Search Results (56)

This study assessed the effects of passive arm-support exoskeletons (ASEs) on postural stability during repetitive arm-reaching tasks. In a 3 × 3 × 2 within-subject design, twenty-four healthy right-handed men completed left-, front-, and right-facing arm-reaching tasks at two working distances (65.5 and 68.9 cm) under three intervention conditions (Without, VEX, Airframe). Postural stability was assessed using center of pressure (CoP) data recorded from a force plate. Both ASEs clearly reduced the mean amplitude of CoP in the mediolateral (ML) direction (i.e., the absolute value of MEAN ML and ML APDF10), although neither yielded improvements in anteroposterior (AP) stability. Task direction significantly influenced all CoP measures: left-facing tasks produced the greatest leftward bias, whereas front-facing tasks yielded the smallest AP CoP amplitude. Increasing the working distance by <4 cm modestly heightened AP bias, as reflected in larger AP bias metrics (i.e., MEAN AP, ML APDF50, and ML APDF90). Overall, passive ASEs selectively enhanced lateral postural control, while their effect on AP stability was negligible or even slightly adverse. These findings indicate that the practical utility of passive ASEs depends on the directional demands of specific occupational tasks. Full article

Gait analysis provides crucial insights into neuromuscular coordination and postural control, especially in ageing populations and rehabilitation contexts. This study investigates the complexity of muscle activation and ground reaction force patterns during gait by applying detrended fluctuation analysis (DFA) to electromyography (EMG) and force-sensitive resistor (FSR) signals. Data from a two-arm randomised clinical trial (RCT) supplemented with an observational control group were used in this study. Participants performed a single-task walking protocol, with EMG recorded from the tibialis anterior and lateral gastrocnemius muscles of both legs and FSR sensors placed under the feet. Gait cycles were segmented using heel-strike detection from the FSR signal, enabling analysis of individual strides. For each gait cycle, DFA was applied to quantify the long-range temporal correlations in the EMG and FSR time series. Results revealed consistent $\alpha$-scaling exponents across cycles, with EMG signals exhibiting moderate persistence ($\alpha \approx 0.85$–$0.92$) and FSR signals showing higher persistence ($\alpha \approx 1.5$), which is indicative of stable and repeatable gait patterns. These findings support the utility of DFA as a nonlinear signal processing tool for characterising gait dynamics, offering potential markers for gait stability, motor control, and intervention effects in populations practising movement-based therapies such as Tai Chi. Future work will extend this analysis to dual-task conditions and comparative group studies. Full article

(1) Background: Detecting long-lie incidents—where individuals remain immobile after a fall—is essential for timely intervention and preventing severe health consequences. However, most existing systems focus only on fall detection, neglect post-fall monitoring, and raise privacy concerns, especially in real-time, non-invasive applications; (2) Methods: This study proposes a lightweight, privacy-preserving, long-lie detection system utilizing thermal imaging and a soft-voting ensemble classifier. A low-resolution thermal camera captured simulated falls and activities of daily living (ADL) performed by ten healthy participants. Human pose keypoints were extracted using MediaPipe, followed by the computation of five handcrafted postural features. The top three classifiers—automatically selected based on cross-validation performance—formed the soft-voting ensemble. Long-lie conditions were identified through post-fall immobility monitoring over a defined period, using rule-based logic on posture stability and duration; (3) Results: The ensemble model achieved high classification performance with accuracy, precision, recall, and an F1 score of 0.98. Real-time deployment on a Raspberry Pi 5 demonstrated the system is capable of accurately detecting long-lie incidents based on continuous monitoring over 15 min, with minimal posture variation; (4) Conclusion: The proposed system introduces a novel approach to long-lie detection by integrating privacy-aware sensing, interpretable posture-based features, and efficient edge computing. It demonstrates strong potential for deployment in homecare settings. Future work includes validation with older adults and integration of vital sign monitoring for comprehensive assessment. Full article

