Publications

Motion-Tolerant Measurement of Respiration and Heartbeat via mmWave Radar Across Diverse Healthcare Scenarios

Noninvasive vital sign (e.g., respiration and heart-beat) monitoring via millimeter wave radar (mmWave) presents a promising paradigm for health management and disease diagnostics, yet the widespread adoption of this technology is often hindered by motion artifacts, environmental noise, and limited adaptability to diverse scenarios. This paper proposes a robust respiratory and heartbeat measurement approach that integrates enhanced radar sensing with adaptive decomposition algorithms for diverse healthcare environments. Initially, we introduce a target-signal cooperative mechanism that combines target detection for spatial localization with beamforming-driven directional adjustments and channel coherent accumulation, thereby enhancing the signal to noise ratio (SNR) in the region of interest. Then, the marine predators algorithm (MPA) is implemented to optimize variational mode decomposition (VMD) parameters for precise vital sign extraction. Prior to this, a cascaded signal processing pipeline is applied, comprising static clutter suppression, differentiation and cross multiplication (DACM) phase extraction, and signal smoothing to mitigate noise effects. Experimental validation encompasses two configurations: (1) multi-angle and multi-distance static indoor scenarios, (2) four motion scenarios with controlled body movements, designed to emulate real-world conditions. The experimental results indicate superior respiratory rate (RR) estimation performance, achieving a mean absolute error (MAE) of 1.02 bpm. For heartbeat rate (HR) monitoring, MAEs of 3.23 bpm (static scenarios, 95.78% accuracy) and 4.16 bpm (lightweight motion scenarios, 94.88% accuracy) demonstrate significant feasibility and substantial reliability.

A wireless gait measuring instrument based on plantar pressure measuring

In this paper, we developed a gait measuring instrument for those who are short on walking, such as children with cerebral palsy, post-stroke, and etc... This gait measuring instrument can measure the pressure distribution under sole of a patient's feet with a pair of insoles with tactile sensors fixed on them. Then the instrument sends the information to our PC wirelessly. The pc displays information of the pressure distribution and gait information to doctors with 3d interface. Thus, doctors can map out a rehabilitation program for every patient individually according to the information, helping them recover the ability to walk.

A Two-Layer Adaptive Assist-As-Needed Control Scheme for Rehabilitation Robotics

Design and Analysis of Cloud Upper Limb Rehabilitation System Based on Motion Tracking for Post-Stroke Patients

In order to improve the convenience and practicability of home rehabilitation training for post-stroke patients, this paper presents a cloud-based upper limb rehabilitation system based on motion tracking. A 3-dimensional reachable workspace virtual game (3D-RWVG) was developed to achieve meaningful home rehabilitation training. Five movements were selected as the criteria for rehabilitation assessment. Analysis was undertaken of the upper limb performance parameters: relative surface area (RSA), mean velocity (MV), logarithm of dimensionless jerk (LJ) and logarithm of curvature (LC). A two-headed convolutional neural network (TCNN) model was established for the assessment. The experiment was carried out in the hospital. The results show that the RSA, MV, LC and LJ could reflect the upper limb motor function intuitively from the graphs. The accuracy of the TCNN models is 92.6%, 80%, 89.5%, 85.1% and 87.5%, respectively. A therapist could check patient training and assessment information through the cloud database and make a diagnosis. The system can realize home rehabilitation training and assessment without the supervision of a therapist, and has the potential to become an effective home rehabilitation system.

Active Motion Control of a Knee Exoskeleton Driven by Antagonistic Pneumatic Muscle Actuators

The pneumatic muscle actuator (PMA) has been widely applied in the researches of rehabilitation robotic devices for its high power to weight ratio and intrinsic compliance in the past decade. However, the high nonlinearity and hysteresis behavior of PMA limit its practical application. Hence, the control strategy plays an important role in improving the performance of PMA for the effectiveness of rehabilitation devices. In this paper, a PMA-based knee exoskeleton based on ergonomics is proposed. Based on the designed knee exoskeleton, a novel proxy-based sliding mode control (PSMC) is introduced to obtain the accurate trajectory tracking. Compared with conventional control approaches, this new PSMC can obtain better performance for the designed PMA-based exoskeleton. Experimental results indicate good tracking performance of this controller, which provides a good foundation for the further development of assist-as-needed training strategies in gait rehabilitation.

Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis

An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA) algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated.

DCP-AHS: A High-Performance Distributed Cooperative Positioning Model for Concave Networks

Node positioning is an essential function of wireless networks and serves as the foundation for many applications. In the existing works, the cooperative positioning approaches have been extensively studied and are shown to be effective for scenarios with energy and cost constraints. However, these approaches may not perform well in concave networks with holes or obstacles. To address this issue, this paper proposes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed cooperative positioning model with adaptive hop-range selection</i> (DCP-AHS for short) for concave networks. DCP-AHS first uses a low-complexity and fast convergent distance estimation method based on the local neighbor nodes. It then uses an adaptive hop-range selection method based on the residual analysis between pairs of anchors. Within the hop range, an unknown node uses multi-lateration with the optimal weight function to determine its estimated position. Finally, a weighted Bounding-Box method with the virtual anchor is employed to avoid significant position estimation errors caused by the collinearity issues. Simulation results demonstrated that the proposed DCP-AHS significantly outperformed the existing algorithms regarding efficiency, accuracy, and stability in various concave networks. Specifically, our proposed model achieved a median improvement of 16.62% to 81.65% in positioning accuracy compared to the comparison algorithms.

