzzy inference system into the classical PID architecture, the FPID controller dynamically adjusts control gains in response to tracking error and patient effort. The simulation results indicate that, in passive mode, the FPID controller achieves a 32% lower RMSE, reduced overshoot, and a faster settling time compared to the conventional PID. In the active–assistive mode, the FPID demonstrates enhanced responsiveness and reduced error lag when tracking a sinusoidal reference, while in resistive mode, it more effectively compensates for imposed load disturbances. A rehabilitation scenario simulating repeated motion cycles on a healthy subject further confirms that the FPID controller consistently produces a lower overall RMSE and variability. Full article

This study, in response to the optimization needs of fall risks for the elderly in the context of cultural and tourism integration in Hebei Province, China, established a quantitative correlation system between ten gait parameters and ten types of spatial risk factors. By collecting gait data (Qualisys infrared motion capture system, sampling rate 200 Hz) and spatial parameters from 30 elderly subjects (with mild, moderate, and severe functional impairments), a multi-level regression model was established. This study revealed that step frequency, step width, and step length were nonlinearly associated with corridor length, door opening width, and step depth (R² = 0.53–0.68). Step speed, ankle dorsiflexion, and foot pressure were key predictive factors (OR = 0.04–8.58, p < 0.001), driving the optimization of core spatial factors such as threshold height, handrail density, and friction coefficient. Step length, cycle, knee angle, and lumbar moment, respectively, affected bed height (45–60 cm), switch height (1.2–1.4 m), stair riser height (≤35 mm), and sink height adjustment range (0.7–0.9 m). The prediction accuracy of the ten optimized values reached 86.7% (95% CI: 82.1–90.3%), with Hosmer–Lemeshow goodness-of-fit x² = 7.32 (p = 0.412) and ROC curve AUC = 0.912. Empirical evidence shows that the graded optimization scheme reduced the fall risk by 42–85%, and the estimated fall incidence rate decreased by 67% after the renovation. The study of the “abnormal gait—spatial threshold—graded optimization” quantitative residential layout optimization provides a systematic solution for the data-quantified model of elderly-friendly residential renovations. Full article

Triboelectric nanogenerators (TENGs) hold significant potential for decentralized energy harvesting; however, their dependence on rotational mechanical energy often limits their ability to harness ubiquitous horizontal motion in real-world applications. Here, a single horizontal linear-to-rotational triboelectric nanogenerator (SHLR-TENG) is presented, designed to efficiently convert linear motion into rotational energy using a robust gear system, enabling a high voltage and reliable full cycle of alternating current (AC). The device features a radially patterned disk with triboelectric layers composed of polyimide. The SHLR-TENG achieves a peak-to-peak voltage of 1420 V, a short-circuit current of 117 µA, and an average power output of 41.5 mW, with a surface charge density of 110 µC/m². Moreover, it demonstrates a power density per unit volume of 371.2 W·m⁻³·Hz⁻¹. The device retains 80% efficiency after 1.5 million cycles, demonstrating substantial durability under mechanical stress. These properties enable the SHLR-TENG to directly power commercial LEDs and low-power circuits without the need for energy storage. This study presents an innovative approach to sustainable energy generation by integrating horizontal motion harvesting with rotational energy conversion. The compact and scalable design of the SHLR-TENG, coupled with its resilience to humidity (20–90% RH) and temperature fluctuations (10–70 °C), positions it as a promising next-generation energy source for Internet of Things (IoT) devices and autonomous systems. Full article

This study investigates a stochastic production planning problem with regime-switching parameters, inspired by economic cycles impacting production and inventory costs. The model considers types of goods and employs a Markov chain to capture probabilistic regime transitions, coupled with a multidimensional Brownian motion representing stochastic demand dynamics. The production and inventory cost optimization problem is formulated as a quadratic cost functional, with the solution characterized by a regime-dependent system of elliptic partial differential equations (PDEs). Numerical solutions to the PDE system are computed using a monotone iteration algorithm, enabling quantitative analysis. Sensitivity analysis and model risk evaluation illustrate the effects of regime-dependent volatility, holding costs, and discount factors, revealing the conservative bias of regime-switching models when compared to static alternatives. Practical implications include optimizing production strategies under fluctuating economic conditions and exploring future extensions such as correlated Brownian dynamics, non-quadratic cost functions, and geometric inventory frameworks. In contrast to earlier studies that imposed static or overly simplified regime-switching assumptions, our work presents a fully integrated framework—combining optimal control theory, a regime-dependent system of elliptic PDEs, and comprehensive numerical and sensitivity analyses—to more accurately capture the complex stochastic dynamics of production planning and thereby deliver enhanced, actionable insights for modern manufacturing environments. Full article