Monitoring posture and movement accurately and efficiently is essential for both physical therapy and athletic training evaluation and interventions. Motion Tape (MT), a self-adhesive wearable skin-strain sensor made of piezoresistive graphene nanosheets (GNS), has demonstrated promise in capturing low back posture and movements. However, to address some of its limitations, this work explores alternative materials by replacing GNS with multi-walled carbon nanotubes (MWCNT). This study aimed to characterize the electromechanical properties of MWCNT-based MT. Cyclic load tests for different peak tensile strains ranging from 1% to 10% were performed on MWCNT-MT made with an aqueous ink of 2% MWCNT. Additional tests to examine load rate sensitivity and fatigue were also conducted. After characterizing the properties of MWCNT-MT, a human subject study with 10 participants was designed to test its ability to capture different postures and movements. Sets of six sensors were made from each material (GNS and MWCNT) and applied in pairs at three levels along each side of the lumbar spine. To record movement of the lower back, all participants performed forward flexion, left and right bending, and left and right rotation movements. The results showed that MWCNT-MT exceeded GNS-MT with respect to consistency of signal stability even when strain limits were surpassed. In addition, both types of MT could assess lower back movements. Full article

The body roll of the tractor propagates through its rigid hitch system to the mounted implement, causing asymmetrical soil penetration depths between the implement’s lateral working elements, which affects the operational effectiveness of the implement. To address this issue, this study developed an automatic leveling system based on a dual closed-loop fuzzy Proportional-Integral-Derivative (PID) algorithm for tractor-mounted implements. The system employed an attitude angle sensor to detect implement posture in real time and utilized two double-acting hydraulic cylinders to provide a compensating torque for the implement that is opposite to the direction of the body’s roll. The relationship model between the implement’s roll angle and the actuator’s response time was established. The controller performed implement leveling by regulating the spool position and holding time of the solenoid directional valve. Simulink simulations showed that under the control of the dual closed-loop fuzzy PID algorithm, the implement’s roll angle adjusted from 10° to 0° in 1.72 s, which was 56.89% shorter than the time required by the fuzzy PID algorithm, with almost no overshoot. This demonstrates that the dual closed-loop fuzzy PID algorithm outperforms the traditional fuzzy PID algorithm. Static tests showed the system adjusted the implement roll angle from ±10° to 0° within 1.3 s. Field experiments demonstrated that the automatic leveling system achieved a maximum absolute error (MaxAE) of 0.91°, a mean absolute error (MAE) of 0.19°, and a root mean square error (RMSE) of 0.28°, with errors within 0.5° for 92.52% of the time. Results from terrain mutation tests indicate that under a sudden 5° vehicle roll angle change, the system confines implement deviation to ±1.5°. The system exhibits high control precision, stability, and robustness, fulfilling the demands of tractor-mounted implement leveling. Full article

Work-related musculoskeletal disorders (WMSDs) are common in the agricultural industry due to repetitive tasks, like plant transplanting, which involve sustained bending, squatting, and awkward postures. This study uses wearable sensors to evaluate human biomechanics during simulated transplanting and assesses the potential of collaborative robot (cobot) assistance to reduce physical strain. Sixteen participants performed transplanting tasks under manual and cobot-assisted conditions. Kinematic and electromyographic (EMG) data were collected using Xsens motion capture and Trigno EMG systems. Cobot assistance significantly reduced the segment velocity and acceleration in key spinal regions (L5/S1, L1/T12, T1/C7), indicating lower dynamic spinal loading. It also altered muscle activation, decreasing biceps brachii use while increasing activation in stabilizing muscles such as the flexor carpi radialis, brachioradialis, and upper trapezius. Task duration decreased by 59.46%, suggesting improved efficiency. These findings highlight cobots’ potential to enhance ergonomic outcomes by encouraging controlled movements and reducing postural stress. However, the shift in muscle activation underscores the need for task-specific cobot tuning. This research supports the use of integrated IMU and EMG systems to inform cobot design and enable real-time biomechanical monitoring in labor-intensive settings. Full article