Psychophysiological classification and experiment study for spontaneous EEG based on two novel mental tasks

BACKGROUND: Study of imagination offers a perfect setting for study of a large variety of states of consciousness.OBJECTIVE: Here, we studied the characteristics of two electroencephalographic (EEG) patterns evoked by two different imaginary tasks and evaluated the binary classification performance.METHODS: Fifteen individuals (11 male and 4 female, age range of 22 to 33) participated in five sessions of 32-channel EEG recordings.Only by analyzing the subjects' output EEG signals from the central parieto-occipital region of PZ electrode, under the circumstances of consciousness of relaxation-meditation or tension-imagination, we carried out the experiment of feature extraction for spontaneous EEG, as the subjects were blindfolded but asked to open their eyes all the same.The Hilbert-Huang Transform (HHT) was utilized to obtain the Hilbert time-frequency amplitude spectrum, and then with the feature vector set extracted, a two-class Fisher linear discriminant analysis classifier was trained for classification of data epochs of those two tasks.RESULTS: The overall result was that about 90% (± 5%) of the epochs could be correctly classified to their originating task.CONCLUSION: This study not only brings new opportunities for consciousness studies, but also provides a new classification paradigm for achieving control of robots based on the brain-computer interface (BCI).

Design and Validation of an Aerodynamics and Kinematics Measurement Platform for Flapping-Wing Robots

Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy

Isopropyl alcohol molecules, as a biomarker for anti-virus diagnosis, play a significant role in the area of environmental safety and healthcare relating volatile organic compounds. However, conventional gas molecule detection exhibits dramatic drawbacks, like the strict working conditions of ion mobility methodology and weak light-matter interaction of mid-infrared spectroscopy, yielding limited response of targeted molecules. We propose a synergistic methodology of artificial intelligence-enhanced ion mobility and mid-infrared spectroscopy, leveraging the complementary features from the sensing signal in different dimensions to reach superior accuracy for isopropyl alcohol identification. We pull in "cold" plasma discharge from triboelectric generator which improves the mid-infrared spectroscopic response of isopropyl alcohol with good regression prediction. Moreover, this synergistic methodology achieves ~99.08% accuracy for a precise gas concentration prediction, even with interferences of different carbon-based gases. The synergistic methodology of artificial intelligence-enhanced system creates mechanism of accurate gas sensing for mixture and regression prediction in healthcare.

Detection of self-paced movement intention from pre-movement electroencephalogram signals with Hilbert transform

The movement related cortical potential (MRCP) is a well-known neural signature of human's self-paced movement intention, which can be exploited by future neuroprosthesis. Most existing studies have explored the amplitude representation for the movement intention. In this paper we investigate the Hilbert transformed MRCP, which implicitly includes phase information complementarily to amplitude information, for the detection of self-paced upper-limb movement intention. On the datasets in which 5 healthy subjects executed a self-initiated upper limb center-out reaching task in three sessions, we have evaluated the detection model with Hilbert transformed MRCP as features and the state-of-art one with the original MRCP amplitude as features. Results show that the Hilbert transformed MRCP based detector is more accurate than the original MRCP based one.

Real time stiffness display interface device for perception of virtual soft object

A novel method based on deformable length of elastic element control (DLEEC) to realize the stiffness display for perception of virtual soft object is proposed, and the stiffness display interface device has been developed and is presented. The stiffness display interface device is composed of a thin elastic beam and an actuator to adjust the length of the beam. The deformation of the beam under a force is proportional to the third power of the beam length. By controlling the beam length, the stiffness display device can reproduce the stiffness of the virtual object from very soft to hard, so that the human fingertip can feel it as if he directly touches with the virtual object by interacting with the device. A real time position control algorithm is employed to guarantee the real time stiffness display.

Regularization feature selection projection twin support vector machine via exterior penalty

Development of a Lightweight Underwater Manipulator for Delicate Structural Repair Operations

In recent years, underwater robots have been increasingly used in the maintenance of hydraulic structures. Underwater manipulators are essential devices that are used to carry out such maintenance tasks. For delicate repair operations such as fixing tiny cracks, most existing underwater manipulators face limitations in terms of size, accuracy, and scalability. Therefore, in this letter, we present a novel electric underwater manipulator, named SEU-4. This four-degree-of-freedom manipulator weighs 8.91 kg and has a maximum payload of 9 kg. It has a rapid-switching interface that supports convenient mechanical and electrical connections for end-effectors. To compensate for the disturbances that are present in the complex underwater environment, a trajectory-tracking control strategy based on a disturbance observer and sliding-mode control (DOB-SMC) is proposed. A prototype of the proposed underwater manipulator was created, and a flowing-water experimental platform was constructed to test its trajectory-tracking performance in fast-flowing water. The experimental results show that the manipulator achieves a trajectory-tracking error of 1.03 mm in static water and 2.91 mm in flowing water at 1.2 m/s, which satisfies the requirements of delicate repair operations.

Feel the inside: A haptic interface for navigating stress distribution inside objects

A Geometric Folding Pattern for Robot Coverage Path Planning

Conventional coverage path planning algorithms are mainly based on the zigzag and spiral patterns or their combinations. The traversal order is limited by the linear or inside-outside manner. We propose a new set of coverage patterns induced from geometric folding operations, called the geometric folding pattern, to make coverage paths with more flexible traversal order. We study the modeling and parameterization of the geometric folding patterns. Then, a sampling operator is introduced. Based on the computational tools, we demonstrate the application of the proposed patterns in designing coverage paths. We show that the simple geometric folding patterns are flexible and controllable, which enables more choices for the coverage path planning problem.

The Design and Implementation of a Somatosensory Tactile Actuator and the Dynamic Calibration System for Its Output Frequencies