Search Results (73)

Background: Joint position sense (JPS) is a critical component of proprioception and postural control, especially in older adults, where deficits are associated with increased risk of falls and functional decline. Recent studies have explored smartphone-based digital inclinometers as accessible tools for clinical proprioceptive assessment, but their participant-level reproducibility in institutionalized elderly populations remains unexplored. Objective: We aimed to examine the reproducibility of joint position sense performance in institutionalized older adults, using a smartphone-based inclinometer that has been applied in other populations. Assessing joint position sense with accessible smartphone-based tools may provide practical insights for rehabilitation and functional assessment in older adults. Methods: Thirty-five-year-old or older adults (mean age 85.9 ± 8.19 years) participated in this test–retest study. JPS was assessed using the iPhone^® inclinometer. Participants memorized and attempted to reproduce a 30° forward trunk flexion while standing. The absolute angular error was measured across two sessions, one week apart. Relative and absolute reproducibility were analyzed using intraclass correlation coefficients (ICC 2,1), standard error of measurement (SEM), Smallest Real Difference (SRD), and Bland–Altman analysis. Results: The ICC for the whole sample was 0.839 (95% CI: 0.72–0.91), indicating good reproducibility of participants’ proprioceptive performance. SEM and SRD were 3.65° (33.3%) and 10.1° (92.3%), respectively. Bland–Altman plots showed minimal bias (0.23°) and only 5.71% of values fell outside the 95% limits of agreement. Conclusions: Participants demonstrated moderate-to-good reproducibility in replicating joint position sense, reflecting consistent proprioceptive performance across sessions. This approach demonstrates feasibility for repeated proprioceptive assessment in this population. These findings have potential applications for functional monitoring and fall-prevention programs in institutionalized older adults. Full article

Conventional vibrational systems associated with proprioception are mostly equipped with a single standard frequency and amplitude. This feature often fails to show kinesthetic illusion on different subjects, as different individuals respond to different frequencies and amplitudes. Additionally, different muscle groups may also require the flexibility of frequencies and amplitudes. We developed a custom vibrational system that is equipped with flexible frequency and amplitude, adapted to a custom graphical user interface (GUI). Based on the user’s criteria, the proposed vibrational system enables a wide range of frequencies and amplitudes that can be swept under a single platform. In addition, the system uses small linear actuators that are wearable and attach to the subject without the need for restrictive straps. The vibrational system was used to model trunk proprioceptive impairment associated with low back pain. Low back pain is the leading cause of disability worldwide. It is mostly associated with impaired postural control of the trunk. For postural control, the somatosensory system transmits proprioceptive (position sense) information from the sensors in the skin, joints, muscles, and tendons. Proprioceptive studies on trunk muscles have been conducted where the application of vibration at a set amplitude and frequency across all participants resulted in altered proprioception and a kinesthetic illusion, but not in all individuals. To assess the feasibility of the system, we manipulated the trunk proprioception of five subjects, demonstrating that the vibrational system is capable of modulating trunk proprioception and the value of customizing parameters of the system to obtain maximal deficits from individual subjects. Full article

Neuromuscular robotic prostheses have emerged as a critical convergence point between biomedical engineering, machine learning, and human–machine interfaces. This work provides a narrative state-of-the-art review regarding recent developments in robotic prosthetic technology, emphasizing sensor integration, actuator architectures, signal acquisition, and algorithmic strategies for intent decoding. Special focus is given to non-invasive biosignal modalities, particularly surface electromyography (sEMG), as well as invasive approaches involving direct neural interfacing. Recent developments in AI-driven signal processing, including deep learning and hybrid models for robust classification and regression of user intent, are also examined. Furthermore, the integration of real-time adaptive control systems with surgical techniques like Targeted Muscle Reinnervation (TMR) is evaluated for its role in enhancing proprioception and functional embodiment. Finally, this review highlights the growing importance of modular, open-source frameworks and additive manufacturing in accelerating prototyping and customization. Progress in this domain will depend on continued interdisciplinary research bridging artificial intelligence, neurophysiology, materials science, and real-time embedded systems to enable the next generation of intelligent prosthetic devices. Full article