Background: Sounds that cause disturbances and perturbations to the vestibular (inner ear organ responses) and visual (acute oculomotor responses) systems can impact postural stability. The purpose of this study was to assess the impact of different types of sounds and noises on both static and dynamic PS. Methods: A total of 20 participants (12 females and 8 males; age: 21.35 ± 1.79 years; height: 170.7 ± 9.3 cm; mass: 66.725 ± 14.1 kg) were tested using the limits of stability (LOS) test on the BTrackS™ balance plate and a Timed Up and Go (TUG) test, when exposed to four different sounds and occupational noises [construction noise (CN), white noise (WN), sirens (SRs), and nature sounds (NAs)] in a randomized order with a no sounds (NSs) control performed initially (intensity range of 70–80 dB). The center of pressure (COP) total sway area (cm²) from the LOS and the time to completion of the TUG (seconds) were analyzed using a one-way repeated measures of analysis of variance at an alpha level of 0.05. Results: The observations demonstrated significant differences between the sounds and noises for the TUG (p < 0.001) but not for the LOS test (p = 0.406). Pairwise comparisons for the significant main effect for the TUG revealed that NSs demonstrated significantly slower time to completion compared to CN, WN, and SRs but not NAs. Conclusions: The findings suggest that the different sounds and noises did not impact static PS during the LOS test, which involved the voluntary excursion of the COP while maintaining the same base of support (BOS). However, during dynamic PS with a changing BOS while walking in the TUG, exposure to CN, SRs, and WN demonstrated a faster completion time than NSs or NAs. This finding may be attributed to the anxiety induced by the noise immersion and perception of sounds, compared to calm NAs and no sounds. The findings can aid in better understanding the impact of different occupational noises on PS and emphasize the need for better noise protection and reduction in loud work environments. Full article

The construction industry is physically demanding, often requiring workers to lift heavy materials, perform repetitive bending motions, and maintain stability on elevated structures. Wearable robotic exoskeletons have been introduced as a promising solution to alleviate physical strain and enhance work efficiency. However, prior research has predominantly focused on the ergonomic benefits and injury prevention potential of exoskeletons, with limited quantitative analysis on their impact on actual productivity. This study addressed this research gap by experimentally evaluating the effects of a wearable robotic exoskeleton on construction productivity and motion stability. A total of 20 experienced construction workers participated in controlled experiments involving three representative tasks: sack carrying, masonry bricklaying, and scaffolding installation. Each task was performed under both low-intensity and high-intensity conditions, with and without exoskeleton. Performance metrics, including work output, movement stability, and postural control, were measured using IMU sensors and productivity tracking over a 2 h work period. The results demonstrated that exoskeleton-assisted work led to significant productivity improvements, particularly in high-intensity tasks, with productivity gains of up to 59.5%. Additionally, movement stability metrics showed a 24.8% to 35.4% reduction in sway areas, indicating enhanced balance and control. The findings further revealed that the productivity advantage of exoskeletons increased over time, highlighting their potential in mitigating fatigue effects during prolonged work sessions. These findings provide empirical evidence that wearable robotic exoskeletons can serve as effective tools for improving construction productivity and worker stability, positioning them as viable solutions for physically demanding tasks in construction and related industries. Full article

Background: Visceral manipulation is a soft tissue manual work technique that originates from and is classified within the Osteopathic Manipulative Technique (OMT), focusing on the fascial tissue mobilisation of the visceral system. Manual therapy on internal organs is based on anatomy, physiology and physics. Methods: Sixty women with endometriosis and pelvic organ prolapse, aged 41.5 ± 12.02, participated in the study. The women had manipulation therapy once a week for 5 weeks. The World Health Organisation Quality of Life BREF questionnaire and a postural stability assessment were administered before and after performing visceral manipulation. Results: The p-value = 0.0093 obtained in the group with prolapses and the p-value = 0.0001 in the group with endometriosis indicated that the applied visceral manipulation improved the women’s quality of life. No effect of therapy was observed on postural stability. Conclusions: Visceral manipulation improved the quality of life of both study groups of women. A significant difference was also observed when comparing the two groups for area. Full article

Background/Objectives: To examine the combined effects of sleep quality, dual tasks, and load carriage on postural stability. Methods: Twenty-three university student participants (12 males, ages: 24.6 ± 6.1 year) completed the Pittsburgh Sleep Quality Index (PSQI), then performed quiet standing and a dual task while standing on force plates with and without load carriage. Correlations and repeated measures analysis of variances were used to assess relationships, main effects, and interaction effects of tasks on center of pressure (COP) to assess postural stability. Both a traditional PSQI global score and a sensitivity analysis of the PSQI cut-off were conducted. Results: With the traditional PSQI criteria, a main effect of sleep quality on 95% ellipse area was observed, with good sleepers outperforming bad sleepers (p = 0.016). Additionally, a significant interaction between sleep quality and task (p = 0.049) indicated that COP anterior–posterior velocity was lower during the dual task for good sleepers. No effects on sleep quality or interaction were found for other COP measures. The sensitivity analysis yielded no effect on sleep quality or interaction effects on any COP measure. There were no significant correlations between the PSQI global scores and COP variables. Conclusions: Overall, the results indicate that sleep quality alone had a limited effect and did not significantly interact with dual tasks or load carriage during quiet standing. Practitioners working with individuals who commonly experience poor sleep quality and perform load carriage and dual tasks should consider that common COP screens to assess postural stability may not detect differences due to self-reported sleep quality in healthy, young adults. Full article

