Search Results (67)

Gait is a sensitive marker of mobility decline and fall risk, motivating unobtrusive sensing methods that can extract spatiotemporal parameters outside specialized gait laboratories. This paper presents a physics-based comparison of two millimeter-wave frequency-modulated continuous-wave (FMCW) radar deployment paradigms using a low-cost, system-on-chip (SoC) 60 GHz Infineon BGT60TR13C radar sensor: (i) a fixed (tripod-mounted) corridor observer and (ii) a shoe-mounted body-centric configuration attached to the medial side of the left shoe. Four healthy adult author-participants performed repeated 30 s corridor trials under five gait styles (regular, slow, fast, simulated festination, and simulated freezing-of-gait), including brief pauses during turns; an empty-corridor recording was acquired to characterize static clutter. Step events were detected using peak-picking on foot-related velocity envelopes with adaptive thresholds, and step count, cadence, step time, and step-time variability were derived. Performance of the fixed and shoe-mounted configurations was quantitatively compared to video ground truth using mean absolute percentage error (MAPE) for step count estimation. Across all gait styles, the shoe-mounted FMCW radar consistently reduced step-count error relative to the fixed corridor-mounted configuration, with the largest gains under irregular patterns (e.g., festination: 37.1% fixed vs. 9.6% shoe-mounted). These findings highlight the advantages of body-centric millimeter-wave radar sensing and support low-cost SoC radar as a pathway toward wearable, privacy-preserving gait monitoring in real-world environments. Full article

The development of highly dynamic bipedal robots demands sensing capable of capturing key contact-related signals in real time, particularly the Center of Pressure (CoP). CoP is fundamental for locomotion control and state estimation and is also of interest in biomedical applications such as gait analysis and lower-limb assistive devices. To enable reliable CoP acquisition under dynamic walking, this paper presents a foot-mounted measurement system and an online self-calibration method that adapts sensor scale and bias parameters during locomotion using both external foot sensors and the robot’s proprioceptive measurements. We demonstrate an online self-calibration pipeline that updates foot-sensor scale and bias parameters during a walking experiment on a NAO-V5 platform using a sliding window optimization. The reported results indicate improved within-trial consistency relative to an offline-calibrated reference baseline under the tested walking conditions. In addition, the framework reconstructs a digitized estimate of the vertical ground reaction force (vGRF) from load-cell readings; due to ADC quantization and the discrete offline calibration dataset, the vGRF signal may exhibit stepwise behavior and should be interpreted as a reconstructed (digitized) quantity rather than laboratory-grade continuous force metrology. Overall, the proposed sensing-and-calibration pipeline offers a practical solution for dynamic CoP acquisition with low-cost hardware. Full article

In field exploration, disaster rescue, and complex terrain operations, the accuracy of ground type recognition directly affects the walking stability and task execution efficiency of legged robots. To address the problem of terrain recognition in complex ground environments, this paper proposes a high-precision classification method based on single-leg triaxial force signals. The method first employs a one-dimensional convolutional neural network (1D-CNN) module to extract local temporal features, then introduces a long short-term memory (LSTM) network to model long-term and short-term dependencies during ground contact, and incorporates a convolutional block attention module (CBAM) to adaptively enhance the feature responses of critical channels and time steps, thereby improving discriminative capability. In addition, an improved whale optimization algorithm (iBWOA) is adopted to automatically perform global search and optimization of key hyperparameters, including the number of convolution kernels, the number of LSTM units, and the dropout rate, to achieve the optimal training configuration. Experimental results demonstrate that the proposed method achieves excellent classification performance on five typical ground types—grass, cement, gravel, soil, and sand—under varying slope and force conditions, with an overall classification accuracy of 96.94%. Notably, it maintains high recognition accuracy even between ground types with similar contact mechanical properties, such as soil vs. grass and gravel vs. sand. This study provides a reliable perception foundation and technical support for terrain-adaptive control and motion strategy optimization of legged robots in real-world environments. Full article

