Wearable Motion Analysis System for Thoracic Spine Mobility With Inertial Sensors.

This study presents a wireless wearable portable system designed for the automatic quantitative spatio-temporal analysis of continuous thoracic spine motion across various planes and degrees of freedom (DOF). This includes automatic motion segmentation, computation of the range of motion (ROM) for six distinct thoracic spine movements across three planes, tracking of motion completion cycles, and visualization of both primary and coupled thoracic spine motions. To validate the system, this study employed an Inter-days experimental setting to conduct experiments involving a total of 957 thoracic spine movements, with participation from two representatives of varying age and gender. The reliability of the proposed system was assessed using the Intraclass Correlation Coefficient (ICC) and Standard Error of Measurement (SEM). The experimental results demonstrated strong ICC values for various thoracic spine movements across different planes, ranging from 0.774 to 0.918, with an average of 0.85. The SEM values ranged from 0.64° to 4.03°, with an average of 1.93°. Additionally, we successfully conducted an assessment of thoracic spine mobility in a stroke rehabilitation patient using the system. This illustrates the feasibility of the system for actively analyzing thoracic spine mobility, offering an effective technological means for non-invasive research on thoracic spine activity during continuous movement states.

Robot-assisted anterior cruciate ligament reconstruction based on three-dimensional images.

Background Tunnel placement is a key step in anterior cruciate ligament (ACL) reconstruction. The purpose of this study was to evaluate the accuracy of bone tunnel drilling in arthroscopic ACL reconstruction assisted by a three-dimensional (3D) image-based robot system. Methods Robot-assisted ACL reconstruction was performed on twelve freshly frozen knee specimens. During the operation, three-dimensional images were used for ACL bone tunnel planning, and the robotic arm was used for navigation and drilling. Twelve patients who underwent traditional arthroscopic ACL reconstruction were included. 3D computed tomography was used to measure the actual position of the ACL bone tunnel and to evaluate the accuracy of the robotic and traditional ACL bone tunnel. Results On the femoral side, the positions of robotic and traditional surgery tunnels were 29.3 ± 1.4% and 32.1 ± 3.9% in the deep-to-shallow direction of the lateral femoral condyle (p = 0.032), and 34.6 ± 1.2% and 21.2 ± 9.4% in the high-to-low direction (p < 0.001), respectively. On the tibial side, the positions of the robotic and traditional surgical tunnels were located at 48.4 ± 0.9% and 45.8 ± 2.8% of the medial-to-lateral diameter of the tibial plateau (p = 0.008), 38.1 ± 0.8% and 34.6 ± 6.0% of the anterior-to-posterior diameter (p = 0.071), respectively. Conclusions In this study, ACL reconstruction was completed with the assistance of a robot arm and 3D images, and the robot was able to drill the bone tunnel more accurately than the traditional arthroscopic ACL reconstruction.

Controlling flat-foot limit cycle walkers with compliant joints based on local stability variation.

This study investigates local stability of a four-link limit cycle walking biped with flat feet and compliant ankle joints. Local stability represents the behavior along the solution trajectory between Poincare sections, which can provide detailed information about the evolution of disturbances. The effects of ankle stiffness and foot structure on local stability are studied. In addition, we apply a control strategy based on local stability analysis to the limit cycle walker. Control is applied only in the phases with poor local stability. Simulation results show that the energy consumption is reduced without sacrificing disturbance rejection ability. This study may be helpful in motion control of limit cycle bipedal walking robots with flat feet and ankle stiffness and understanding of human walking principles.

Muscle redistribution technique for expressing motion intention in patients with wrist-level amputation.

This study reports the preliminary results of a technique for redistributing muscles at the wrist in the stump of hand amputees by suturing the tendons to the dermis. The technique has the potential to improve control of hand prostheses by detecting movement intentions.

Self-Powered Textile Triboelectric Pulse Sensor for Cardiovascular Monitoring.