Top-view systems for lameness detection have advantages such as easy installation and minimal impact on farm work. However, the unclear lameness motion characteristics of the back result in lower recognition accuracy for these systems. Therefore, we analysed the compensatory behaviour of cows based on top-view walking videos, extracted compensatory motion features (CMFs), and constructed a model for recognising lameness in cows. By locating the hook, pin, sacrum, and spine positions, the motion trajectories of key points on the back were plotted. Based on motion trajectory analysis of 655 samples (258 sound, 267 mild lameness, and 130 severe lameness), the stability mechanisms of back movement posture were investigated, compensatory behaviours in lame cows were revealed, and methods for extracting CMFs were established, including swing and posture features. The feature correlation among differently scoring samples indicated that early-stage lame cows primarily exhibited compensatory swing, while those with severe lameness showed both compensatory swing and posture. Lameness classification models were constructed using machine learning and threshold discrimination methods, achieving classification accuracies of 81.6% and 83.05%, respectively. The threshold method reached a recall rate of 93.02% for sound cows. The proposed CMFs from back depth images are highly correlated with early lameness, improving the accuracy of top-view lameness detection systems. Full article

The purpose of this paper is to describe ongoing research on appropriate instrumentation and analysis techniques to characterize postural stability, postural agility, and dynamic stability, which collectively comprise the postural control spectrum. This study had a specific focus on using emerging sensors to develop protocols suitable for use outside laboratory or clinical settings. First, we examined the optimal number and placement of wearable accelerometers for assessing postural stability. Next, we proposed metrics and protocols for assessing postural agility with the use of a custom force plate-controlled video game. Finally, we proposed a method to quantify dynamic stability during walking tasks using novel frequency-domain metrics extracted from acceleration data obtained with a single body-worn IMU. In each of the three studies, a surrogate for instability was introduced, and the sensors and metrics discussed in this paper show promise for differentiating these trials from stable condition trials. Next steps for this work include expanding the tested population size and refining the methods to even more reliably and unobtrusively characterize postural control status in a variety of scenarios. Full article

The proposed work presents a Sliding Mode Controller (SMC) for trajectory tracking of a TurtleBot3 Burger mobile robot based on sliding mode. Trajectory tracking is performed in congested environments; therefore, an obstacle avoidance strategy is presented to prevent the robot from colliding with obstacles. A clear and detailed methodology is developed for the design of the SMC controller with a PID sliding surface for trajectory tracking that can be extrapolated to position control and posture control. The discontinuous part of the SMC is used to ensure that the robot tends to the desired reference whenever an obstacle appears. The stability analysis of the proposed controller is presented, and the experimental results are shown, demonstrating the good performance of the controller. Full article

This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot’s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot’s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog’s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains. Full article

At the current stage, the automation level of GNSS RTK equipment is low, and manual operation leads to decreased accuracy and efficiency in setting out. To address these issues, this paper has designed an algorithm for automatic setting out that resolves the common problem of reduced accuracy in conventional RTK. First, the calculation of the laser rotation center is conducted using relevant parameters to calibrate the instrument’s posture and angle. Then, by analyzing the posture information, the relative position and direction of the instrument to the point to be set out are determined, and the rotation angles in the horizontal and vertical directions are calculated. Following this, the data results are analyzed, and the obtained rotation angles are output to achieve automatic control of the instrument. Finally, a rotating laser composed of servo motors and laser modules is used to control the GNSS RTK equipment to locate the set-out point, thereby determining its position on the ground and displaying it in real-time. Compared to traditional GNSS RTK equipment, the proposed automatic setting out algorithm and the developed GNSS laser RTK equipment reduce the setting out error from 15 mm to 10.3 mm. This reduces the barrier to using GNSS RTK equipment, minimizes human influence, enhances the work efficiency of setting out measurements, and ensures high efficiency and stability under complex conditions. Full article