Teacher-Informed Tactile Sensing Upgrade for Child–Robot Physical Interaction: A Comparison of Velostat and FBG Sensors
Social robots designed to engage with children require additional co-designed features that reflect authentic interactions. Physical human robot interaction (pHRI) in this application requires tactile sensors that are reliable and safe for natural children’s touch. This work reports on the design and assessment of two tactile sensing systems—one using Velostat and the other based on optical sensing—developed in line with teacher feedback to enhance the integration of the CASTOR robot into educational activities. Both sensors were characterized under controlled forces representative of human touch. The Velostat sensor exhibited a nonlinear force response, higher spatial resolution, and lower hysteresis, while the optical sensor demonstrated a highly linear response and greater sensitivity. The sensing systems were subsequently evaluated with 5 participants (25.73 ± 8.32 years) in a hand-shaking pHRI task. A heatmap-based analysis revealed that both sensors effectively captured the temporal and spatial distribution of contact forces, while also highlighting qualitative differences in localized forces among participants, with a maximum exerted force of 1.30 N. Overall, the results indicate that the Velostat-based sensor is advantageous for the educational scenario, as it provides detailed contact localization, exhibits low hysteresis, is more robust, and is replicable.
POF-Based Sensor Device for Assessment of Human–Robot Interaction Forces
This article presents the development and validation of a polymer optical fiber (POF) sensor system for quantifying human–robot interaction (HRI) torque in knee exoskeletons. The device consists of a four-channel POF array embedded in a shank cuff that provides online torque signals during gait tasks. The channels were calibrated against a reference load cell, yielding dynamic force RMSE in the range 3.93–5.14 N and correlation between 0.88 and 0.94. In quasi-static interface tests with six participants, the reconstructed POF torque tracked full torque transfer through the cuff, with RMSE of 1.4–1.8 N ⋅ m for sinusoidal and triangular actuator profiles. In treadmill trials, the POF-based torque was used as a reference to assess disturbance observer (DOB) techniques. All observers reproduced the main interaction-torque patterns but showed attenuation and temporal shifts with respect to the POF reference, with RMSE between 0.8 and 1.3 N ⋅ m. These results indicate that the POF cuff delivers a consistent interaction-torque signal at the interface and can serve as a practical reference when analyzing bandwidth and robustness limitations of observer-based estimators.
High-Resolution Distributed Optical Fiber Sensor for Multiparameter Sensing, Local Inhomogeneity Detection, and Poisson’s Ratio Estimation in Concrete Structures
The structural health monitoring (SHM) concept emerges as an important approach for the continuous assessment of civil infrastructure, directly aligning with the demands for sustainability, safety, and smart devices in modern urbanization and smart cities. Addressing these demands, this article presents the development and validation of a multiparameter distributed optical fiber sensor (DOFS) for the simultaneous measurement of temperature, longitudinal strain, and transverse strain with subcentimeter-scale spatial resolution. Based on optical frequency domain reflectometry (OFDR), the system utilizes a geometrically configured commercial fiber to act as a continuous spatial rosette. The sensor was characterized, achieving linear sensitivities of 0.1253 GHz/ μϵ and 1.251 GHz/°C, with a limit of detection of 0.1 μϵ and 0.1 ∘ C and a spatial resolution of 2.5 mm. Application tests on concrete cylinders under compressive loading demonstrated the system’s capability to decouple multiaxial strain components while inherently compensating for thermal cross-sensitivity, maintaining a stable temperature variation of 0.3 ∘ C during testing. The decomposed experimental strain profiles were validated against a numerical finite element analysis (FEA). In contrast with the idealized numerical model, the distributed sensor successfully mapped real-world localized strain concentrations and inherent material inhomogeneities, registering spatial strain variations of up to 619.22 μϵ (longitudinal) and 113.75 μϵ (transverse) near the elastic limit (160 kN). By aggregating the synchronized multitest data, the Poisson’s ratio of the concrete was estimated at 0.16. Furthermore, the system captured the complete spatiotemporal evolution of damage, precisely identifying the macroscopic rupture region. Finally, the results demonstrate that the proposed device provides a robust, single-fiber solution for 2-D strain field mapping and multiparameter SHM, filling key technological gaps for future smart infrastructure applications.
