Search Results (50)

Attempting to make machines mimic human walking, grasping, balancing, and other behaviors is a deep exploration of cognitive science and biological principles. Due to the existing prediction lag problem, an error compensation mechanism that integrates historical motion data is proposed. By constructing a humanoid autonomous walking control system, this paper aims to use a three-dimensional linear inverted pendulum model to plan the general framework of motion. Firstly, the landing point coordinates of the single foot support period are preset through gait cycle parameters. In addition, it is substituted into dynamic equation to solve the centroid (COM) trajectory curve that conforms to physical constraints. A hierarchical whole-body control architecture is designed, with a task priority based on quadratic programming solver used at the bottom to decompose high-level motion instructions into joint space control variables and fuse sensor data. Furthermore, the numerical iterative algorithm is used to solve the sequence of driving angles for each joint, forming the control input parameters for driving the robot’s motion. This algorithm solves the limitations of traditional inverted pendulum models on vertical motion constraints by optimizing the centroid motion trajectory online. At the same time, it introduces a contact phase sequence prediction mechanism to ensure a smooth transition of the foot trajectory during the switching process. Simulation results demonstrate that the proposed framework improves disturbance rejection capability by over 30% compared to traditional ZMP tracking and achieves a real-time control loop frequency of 1 kHz, confirming its enhanced robustness and computational efficiency. Full article

This study investigated differences in energy-expenditure (EE) modeling between badminton players of varying competitive levels during aerobic training. It evaluated the impact of sensor quantity and sample size on prediction model accuracy and generalizability, providing evidence for personalized training-load monitoring. Fifty badminton players (25 elite, 25 enthusiasts) performed treadmill running, cycling, rope skipping, and stair walking. Data were collected using accelerometers (waist, wrists, ankles), a heart rate monitor, and indirect calorimetry (criterion EE). Multiple machine learning models (Linear Regression, Bayesian Ridge Regression, Random Forest, Gradient Boosting) were employed to develop EE prediction models. Performance was assessed using R², mean absolute percentage error (MAPE), and root mean square error (RMSE), with further evaluation via the Triple-E framework (Effectiveness, Efficiency, Extension). Elite athletes demonstrated stable, coordinated movement patterns, achieving the best values for R² and the smallest errors using minimal core sensors (typically dominant side). Enthusiasts required multi-site sensors to compensate for greater execution variability. Increasing sensors beyond three yielded no performance gains; optimal configurations involved 2–3 core accelerometers combined with heart rate data. Expanding sample size significantly enhanced model stability and generalizability (e.g., running task R² increased from 0.49 (N = 20) to 0.95 (N = 40)). Triple-E evaluation indicated that strategic sensor minimization coupled with sufficient sample size maximized predictive performance while reducing computational cost and deployment burden. Competitive level significantly influences EE modeling requirements. Elite athletes are suited to a “low-sensor, small-sample” scenario, whereas enthusiasts necessitate a “multi-sensor, large-sample” strategy. Full article

Natural and unconstrained locomotion remains a fundamental challenge in creating truly immersive virtual reality (VR) experiences. This paper presents the design and control of a novel robotic omnidirectional treadmill (ODT) based on the bilateral symmetry of two cooperative five-bar planar mechanisms designed to replicate realistic walking mechanics. The central contribution is a human in the loop control strategy designed to achieve stable walking in place. This framework employs a specific control strategy that actively repositions the footplates along a dynamically defined ‘Line of Movement’ (LoM), compensating for the user’s motion to ensure the midpoint between the feet remains stabilized and symmetrical at the platform’s geometric center. A comprehensive dynamic model of both the ODT and a coupled humanoid robot was developed to validate the system. Numerical simulations demonstrate robust performance across various gaits, including turning and catwalks, maintaining the user’s locomotion center with a maximum resultant drift error of 11.65 cm, a peak value that occurred momentarily during a turning motion and remained well within the ODT’s safe operational boundaries, with peak errors along any single axis remaining below 9 cm. The system operated with notable efficiency, requiring RMS torques below 22 Nm for the primary actuators. This work establishes a viable dynamic and control architecture for foot-tracking ODTs, paving the way for future enhancements such as haptic terrain feedback and elevation simulation. Full article

This paper focused on the implementation method and results of modifying a Raspberry Pi 4 for real-time control of brushless direct-current motors, with application in a semi-active two-axis ankle prosthesis. CANopen over EtherCAT was implemented directly on the Raspberry Pi to synchronize real-time communication between it and the motor controllers. Kinematic algorithms for setting ankle angles of zero to ten degrees in any combination of sagittal and frontal angles were implemented. To achieve reliable motor communication, where the motors continuously move, the distributed clock synchronization of Linux and Motor driver systems needs to have a finely tuned Proportional-Integral compensation and a consistent sampling period. Data collection involved moving the ankle through 33 unique pre-selected ankle configurations nine times. The system allowed for quick movement (mean settling time 0.192 s), reliable synchronization (standard deviation of 4.51 microseconds for sampling period), and precise movement (mean movement error less than 0.2 deg) for ankle angle changes and also a high update rate (250 microseconds sampling period) with modest CPU load (12.48%). This system aims to allow for the prosthesis to move within a single swing phase, enabling it to efficiently adapt to various speeds and terrains, such as walking on slopes, stairs, or around corners. Full article