Background: A single lumbar-mounted inertial sensor offers a practical alternative to optoelectronic systems for gait analysis, simplifying measurements and improving usability in the clinical field. However, its validity can be influenced by sensor placement and signal choice. This study aimed to develop and validate a novel algorithm for estimating spatiotemporal parameters using anteroposterior linear acceleration and angular velocity around the sagittal axis using a single inertial measurement unit (IMU) placed on the lumbar region. The proposed algorithm was validated comparing the parameters computed by the algorithm with the ones computed using a commercial wearable system based on a two-foot-mounted IMU configuration. Thirty healthy subjects underwent a 2 min walk test, and five spatiotemporal parameters were computed using the two methodologies. Study results showed that cadence and gait cycle time exhibited very high agreement, with only a small, statistically significant bias in cadence negligible for practical purposes. In contrast, swing, stance, and double-support parameters showed disagreement due to the presence of systematic proportional errors. This work introduces a novel algorithm for gait event detection and spatiotemporal parameter estimation, addressing uncertainties related to sensor placement, metric models, processing techniques, and signal selection, while avoiding synchronization issues associated with using multiple sensors. Full article

This study examined the influence of physiological parameters on peak velocity (Vpeak) and of kinematic variables on running economy (RE) during an outdoor incremental VAM-EVAL test completed by eleven national-level triathletes. Maximal oxygen uptake (VO₂max), ventilatory thresholds, RE, and minimum muscle oxygen saturation (SmO₂min) were obtained with a portable gas analyzer and near-infrared spectroscopy (NIRS), while cadence, stride length, vertical oscillation, and contact time were recorded with a foot-mounted inertial sensor. Multiple linear regression showed that VO₂max and SmO₂min together accounted for 86% of the variance in Vpeak (VO₂max: r = 0.76; SmO₂min: r = −0.68), whereas RE at 16 km·h⁻¹ displayed only a moderate association (r = 0.54). Links between RE and kinematic metrics were negligible to weak (r ≤ 0.38). These findings confirm VO₂max as the primary determinant of Vpeak and suggest that SmO₂min can be used as a complementary, non-invasive marker of endurance capacity in triathletes, measurable in the field with portable NIRS. Additionally, inter-individual differences in cadence, stride length, vertical oscillation, and contact time suggest that kinematic adjustments are not universally effective but rather highly individualized, with their impact on RE likely depending on each athlete’s specific characteristics. Full article

Differences in horseshoe materials may have effects on gait that could change perceived esthetic qualities. Objective information regarding effects of shoeing on gait characteristics of horses is scant. The aim of this study was to determine differences in gait characteristics for horses under various experimental shoeing conditions (barefoot, aluminum shoes, steel shoes) on two surfaces (asphalt and soft footing) using body- and hoof-mounted sensors. We hypothesized that shoeing would affect hoof arc height during early (arc height a) and late (arc height b) swing phases but would not affect other gait variables. Twelve healthy, adult, client-owned horses were evaluated at a trot on asphalt and soft footing under the three experimental shoeing conditions. No significant (p < 0.05) effects of shoeing were detected for gait symmetry (Q score), mediolateral hoof deviation, stride length, or midstance, breakover, swing, and landing stride phase times. Hoof arc height a was significantly (p < 0.001) lower for aluminum versus steel shoes for right and left forelimbs on asphalt and soft footing. Hoof arc height b was significantly higher for aluminum versus steel shoes on soft footing for left (p < 0.001) and right (p = 0.02) forelimbs. Findings indicate that shoe weights affect early and late swing phase hoof heights differently. Further investigation is warranted to determine whether measured hoof arc height changes affect subjective esthetics of gait. Full article

Background/Objectives: Hearing loss, particularly in older adults, is associated with reduced physical functioning; increased fall risk; and altered gait patterns, including slower walking speed and shorter step length. While the underlying mechanisms are not fully understood, one possibility is that these gait changes result from an additional cognitive load due to hearing difficulties. Prior research suggests that hearing aids may improve balance; however, their impact on gait remains less well explored. Methods: This study investigated gait parameters in individuals with hearing loss as they walked with and without hearing aid amplification under different dual-task conditions. Additionally, we showed the potential of ear-worn sensors for detecting relevant gait changes. To achieve this, we used a hearing-aid-integrated accelerometer and our open-source EarGait framework comprising gait-related algorithms specifically developed for ear-worn sensors. Results: Our findings revealed no significant differences in gait velocity or step length between the unaided and aided conditions. For stride time, we observed a significant interaction effect; however, the effect size was negligible. The dual-task costs were lower than in previous reports, indicating that the applied dual-task paradigm did not induce the expected cognitive demand. The ear-worn gait analysis system showed strong performance compared to foot-worn sensors. Conclusions: Our findings indicate that in controlled, low-cognitive-demand settings, hearing aid amplification does not affect gait performance and, therefore, neither hinders nor improves walking performance. Additionally, the high accuracy of the ear-worn gait analysis system highlights the strong potential of ear-mounted wearable devices (“earables”) for real-world mobility assessments. Future research should explore more complex real-world conditions to better understand the impact of hearing aids on walking behavior. Our proposed earable-based system offers a promising tool for continuous, unobtrusive gait monitoring in everyday environments. Full article