Characterization and functional evaluation of a fiber-optic instrumented impedance-controlled upper-limb exoskeleton
The use of active exoskeletons for musculoskeletal disorder (MSD) prevention is increasing in industrial environments. Wearable sensing technologies enable closed-loop control of these devices. However, magnetic disturbances and sensor fixation issues can compromise measurement reliability and affect exoskeleton behavior. Polymer optical fiber (POF) sensors offer intrinsic immunity to electromagnetic interference and mechanical compliance, making them a promising alternative for wearable kinematic sensing. Nevertheless, their integration within the control loop of active exoskeletons remains largely unexplored. This study investigates the impact of POF sensing modality integrated within the impedance control loop of an upper-limb exoskeleton, focusing on control performance and system dynamics. A two-stage experimental protocol was conducted under an approved ethical protocol. First, a test-bench evaluation showed that POF sensing modality achieves comparable rise time and mean absolute error to IMU sensing modality, though it resulted in a 0.565 s increase in settling time. Second, participants performed standardized lifting tasks under assisted (Exo-POF and Exo-IMU) and unassisted (No-Assist) conditions. Kinematic and electromyographic (EMG) data showed a 16–23% reduction in muscle activation while preserving range of motion. These findings demonstrate that POF sensing modality significantly influences system dynamics and human–robot interaction. Subsequently, the proof of concept suggests the feasibility of the proposed approach during lifting, handling, and lowering tasks, opening the way for future validation under representative industrial environments.
High-Resolution Distributed Optical Fiber Sensor for Multiparameter Sensing, Local Inhomogeneity Detection, and Poisson’s Ratio Estimation in Concrete Structures
The structural health monitoring (SHM) concept emerges as an important approach for the continuous assessment of civil infrastructure, directly aligning with the demands for sustainability, safety, and smart devices in modern urbanization and smart cities. Addressing these demands, this article presents the development and validation of a multiparameter distributed optical fiber sensor (DOFS) for the simultaneous measurement of temperature, longitudinal strain, and transverse strain with subcentimeter-scale spatial resolution. Based on optical frequency domain reflectometry (OFDR), the system utilizes a geometrically configured commercial fiber to act as a continuous spatial rosette. The sensor was characterized, achieving linear sensitivities of 0.1253 GHz/ μϵ and 1.251 GHz/°C, with a limit of detection of 0.1 μϵ and 0.1 ∘ C and a spatial resolution of 2.5 mm. Application tests on concrete cylinders under compressive loading demonstrated the system’s capability to decouple multiaxial strain components while inherently compensating for thermal cross-sensitivity, maintaining a stable temperature variation of 0.3 ∘ C during testing. The decomposed experimental strain profiles were validated against a numerical finite element analysis (FEA). In contrast with the idealized numerical model, the distributed sensor successfully mapped real-world localized strain concentrations and inherent material inhomogeneities, registering spatial strain variations of up to 619.22 μϵ (longitudinal) and 113.75 μϵ (transverse) near the elastic limit (160 kN). By aggregating the synchronized multitest data, the Poisson’s ratio of the concrete was estimated at 0.16. Furthermore, the system captured the complete spatiotemporal evolution of damage, precisely identifying the macroscopic rupture region. Finally, the results demonstrate that the proposed device provides a robust, single-fiber solution for 2-D strain field mapping and multiparameter SHM, filling key technological gaps for future smart infrastructure applications.
POF-Based Sensor Device for Assessment of Human-Robot Interaction Forces
This article presents the development and validation of a polymer optical fiber (POF) sensor system for quantifying human–robot interaction (HRI) torque in knee exoskeletons. The device consists of a four-channel POF array embedded in a shank cuff that provides online torque signals during gait tasks. The channels were calibrated against a reference load cell, yielding dynamic force RMSE in the range 3.93–5.14 N and correlation between 0.88 and 0.94. In quasi-static interface tests with six participants, the reconstructed POF torque tracked full torque transfer through the cuff, with RMSE of 1.4–1.8 N ⋅ m for sinusoidal and triangular actuator profiles. In treadmill trials, the POF-based torque was used as a reference to assess disturbance observer (DOB) techniques. All observers reproduced the main interaction-torque patterns but showed attenuation and temporal shifts with respect to the POF reference, with RMSE between 0.8 and 1.3 N ⋅ m. These results indicate that the POF cuff delivers a consistent interaction-torque signal at the interface and can serve as a practical reference when analyzing bandwidth and robustness limitations of observer-based estimators.