Hexapod robots that use external sensors to sense the environment are susceptible to factors such as light intensity or foggy weather. This effect leads to a drastic decrease in the motility of the hexapod robot. This paper proposes a motion control strategy for a blind hexapod robot. The hexapod robot is symmetrical and its environmental sensing capability is obtained by collecting proprioceptive signals from internal sensors, allowing it to pass through rugged terrain without the need for external sensors. The motion gait of the hexapod robot is generated by a central pattern generator (CPG) network constructed by Hopf oscillators. This gait is a periodic gait controlled by specific parameters given in advance. A policy network is trained in the target terrain using deep reinforcement learning (DRL). The trained policy network is able to fine-tune specific parameters by acquiring information about the current terrain. Thus, an adaptive gait is obtained. The experimental results show that the adaptive gait enables the hexapod robot to stably traverse various complex terrains. Full article

Quadruped robots have shown significant potential in disaster relief applications, where they have to navigate complex terrains for search and rescue or reconnaissance operations. However, their deployment is hindered by limited adaptability in highly uncertain environments, especially when relying solely on vision-based sensors like cameras or LiDAR, which are susceptible to occlusions, poor lighting, and environmental interference. To address these limitations, this paper proposes a novel sensor-enhanced hierarchical switching model predictive control (MPC) framework that integrates proprioceptive sensing with a bi-level hybrid dynamic model. Unlike existing methods that either rely on handcrafted controllers or deep learning-based control pipelines, our approach introduces three core innovations: (1) a situation-aware, bi-level hybrid dynamic modeling strategy that hierarchically combines single-body rigid dynamics with distributed multi-body dynamics for modeling agility and scalability; (2) a three-layer hybrid control framework, including a terrain-aware switching MPC layer, a distributed torque controller, and a fast PD control loop for enhanced robustness during contact transitions; and (3) a multi-IMU-based proprioceptive feedback mechanism for terrain classification and adaptive gait control under sensor-occluded or GPS-denied environments. Together, these components form a unified and computationally efficient control scheme that addresses practical challenges such as limited onboard processing, unstructured terrain, and environmental uncertainty. A series of experimental results demonstrate that the proposed method outperforms existing vision- and learning-based controllers in terms of stability, adaptability, and control efficiency during high-speed locomotion over irregular terrain. Full article

Background: Whole-body vibration (WBV) favors central integration and elaboration of proprioceptive stimuli, enhancing gait performance in individuals with Parkinson’s disease (PD). However, the effect of WBV on spatiotemporal gait kinematics in PD has been neglecting so far. This study aims to examine how exposure to WBV could influence kinematic parameters in PD. Methods: Gait kinematic parameters of 26 mild-stage PD participants (age: 66.7 ± 1.63 years) were measured using BTS G-Walk sensor during a 10 m walk test under three conditions—WBV, half squat without vibration (HS), and control condition (CC)—in a crossover randomized design. Results: Walking time was significantly slower (p < 0.01) in CC compared to WBV and HS, while no significant differences were observed between WBV and HS. Right leg propulsion was significantly lower in CC compared to HS (p < 0.01), with no significant differences between CC and WBV. Left leg propulsion was significantly lower in CC and WBV compared to HS (p < 0.01 and p < 0.05, respectively). Pelvic tilt was significantly lower (p < 0.05) in CC compared to WBV and HS, but no significant difference was observed between WBV and HS. Cadence was significantly lower (p < 0.05) in CC and WBV than HS. Conclusions: WBV shows promising effects on functional mobility and postural control in PD, with HS offering greater benefits. Exercise modalities should be carefully selected to enhance different gait parameters. Full article