Cardiovascular diseases have become a severe threat to human health. Fortunately, most of them can be effectively assessed and prevented through long-term monitoring of cardiovascular signals. Wearable medical sensors play an essential role in monitoring human physiological health, which are heading towards ultra-low power consumption, high sensitivity and stability. Furthermore, a comfortable wearable sensor also needs to be flexible and breathable. Here, a self-powered textile pulse sensor (STPS) based on triboelectric nanogenerator (TENG) is demonstrated for real-time monitoring of the radial artery pulse waveform. STPS can directly convert tiny pressure signals into electrical signals with excellent linearity (R2 = 0.996), low detection limit, and long-term stable performance (5×104 cycles). The flexible textile-based STPS can be conformally attached to the human body for continuously and stably recording physiological mechanical signals, which is expected to be utilized in the personalized cardiovascular pulse monitoring wearable devices in the Internet of Things era.

Mechanistic Study on the Possibility of Converting Dissociated Oxygen into Formic Acid on χ-Fe5C2(510) for Resource Recovery in Fischer-Tropsch Synthesis.

During Fischer-Tropsch synthesis, O atoms are dissociated on the surface of Fe-based catalysts. However, most of the dissociated O would be removed as H2O or CO2, which results in a low atom economy. Hence, a comprehensive study of the O removal pathway as formic acid has been investigated using the combination of density functional theory (DFT) and kinetic Monte Carlo (kMC) to improve the economics of Fischer-Tropsch synthesis on Fe-based catalysts. The results show that the optimal pathway for the removal of dissociated O as formic acid is the OH pathway, of which the effective barrier energy (0.936 eV) is close to that of the CO activation pathway (0.730 eV), meaning that the removal of dissociated O as formic acid is possible. The main factor in an inability to form formic acid is the competition between the formic acid formation pathway and other oxygenated compound formation pathways (H2O, CO2, methanol-formaldehyde); the details are as follows: 1. If the CO is hydrogenated first, then the subsequent reaction would be impossible due to its high effective Gibbs barrier energy. 2. If CO reacts first with O to become CO2, it is difficult for it to be hydrogenated further to become HCOOH because of the low adsorption energy of CO2. 3. When the CO + OH pathway is considered, OH would react easily with H atoms to form H2O due to the hydrogen coverage effect. Finally, the removal of dissociated O to formic acid is proposed via improving the catalyst to increase the CO2 adsorption energy or CO coverage.

Hip Exoskeleton Based Indirect Intervention Strategy Towards Gait Training After Total Knee Arthroplasty.

Total Knee Arthroplasty (TKA) is the most effective approach for function restoration in patients with severe knee osteoarthritis. However, kinematic, kinetic and muscle activation differences between post-TKA patients and healthy people can be observed in many studies. Exoskeletons have been applied to post-TKA rehabilitation for many years, while few studies concentrated on the stance phase abnormality, neither in the aspect of kinematics nor in muscle activation. In this paper, we propose an indirect resistance strategy for post-operative TKA patient gait training. Three healthy subjects were asked to wear the hip exoskeleton and provided with 8 N·m resistance on the hip extension phase of the gait cycle. The intervention leads to an increment in the knee extension muscle activity as well as the augmentation in maximum knee angle in loading response. The results indicated that the application of resistance in the hip extension phase is a potential therapeutic approach for post-TKA rehabilitation, and may increase the gait training efficiency in the near future.

Robotic Brace Based Multi-Dimensional Assessment for Trunk Ability: A Preliminary Study in Patients with Spinal Cord Injury.

Evaluating trunk control ability is significant in guiding patients towards proper functional training. Most existing devices have only a singular assessment function, resulting in prolonged and asynchronous assessments. Devices with multi-dimensional assessment capabilities may address these limitations. This study utilizes a robotic brace, RoboBDsys-II, to assess the trunk ability of individuals with spinal disorders and to validate its effectiveness. The device can simultaneously collect kinematic, kinetic, and center of pressure data, reducing the assessment time and enabling the simultaneous assessment. The force platform is designed to measure the center of pressure and the force control of the parallel module is developed for the coronal movement assessment. Four patients with spinal cord injury participated in the study to assess their trunk range of motion and muscle strength. Results demonstrate that the trunk range of motion determines the center of pressure metrics in lateral bending experiments. Furthermore, RoboBDsys-II exhibits excellent test-retest reliability in lateral bending experiments and can reveal the muscle strength differences in different directions. The system has potential advantage in the trunk ability assessment.

A Learning-Free Method for Locomotion Mode Prediction by Terrain Reconstruction and Visual-Inertial Odometry.