This paper presents an 8 × 8 channel optoelectronic readout array (ORA) realized in the TSMC 180 nm 1P6M RF CMOS process for the applications of short-range light detection and ranging (LiDAR) sensors. We propose several circuit techniques in this work, including an amplitude-to-voltage (A2V) converter that reduces the notorious walk errors by intensity compensation and a time-to-voltage (T2V) converter that acquires the linear slope of the output signals by exploiting a charging circuit, thus extending the input dynamic range significantly from 5 μA_pp to 1.1 mA_pp, i.e., 46.8 dB. These results correspond to the maximum detection range of 8.2 m via the action of the A2V converter and the minimum detection range of 56 cm with the aid of the proposed T2V converter. Optical measurements utilizing an 850 nm laser diode confirm that the proposed 8 × 8 ORA with 64 on-chip avalanche photodiodes (APDs) can successfully recover the narrow 5 ns light pulses even at the shortest distance of 56 cm. Hence, this work provides a potential CMOS solution for low-cost, low-power, short-range LiDAR sensors. Full article

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope’s working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time–frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope’s output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time–frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope’s output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10⁻³°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h. Full article

Background: Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, a reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. Methods: In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on ten subjects while performing 5-min sitting and walking tasks. Results: Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. Conclusion: We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity. Full article

In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%. Full article

The rapid advancement of mobile laser scanner technology used for terrain mapping, among other things, imposes increasing requirements for scanning frequency and distance measurement accuracy. To meet these requirements, rangefinder modules are expected to operate with high echo signal dynamics and to allow accurate distance measurement even based on single-laser-pulse echo detection. Such performance can be potentially achieved using pulsed time-of-flight (ToF) laser rangefinders (LRF). In conventional ToF modules, however, the STOP signal (for time counter interruption) is generated using a straightforward fixed-threshold comparator method. Unfortunately, it corresponds to the so-called walk error, i.e., the dependence of the measured time of flight on the magnitude of the echo signal. In most ranging applications, however, the LRF detection channel can be exposed to an extremely large span of received echo power levels, which depend on the distance measured, type of target surface, atmospheric transmission, etc. Thus, the walk error is an inseparable element of the conventional ToF technique and creates a fundamental limit for its precision. This article presents a novel method of walk error compensation in real time. By using our authorial electronic circuit for measuring the magnitude of the echo signal, it is possible to effectively compensate for the walk error even when the echo signal brings the detection channel amplifiers into saturation. In addition, the paper presents a laboratory method for calibrating the walk error compensation curve. Full article

This paper explores the possibility of distributing the fields of view (FOVs) of a centralized lidar cluster using fixed mirrors for future use in safety applications in robotics and elsewhere. A custom modular lidar system with time-over-threshold (TOT) walk error compensation was developed for the experiments. It comprises a control board that provides the processing power and adjustable voltage regulation, and multiple individually addressable analogue front end (AFE) boards that each contain a transmitter, a receiver, time-to-digital (TDC) converters for pulse width measurements on the bot Tx and Rx side, and adjustable reference voltage generators for both the Tx and Rx pulse detection threshold. The lidar system’s performance with a target in the direct line of sight is compared to the configurations where the FOV is redirected with up to three mirrors in different configurations. The results show that the light path through the neighboring mirrors introduces a minor but noticeable measurement error on a portion of the measurement range. Full article

Quantum sensors are expected to offer significant advantages in magnetic- and gravity-aided navigation. However, these techniques depend on the local environmental conditions and require an alternative solution to ensure high position accuracy on shorter timescales. This paper explores the potential of a hypothetical quantum Inertial Measurement Unit (IMU) with much better performance than classical IMUs when used for dead reckoning position estimation preceded by an alignment period to achieve a relative inertial navigation solution. We show that significantly reduced noise levels (velocity random walk) for the accelerometer and/or gyroscope cannot be automatically exploited. In practice, axis misalignment and initial state errors in orientation limit performance. Incorrect orientation causes errors in compensating for the gravity vector, having a dominant effect. This research proposes to introduce an alignment period with a reliable velocity sensor, prior to starting the mission relying solely on inertial navigation. The orientation errors can be estimated with numerical optimization, in which we match the dead reckoning estimate to the reference velocity signal. The fact that quantum IMUs have a much more accurate measurement means that these orientation errors could be compensated much more accurately. When mitigating these dominant error sources, there is a significant benefit to using a quantum IMU for inertial navigation. The initial position error growth is significantly smaller than existing systems. Some estimates are given for the necessary quality of the IMUs to benefit from these protocols and offer a similar position solution quality to high-end or military-grade IMUs. In the future, military operations that do not rely on Global Navigation Satellite System (GNSS) for their Positioning, Navigation and Timing (PNT) information may be performed with unprecedented position accuracy when using a quantum IMU complemented with velocity-aided alignment period, thereby increasing the effectiveness and dependability of the operation. Full article