This paper presents a learning-assisted approach for state estimation of quadruped robots using observations of proprioceptive sensors, including multiple inertial measurement units (IMUs). Specifically, one body IMU and four additional IMUs attached to each calf link of the robot are used for sensing the dynamics of the body and legs, in addition to joint encoders. The extended Kalman filter (KF) is employed to fuse sensor data to estimate the robot’s states in the world frame and enhance the convergence of the extended KF (EKF). To circumvent the requirements for the measurements from the motion capture (mocap) system or other vision systems, the right-invariant EKF (RI-EKF) is extended to employ the foot IMU measurements for enhanced state estimation, and a learning-based approach is presented to estimate the vision system measurements for the EKF. One-dimensional convolutional neural networks (CNN) are leveraged to estimate required measurements using only the available proprioception data. Experiments on real data from a quadruped robot demonstrate that proprioception can be sufficient for state estimation. The proposed learning-assisted approach, which does not rely on data from vision systems, achieves competitive accuracy compared to EKF using mocap measurements and lower estimation errors than RI-EKF using multi-IMU measurements. Full article

Tendon-driven robots offer advantages in terms of their compliance, lightweight design, and remote actuation, making them ideal for applications requiring dexterity and safety. However, existing tendon-driven actuators often suffer from low integration and inaccurate proprioceptive sensing due to their complex pulley-based tension sensors and bulky angle sensors. This paper presents the design and control of a compact and proprioceptive modular tendon-driven actuator. The actuator features a simplified single-pulley tension sensing mechanism and a novel maze-slot fixation method, minimizing friction and maximizing the structural integrity. A 3D Hall effect sensor is employed for accurate estimation of the tendon length with minimal space usage. A feedforward PID controller and a model-based tendon length observer are proposed to enhance the dynamic performance and sensing accuracy. Bench tests demonstrate that the actuator achieves a high power density (0.441 W/g), accurate closed-loop tension control, and reliable tendon length estimations. The proposed design provides a practical and high-performance solution for tendon-driven robots, enabling more agile, compact, and robust robotic systems. Full article

Background/Objectives: Sedentary office workers (SOWs) often adopt prolonged sitting postures, which potentially disrupt the lumbar–pelvic rhythm (LPR) and contribute to lower back pain (LBP). This study aimed to clarify the group differences in LPR and related physical factors between SOWs with and without LBP. Methods: Sixty-three SOWs were divided into LBP (n = 30) and non-LBP (n = 33) groups. The lumbar flexion angle (LF) and lumbar–hip angle difference (LHD), which are indicators of LPR, were measured using inertial sensors during trunk flexion. Hip flexion muscle strength (HFMS) and hip-extension muscle strength (HEMS) were assessed using handheld dynamometry. Hip joint range of motion (ROM) was measured using a goniometer. Lumbar proprioception was evaluated via active joint repositioning, and pain and perception were assessed using the Visual Analog Scale, Oswestry Disability Index, and Fremantle Back Awareness Questionnaire. Results: Multiple regression analysis showed significantly greater LF (estimated regression coefficient [ERC]: −2.9, p < 0.05) and LHD (ERC: −5.5, p < 0.05) during early trunk flexion (ETF) in the LBP group. In the LBP group, LHD during ETF and late trunk flexion were positively correlated with HFMS, and HFMS was correlated with HEMS. Conclusions: HFMS may contribute to an altered LPR in SOWs with LBP. Full article