This paper presents a complete planner and controller scheme for bipedal robots, designed to enhance robustness against external disturbances. The high-level planner utilizes model predictive control (MPC) to optimize both the foothold location and step duration based on the divergent component of motion (DCM) to increase the robustness of generated gaits. For low-level control, we employ a momentum-based observer capable of estimating external forces acting on both stance and swing legs. The full-body dynamics, incorporating estimated disturbances, are integrated into a weighted whole-body control (WBC) to obtain more accurate ground reaction forces needed by the momentum-based observer. This approach eliminates the dependency on foot-mounted sensors for ground reaction force measurement, distinguishing our method from other disturbance estimation methods that rely on direct sensor measurements. Additionally, the controller incorporates trajectory compensation mechanisms to mitigate the effects of external disturbances. The effectiveness of the proposed framework is validated through comprehensive simulations and experimental evaluations conducted on BRUCE, a miniature bipedal robot developed by Westwood Robotics (Los Angeles, CA, USA). These tests include walking under swing leg disturbances, traversing uneven terrain, and simultaneously resisting upper-body pushes. Full article

To suppress pedestrian positioning drift, a velocity constraint commonly known as zero-velocity update (ZUPT) is widely used. However, it cannot correct the error in the non-zero velocity interval (non-ZVI) or observe heading errors. In addition, the positioning accuracy will be further affected when a velocity error occurs in the ZVI (e.g., foot tremble). In this study, the foot, ankle, and shank were regarded as a triangular structure. Consequently, an angle constraint was established by utilizing the sum of the internal angles. Moreover, in contrast to the traditional ZUPT algorithm, a velocity constraint method combined with Coriolis theorem was constructed. Magnetometer measurements were used to correct heading. Three groups of experiments with different trajectories were carried out. The ZUPT method of the single inertial measurement unit (IMU) and the distance constraint method of dual IMUs were employed for comparisons. The experimental results showed that the proposed method had high accuracy in positioning. Furthermore, the constraints built by the lower limb structure were applied to the whole gait cycle (ZVI and non-ZVI). Full article

This work describes a self-powered wireless temperature sensor platform that can be used for foot ulceration monitoring for diabetic patients. The proposed self-powered sensor platform consists of a piezoelectric bimorph, a power conditioning circuit, a temperature sensor readout circuit, and a wireless module. The piezoelectric bimorph mounted inside the shoe effectively converts the foot movement into electric energy that can power the entire sensor platform. Furthermore, a sensor platform was designed, considering the energy requirement of 4.826 mJ for transmitting one data packet of 18 bytes. The self-powered sensor platform prototype was evaluated with five test subjects with different weights and foot shapes; the test results show the subjects had to walk an average of 119.6 s to transmit the first data packet and an additional average of 71.2 s to transmit the subsequent data packet. The temperature sensor showed a resolution of 0.1 °C and a sensitivity of 56.7 mV/°C with a power conditioning circuit efficiency of 74.5%. Full article

Background: One of the elements of improving the striking power of combat sports athletes is the ability to use their body mass in an efficient manner; this can improve movement technique and thus increase combat effectiveness. Therefore, the aim of this study was to gain knowledge about the influence of the effective mass obtained on the strength of turning and side kicks in the context of lateralization. Material and methods: The study involved four adult taekwon-do ITF (International Taekwon-do Federation) competitors. Acceleration and force data were obtained by mounting a wireless IMU sensor manufactured by Noraxon, which was synchronized with a force plate. Results: The median force pressure values amounted to 2661.53 N for the turning kick and 4596.15 N for the side kick, with foot acceleration of 150.56 m/s² and 74.34 m/s², respectively. The calculated median effective mass for the turning kick was 20.12%, and for the side kick, it was 73.09% of the total body mass. The conducted analysis indicates a lack of statistically significant differences between the right and left leg in the obtained kinetic variable values (p > 0.05). Conclusions: Our research suggests that the side kick achieves, on average, higher force values than the turning kick. The noted correlation between the three variables informs us that the greater the effective mass, the greater the force pressure and the smaller the foot acceleration, which is consistent with other studies. The lack of lateralization in the limbs performing the kicks is consistent with another study. Full article