Background: Marker-based motion capture is the current gold standard for three-dimensional (3D) gait analysis. This is a highly technical analysis that is time-consuming, and marker application can trigger anxiety in children. One potential solution is to use markerless camera systems instead. The objective of this study was to compare 3D lower limb gait kinematics in children using both marker-based and markerless motion capture methods. Methods: Ten typically developing children (age 6–13 yrs) completed five barefoot walks at a self-selected speed. A 10-camera marker-based system (Oqus, Qualisys) and a 7-camera markerless system (Miqus, Qualisys) captured synchronised gait data at 85 Hz. Generalised Additive Mixed Models were fitted to the data to identify the random effects of measurement systems, age, and time across the gait cycle. The root-mean-square difference (RMSD) was used to compare the differences between systems. Results: Significant interactions and differences were observed between the marker-based and markerless systems for most joint angles and planes of motion, particularly with regard to time and age. Conclusions: Despite differences across all kinematic profiles, the RMSD in this study was comparable to previously published results. Alternative model definitions and kinematic crosstalk in both systems likely explain the differences. Age differences were not consistent across joint levels, suggesting a larger sample size is required to determine how maturation may affect markerless tracking. Further investigation is required to understand the deviations and differences between systems before implementing markerless technology in a clinical setting. Full article

Background/Objectives: This study focuses on the motion planning and control of an active ankle–foot orthosis (AFO) that leverages biomechanical insights to mitigate footdrop, a deficit that prevents safe toe clearance during walking. Methods: To adapt the motion of the device to the user’s walking speed, a geometric model was used, together with real-time measurement of the user’s gait cycle. A geometric speed-adaptive model also scales a trapezoidal ankle-velocity profile in real time using the detected gait cycle. The algorithm was tested at three different walking speeds, with a prototype of the AFO worn by a test subject. Results: At walking speeds of 0.44 and 0.61 m/s, reduced tibialis anterior (TA) muscle activity was confirmed by electromyography (EMG) signal measurement during the stance phase of assisted gait. When the AFO was in assistance mode after toe-off (initial and mid-swing phase), it provided an average of 48% of the estimated required power to make up for the deliberate inactivity of the TA muscle. Conclusions: Kinematic analysis of the motion capture data showed that sufficient foot clearance was achieved at all three speeds of the test. No adverse effects or discomfort were reported during the experiment. Future studies should examine the device in populations with footdrop and include a comprehensive evaluation of safety. Full article

This study aimed to (1) determine and compare the magnitude and direction of asymmetry in lower limbs neuromuscular properties, range of motion, strength and muscle electrical activity (EMG) in well-trained male road cyclist across categories (elite, under-23 and junior); (2) establish test- and age-specific asymmetry thresholds for these variables to enable individualized classification; and (3) examine the relationship between these lateral asymmetries and performance in a maximal incremental cycle ergometer test. Fifty-five well-trained road cyclists were assessed through tensiomyography (TMG), active knee extension test (AKE), leg press and EMG of vastus lateralis (VL-EMG) during a maximal incremental cycling test. Junior cyclists showed lower asymmetry in VM than elite cyclists, but greater asymmetry in AKE. No significant differences were found in strength or VL-EMG during the maximal incremental cycle ergometer test. The magnitude and direction of lateral asymmetry differs between tests (TMG: 11.3–21.3%; AKE: 2.3%; leg-press: 9.8–31.9%; VL-EMG: 20.8–22.7%). Multiple linear regression revealed a significant predictive model for maximal incremental cycling ergometer performance based on lateral asymmetry in AKE, leg press and VL and rectus femoris contraction time (R²_a = 0.23). These reference data can support trainers in monitoring and managing lateral asymmetry throughout the cyclists’ season. Full article

Legged robots face inherent challenges in energy efficiency and stability at high speeds due to the repetitive acceleration–deceleration cycles of swing-based locomotion. To address these limitations, this paper presents a motion strategy that uses rolling gait instead of swing gait to improve the energy efficiency and stability. First, a wheel-legged quadruped robot, R-Taichi, is developed, which is capable of switching to legged, wheeled, and RHex mobile modes. Second, the mechanical structure of the transformable two-degree-of-freedom leg is introduced, and the kinematics is analyzed. Finally, experiments are conducted to generate wheeled, legged, and RHex motion in both swing and rolling gaits, and the energy efficiency is further compared. The experimental results show that the rolling motion can ensure stable ground contact and mitigate cyclic collisions, reducing specific resistance by up to 30% compared with conventional swing gaits, achieving a top speed of 0.7 m/s with enhanced stability (root mean square error (RMSE) reduction of 22% over RHex mode). Furthermore, R-Taichi exhibits robust multi-terrain adaptability, successfully traversing gravel, grass, and obstacles up to 150 mm in height. Full article