To enhance wrist impairment rehabilitation efficiency, self-rehabilitation training using healthy-side forearm sEMG was introduced, improving patient engagement and proprioception. A sEMG-based movement recognition and muscle force estimation algorithm was proposed to transmit the estimated results to a wrist rehabilitation robot. Dominant eigenvalues of raw forearm EMG signals were selected to construct a movement recognition model that included a BPNN, a voting decision, and an intensified algorithm. An experimental platform for muscle force estimation was established to measure sEMG under various loads. The linear fitting was performed between mean absolute values (MAVs) and external loads to derive static muscle force estimation models. A dynamic muscle force estimation model was established through linear fitting average MAVs. Volunteers wore EMG sensors and performed six typical movements to complete the verification experiment. The average accuracy of only BPNN was 90.7%, and after the addition of the voting decision and intensified algorithm, it was improved to 98.7%. In the resistance training, the measured and estimated muscle forces exhibited similar trends, with RMSE of 4.2 N for flexion/extension and 5.8 N for ulnar/radial deviation. Under two different speeds and loads, the theoretical and estimated values of dynamic muscle forces showed similar trends with almost no phase difference, and the estimation accuracy was better during flexion movements compared to radial deviations. The proposed algorithms had strong versatility and practicality, aiming to realize the self-rehabilitation trainings of patients. Full article

Acquiring terrain information during robot locomotion is essential for autonomous navigation, gait selection, and trajectory planning. Quadruped robots, due to their biomimetic structures, demonstrate enhanced traversability over complex terrains compared to other robotic platforms. Furthermore, the internal sensors of quadruped robots acquire rich terrain-related data during locomotion across diverse terrains. This study investigates the relationship between terrain characteristics and quadruped robots based on proprioception sensor data, and proposes a simple, efficient, and motion-independent terrain classification method by integrating multiple sensor signals. The sensors referred to in the text only include the IMU sensor and joint encoders, which means that the method has a wide range of applicability while requiring sufficiently low hardware cost. The Convolutional Neural Network will serve as the backbone of the algorithm. In addition, the control command about its own control information will serve as supporting information to eliminate the impact of motion patterns on the results. Employing a multi-label classification algorithm, the complex terrains are classified by multiple physical feature labels like roughness, slippage, softness, and slope, which depict terrain attributes. A feature-labeled terrain dataset is established by abstracting diverse terrain features across various terrains. Unlike semantic labels (e.g., grassland, sand, gravel) that are comprehensible only to humans, feature labels provide a more helpful and precise terrain characterization, including broader terrain attributes. Full article

The work presented in this paper is to be read within the context of a connected autonomous vehicle (CAV). This context makes it possible to consider dividing the overall operational domain (operational design domain: ODD) of the vehicle into three sub-domains, relating to the areas of comfort (ODD1), road-holding (ODD2), and emergency situations (ODD3). Thus, based on information from the CAV’s proprioceptive and exteroceptive sensors, in addition to information from the infrastructure and other vehicles, supervision makes it possible, at any time, to identify the ODD in which the vehicle is located and to propose the most appropriate strategy, particularly for suspension control. Work already carried out by the authors made it possible to determine a crone sky hook (CSH) strategy for suspension control, 100% comfort-oriented for ODD1, a mixed crone sky hook—crone ground hook (CSH-CGH) strategy, oriented towards road-holding for ODD2, and a CGH strategy oriented towards safety for ODD3. In this paper, a comparative study focusing on security (ODD3) is presented. It concerns two versions of the CGH strategy (nominal CGHN and generalized CGHG). More precisely, for the comparative study to be meaningful, the control loops of the two versions have the same speed (iso-speed constraint), and the performance indices are normalized with respect to the values obtained in fault mode when the actuator is faulty. Notably, the CGHG version is part of the dynamics of fractional systems. Full article