The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian’s gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems. Full article

Micro-Electro-Mechanical System (MEMS) inertial sensors, characterized by their small size, low cost, and low power consumption, are commonly used in foot-mounted wearable pedestrian autonomous positioning systems. However, they also have drawbacks such as heading drift and poor repeatability. To address these issues, this paper proposes an improved pedestrian autonomous 3D positioning algorithm based on dual-foot motion characteristic constraints. Two sets of small-sized Inertial Measurement Units (IMU) are worn on the left and right feet of pedestrians to form an autonomous positioning system, each integrated with low-cost, low-power micro-inertial sensor chips. On the one hand, an improved adaptive zero-velocity detection algorithm is employed to enhance discrimination accuracy under different step-speed conditions. On the other hand, considering the dual-foot gait characteristics and the height difference feature during stair ascent and descent, horizontal position update algorithms based on dual-foot motion trajectory constraints and height update algorithms based on dual-foot height differences are, respectively, designed. These algorithms aim to re-correct the pedestrian position information updated at zero velocity in both horizontal and vertical directions. The experimental results indicate that in a laboratory environment, the 3D positioning error is reduced by 93.9% compared to unconstrained conditions. Simultaneously, the proposed approach enhances the accuracy, continuity, and repeatability of the foot-mounted IMU positioning system without the need for additional power consumption. Full article

This paper presents a study on bioinspired rigid-flexible coupling adaptive compliant motion control of a robot gecko with hybrid actuation for space stations. The biomimetic robot gecko is made of a rigid trunk, four motor-driven active legs with dual-degree-of-freedom shoulder joints, and four pneumatic flexible pleated active attachment–detachment feet. The adaptive impedance model consists of four input parameters: the inertia coefficient, stiffness coefficient, damping coefficient, and segmented expected plantar force. The robot gecko is equipped with four force sensors mounted on its four feet, from which the normal force of each foot can be sensed in real-time. Based on the sensor signal, the variable stiffness characteristics of the feet in different states are analyzed. Furthermore, an adaptive active compliance control strategy with whole-body rigidity–flexibility-force feedback coupling is proposed for the robot gecko. Four sets of experiments are presented, including open-loop motion control, static anti-interference experiment, segmented variable stiffness experiment, and adaptative compliant motion control, both in a microgravity environment. The experiment results indicated that the presented control strategy worked well and the robot gecko demonstrates the capability of stable attachment and compliant detachment, thereby normal impact and microgravity instability are avoided. It achieves position tracking and force tracking while exhibiting strong robustness for external disturbances. Full article

This paper proposes a novel Blockchain-based indoor navigation system that combines a foot-mounted dual-inertial measurement unit (IMU) setup and a zero-velocity update (ZUPT) algorithm for secure and accurate indoor navigation in GNSS-denied environments. The system estimates the user’s position and orientation by fusing the data from two IMUs using an extended Kalman filter (EKF). The ZUPT algorithm is employed to detect and correct the error introduced by sensor drift during zero-velocity intervals, thus enhancing the accuracy of the position estimate. The proposed Low SWaP-C blockchain-based decentralized architecture ensures the security and trustworthiness of the system by providing an immutable and distributed ledger to store and verify the sensor data and navigation solutions. The proposed system is suitable for various indoor navigation applications, including autonomous vehicles, robots, and human tracking. The experimental results provide clear and compelling evidence of the effectiveness of the proposed system in ensuring the integrity, privacy, and security of navigation data through the utilization of blockchain technology. The system exhibits an impressive ability to process more than 680 transactions per second within the Hyperledger-Fabric framework. Furthermore, it demonstrates exceptional accuracy and robustness, with a mean RMSE error of 1.2 m and a peak RMSE of 3.2 during a 20 min test. By eliminating the reliance on external signals or infrastructure, the system offers an innovative, practical, and secure solution for indoor navigation in environments where GNSS signals are unavailable. Full article