Hydrogel sensors show unique advantages in underwater detection, ocean monitoring, and human–computer interaction because of their excellent flexibility, biocompatibility, high sensitivity, and environmental adaptability. However, due to the water environment, hydrogels will dissolve to a certain extent, resulting in insufficient mechanical strength, poor long-term stability, and signal interference. In this paper, a double-network structure was constructed by polyvinyl alcohol (PVA) and poly([2-(methacryloyloxy) ethyl]7 dimethyl-(3-sulfopropyl) ammonium hydroxide) (PSBMA). The resultant PVA/PSBMA-PA hydrogel demonstrated notable swelling resistance, a property attributable to the incorporation of non-covalent interactions (electrostatic interactions and hydrogen bonding) through the addition of phytic acid (PA). The hydrogel exhibited high stretchability (maximum tensile strength up to 304 kPa), high conductivity (5.8 mS/cm), and anti-swelling (only 1.8% swelling occurred after 14 days of immersion in artificial seawater). Assembled as a sensor, it exhibited high strain sensitivity (0.77), a low detection limit (1%), and stable electrical properties after multiple tensile cycles. The utilization of PVA/PSBMA-PA hydrogel as a wearable sensor shows promise for detecting human joint movements, including those of the fingers, wrists, elbows, and knees. Due to the excellent resistance to swelling, the PVA/PSBMA-PA-based sensors are also suitable for underwater applications, enabling the detection of underwater mannequin motion. This study proposes an uncomplicated and pragmatic methodology for producing hydrogel sensors suitable for use within subaquatic environments, thereby concomitantly broadening the scope of applications for wearable electronic devices. Full article

Fibrous structure is a promising building block for developing high-performance wearable piezoresistive sensors. However, the inherent non-conductivity of the fibrous polymer remains a bottleneck for highly sensitive and fast-responsive piezoresistive sensors. Herein, we reported a polyaniline/reduced graphene oxide @ polydopamine/poly (vinylidene fluoride) (PANI/rGO@PDA/PVDF) nanofiber piezoresistive sensor (PNPS) capable of versatile wearable biomonitoring. The PNPS was fabricated by integrating rGO sheets and PANI particles into a PDA-modified PVDF nanofiber network, where PDA was implemented to boost the interaction between the nanofiber networks and functional materials, PANI particles were deposited on a nanofiber substrate to construct electroactive nanofibers, and rGO sheets were utilized to interconnect nanofibers to strengthen in-plane charge carrier transport. Benefitting from the synergistic effect of multi-dimensional electroactive materials in piezoresistive membranes, the as-fabricated PNPS exhibits a high sensitivity of 13.43 kPa⁻¹ and a fast response time of 9 ms, which are significantly superior to those without an rGO sheet. Additionally, a wide pressure detection range from 0 to 30 kPa and great mechanical reliability over 12,000 cycles were attained. Furthermore, the as-prepared PNPS demonstrated the capability to detect radial arterial pulses, subtle limb motions, and diverse respiratory patterns, highlighting its potential for wearable biomonitoring and healthcare assessment. Full article

Bio-based ionic conductive hydrogels have attracted significant attention for use in wearable electronic sensors due to their inherent flexibility, ionic conductivity, and biocompatibility. However, achieving a balance between high ionic conductivity and mechanical robustness remains a significant challenge. In this study, we present a simple yet effective strategy for fabricating a polyelectrolyte–chitin double-network hydrogel (CAA) via the copolymerization of acrylamide (AM) and acrylic acid (AA) with chitin in an AlCl₃-ZnCl₂-H₂O ternary molten salt system. The synergistic interactions of dynamic metal ion coordination bonds and hydrogen bonding impart the CAA hydrogel with outstanding mechanical properties, including a fracture strain of 1765.5% and a toughness of 494.4 kJ/m³, alongside a high ionic conductivity of 1.557 S/m. Moreover, the hydrogel exhibits excellent thermal stability across a wide temperature range (−50 °C to 25 °C). When employed as a wearable sensor, the hydrogel demonstrates a rapid response time (<0.2 s), remarkable durability over 95 cycles with less than 5% resistance drift, and high sensitivity in detecting various human joint motions (e.g., finger, knee, and elbow bending). It presents a scalable strategy for biomass-derived flexible electronics that harmonizes mechanical robustness with electromechanical performance. Full article

Glove-based human–machine interfaces (HMIs) offer a natural, intuitive way to capture finger motions for gesture recognition, virtual interaction, and robotic control. However, many existing systems suffer from complex fabrication, limited sensitivity, and reliance on external power. Here, we present a flexible, self-powered glove HMI based on a minimalist triboelectric nanogenerator (TENG) sensor composed of a conductive fabric electrode and textured Ecoflex layer. Surface micro-structuring via 3D-printed molds enhances triboelectric performance without added complexity, achieving a peak power density of 75.02 μW/cm² and stable operation over 13,000 cycles. The glove system enables real-time LED brightness control via finger-bending kinematics and supports intelligent recognition applications. A convolutional neural network (CNN) achieves 99.2% accuracy in user identification and 97.0% in object classification. By combining energy autonomy, mechanical simplicity, and machine learning capabilities, this work advances scalable, multi-functional HMIs for applications in assistive robotics, augmented reality (AR)/(virtual reality) VR environments, and secure interactive systems. Full article