Background: Mild traumatic brain injury (mTBI) in the pediatric population is a significant public health concern, often associated with persistent post-concussion symptoms, including postural instability. Current tools for assessing postural control, such as the Balance Error Scoring System (BESS), lack integration with objective metrics. Incorporating force plate sensors into BESS assessments may enhance diagnostic accuracy and support return-to-play or sports decisions. This study evaluates postural performance in children with mTBI compared to controls using an instrumented BESS and examines recovery trajectories after mTBI. Methods: This prospective, longitudinal study included 31 children with mTBI (12.01 ± 3.28 years, 20 females) and 31 controls (12.31 ± 3.27 years, 18 females). Postural control was assessed using an instrumented BESS protocol during standing on a ground reaction force plate at three timepoints: within 72 h post injury (T1), at two weeks (T2), and three months after trauma (T3). Posturographic parameters derived from the displacement of the center of pressure included the ellipse area, path length, and mean velocity in the anterior–posterior and medio–lateral directions. Symptom burden was monitored using the Post-Concussion Symptom Inventory (PCSI). Results: The BESS total scores did not differ significantly between the groups at any timepoint. A significant reduction in BESS errors over time was observed exclusively in the two-legged stance on a soft surface (p = 0.047). The instrumented BESS revealed higher body swaying in the mTBI group compared to controls, particularly under demanding conditions. Significant between-group differences were most frequently observed in single-leg soft surface (38% of comparisons) and two-legged soft surface stances (29%). In those cases, path length and mean velocity differed between groups, respectively. Ellipse area did not show significant differences across conditions. Conclusions: An instrumented BESS has the potential to enhance the detection of subtle postural deficits in pediatric mTBI patients. Specifically, more demanding conditions with altered sensory-proprioceptive input and path length as an outcome measure should be focused on. This study underscores the need for tailored and age-appropriate objective and quantitative balance assessments to improve diagnostic precision in pediatric mTBI populations. Full article

Objective: Mental fatigue (MF) induced by prolonged cognitive tasks poses significant risks to postural stability, yet its effects on multi-sensory integration remain poorly understood. Method: This study investigated how MF alters sensory reweighting and postural control in 27 healthy young males. A 45 min incongruent Stroop task was employed to induce MF, validated via subjective Visual Analog Scale (VAS) scores and psychomotor vigilance tests. Postural stability was assessed under four sensory perturbation conditions (O-H: no interference; C-H: visual occlusion; O-S: proprioceptive perturbation; C-S: combined perturbations) using a Kistler force platform. Center of pressure (COP) signals were analyzed through time-domain metrics, sample entropy (SampEn), and Discrete Wavelet Transform (DWT) to quantify energy distributions across sensory-related frequency bands (visual: 0–0.1 Hz; vestibular: 0.1–0.39 Hz; cerebellar: 0.39–1.56 Hz; proprioceptive: 1.56–6.25 Hz). Results: MF significantly reduced proprioceptive energy contributions (p < 0.05) while increasing vestibular reliance under O-S conditions (p < 0.05). Time-domain metrics showed no significant changes in COP velocity or displacement, but SampEn decreased under closed-eye conditions (p < 0.001), indicating reduced postural adaptability. DWT analysis highlighted MF’s interaction with visual occlusion, altering cerebellar and proprioceptive energy dynamics (p < 0.01). Conclusion: These findings demonstrate that MF disrupts proprioceptive integration, prompting compensatory shifts toward vestibular and cerebellar inputs. The integration of nonlinear entropy and frequency-domain analyses advances methodological frameworks for fatigue research, offering insights into real-time sensor-based fatigue monitoring and balance rehabilitation strategies. This study underscores the hierarchical interplay of sensory systems under cognitive load and provides empirical evidence for optimizing interventions in high-risk occupational and clinical settings. Full article

This paper introduces a team of Automated Guided Vehicles (AGVs) equipped with open-source, perception-enhancing rotating devices. Each device has a set of ArUco markers, employed to compute the relative pose of other AGVs. These markers also serve as landmarks, delineating a path for the robots to follow. The authors combined various control methodologies to track the ArUco markers on another rotating device mounted on the AGVs. Behavior trees are implemented to facilitate task-switching or to respond to sudden disturbances, such as environmental obstacles. The Robot Operating System (ROS) is installed on the AGVs to manage high-level controls. The efficacy of the proposed solution is confirmed through a real experiment. This research contributes to the advancement of AGV technology and its potential applications in various fields for example in a warehouse with a restricted and known environment where AGVs can transport goods while avoiding other AGVs in the same environment. Full article