Bowden cable–fabric is a key force transfer device for flexible exoskeletons, and its precise control of force/displacement is a significant factor in the human–machine interaction of flexible exoskeletons. In this paper, a force/displacement control method based on friction compensation and impedance control was proposed based on a flexible Bowden cable–fabric force transfer testbed system. First, a set of in vitro experimental platforms simulating Bowden cable–fabric force transfer was built according to a typical flexible exoskeleton force transfer system, and following the walking gait of lower limbs, the expected force and knee joint motion were set. Secondly, the Bowden cable–fabric force transfer friction model was constructed as the basis of the system’s force transfer compensation. In addition, the stiffness model of Bowden cable–fabric and the lower leg movement model were established and combined with impedance control to realize the precise control of system displacement. Finally, the damping and stiffness parameters suitable for the system were obtained through the impedance control simulation. In terms of the experiment, an in vitro Bowden cable–fabric force transfer experimental platform was built, and the expected force with the input peak value of 40 N, 50 N, and 60 N was set. Through the friction and position compensation model of Bowden cable–fabric force transfer and impedance control, the relative root-mean-square errors of the output force and expected force were obtained as 2.53%, 2.16%, and 2.07%, respectively. Therefore, the effectiveness of the proposed method is verified, which provides a foundation for the engineering application of flexible exoskeletons. Full article

This paper presents a test methodology to facilitate the measuring processes of LiDAR receiver ICs by avoiding the inherent walk error issue. In a typical LiDAR system, a costly laser diode driver emits narrow light pulses with fast rising edges, and the reflected pulses from targets enter an optical detector followed by an analog front-end (AFE) circuit. Then, the received signals pass through the cascaded amplifiers down to the time-to-digital converter (TDC) that can estimate the detection range. However, this relatively long signal journey leads to the significant decline of rising-edge slopes and the output pulse spreading, thus producing inherent walk errors in LiDAR receiver ICs. Compensation methods requiring complex algorithms and extra chip area have frequently been exploited to lessen the walk errors. In this paper, however, a simpler and lower-cost methodology is proposed to test LiDAR receiver ICs by employing a high-speed buffer and variable delay cells right before the TDC. With these circuits, both START and STOP pulses show very similar pulse shapes, thus effectively avoiding the walk error issue. Additionally, the time interval between two pulses is easily determined by varying the number of the delay cells. Test chips of the proposed receiver IC implemented in a 180-nm CMOS process successfully demonstrate easier and more accurate measurement results. Full article

Wide-range application scenarios, such as industrial, medical, rescue, etc., are in various demand for human spatial positioning technology. However, the existing MEMS-based sensor positioning methods have many problems, such as large accuracy errors, poor real-time performance and a single scene. We focused on improving the accuracy of IMU-based both feet localization and path tracing, and analyzed three traditional methods. In this paper, a planar spatial human positioning method based on high-resolution pressure insoles and IMU sensors was improved, and a real-time position compensation method for walking modes was proposed. To validate the improved method, we added two high-resolution pressure insoles to our self-developed motion capture system with a wireless sensor network (WSN) system consisting of 12 IMUs. By multi-sensor data fusion, we implemented dynamic recognition and automatic matching of compensation values for five walking modes, with real-time spatial-position calculation of the touchdown foot, enhancing the 3D accuracy of its practical positioning. Finally, we compared the proposed algorithm with three old methods by statistical analysis of multiple sets of experimental data. The experimental results show that this method has higher positioning accuracy in real-time indoor positioning and path-tracking tasks. The methodology can have more extensive and effective applications in the future. Full article

Stride length (SL), foot clearance (FC), and foot progression angle (FPA) are the key parameters for diagnosing gait disorders. This study used the distance data between two feet measured by ultra-wideband (UWB) sensors installed on shoes and proposed a method for estimating the three gait parameters. Here, a method of compensating the offset of the UWB sensor and estimating the distances between a base sensor installed on one foot during the stance phase and three UWB sensors on the other during the swing phase was applied. Foot trajectory was acquired in a gait experiment with ten healthy adults walking on a treadmill. The results were compared with those obtained using a motion capture system (MCS). The UWBs sensor displayed average errors of 45.84 mm, 7.60 mm, and 2.82° for SL, FC, and FPA, respectively, compared with the MCS. A similar accuracy level was achieved in a previous study that used an inertial measurement unit (IMU). Thus, these results suggest that UWB sensors can be extensively applied to sensor systems used to analyze mobile gait systems. Full article