This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human movements and sparse features. Evidence of its effectiveness, both in terms of accuracy and operational time consumption, is substantiated through tests conducted using open-source dataset and closed-loop evaluations. Comprehensive experiments were undertaken to validate its prediction accuracy across various test conditions such as subjects, scenarios, sensor mounting positions, camera views, step sizes, walking directions, and disturbances from moving obstacles. A comprehensive prediction accuracy rate of 99.00% confirms the efficacy, generality, and robustness of the proposed method.

Recording the angular dispersion of a terahertz beam into its frequency spectrum for fast measurements.

The frequency-dependent divergence angle of terahertz (THz) beams is a crucial aspect in understanding the generation and transmission of broadband THz waves. However, traditional beam profiling methods, such as 1D or 2D translation/rotation scanning detection, are time-consuming and wasteful of THz energy, making them unsuitable for fast measurement applications, such as single-shot THz generation and detection. Here, we proposed a simple solution that involves passing the THz beam through a core-anti-resonant reflective (CARR) cavity (e.g., a paper tube). The spatial information of the beam is then recorded into its frequency spectrum, which can be easily detected by a following traditional THz time-domain spectroscopy (TDS) system or a single-shot sampling setup. Our method enables the acquisition of the angular dispersion without repetitive measurements, and represents a significant step forward in fast and efficient achievement of spatial properties of broadband THz beams.

A Wearable Co-Located Neural-Mechanical Signal Sensing Device for Simultaneous Bimodal Muscular Activity Detection.

The co-located and concurrent measurement of both muscular neural activity and muscular deformation is considered necessary in many applications, such as medical robotics, assistive exoskeletons and muscle function evaluations. Nevertheless, conventional muscle-related signal perception systems either detect only one of these modalities, or are made with rigid and bulky components that cannot provide conformal and flexible interface. Herein, a flexible, easy-to-fabricate, bimodal muscular activity sensing device, which collects neural and mechanical signal at the same muscle location, is reported. The sensing patch includes a screen-printed sEMG sensor, and a pressure-based muscular deformation sensor (PMD sensor) based on a highly sensitive, co-planar iontronic pressure sensing unit. Both sensors are integrated on a super-thin (25 µm) substrate. The sEMG sensor shows a high signal-to-noise ratio of 37.1 dB, and the PMD sensor sensor exhibits a high sensitivity of 70.9 kPa -1. The responses of the sensor to three types of muscle activities (isotonic, isometric, and passive stretching) were analyzed and validated by ultrasound imaging. Bimodal signals during dynamic walking experiments with different level-ground walking speeds were also investigated. The application of the bimodal sensor was verified in gait phase estimation, and results show that the assembly of both modalities significantly reduce (p < 0.05) the average estimation error across all subjects and all walking speeds to 3.82%. Demonstrations show the potential of this sensing device for informative evaluation of muscular activities, and its abilities in human-robot interaction.

Dynamic electromyography findings of the lower leg muscles during walking in spastic cerebral palsy children with hindfoot valgus.

BACKGROUND: Hindfoot valgus is one of the most prevalent foot deformities in cerebral palsy children. Investigating the muscle activation patterns of cerebral palsy children with hindfoot valgus is crucial to understand their abnormal gait different from typically developing children. METHODS: Electromyography data of 20 cerebral palsy children with hindfoot valgus and 20 typically developing children were recorded for tibialis anterior, peroneal longus, and gastrocnemius medialis. The activation onset and offset times, normalized peak electromyography amplitude, average electromyography amplitude and integral electromyography amplitude for 20 completed cycles were averaged for data analysis. The co-activation index and activation percentage of peroneal longus were used to evaluate the co-activation level for tibialis anterior and peroneal longus muscles. FINDINGS: Compared with typically developing children, the activation onset of tibialis anterior and the activation offset of tibialis anterior, peroneal longus, and gastrocnemius medialis were significantly delayed in cerebral palsy children; moreover, the muscle activation durations of tibialis anterior, peroneal longus, and gastrocnemius medialis were significantly longer, and the normalized average electromyography amplitude of tibialis anterior, peroneal longus and gastrocnemius medialis, and the normalized integral electromyography amplitude of tibialis anterior were significantly lower in cerebral palsy children. Furthermore, for cerebral palsy children, the co-activation index was greater, and the peroneal longus muscles activation percentage was lower in the stance phase and greater in the swing phase than that of typically developing children. INTERPRETATION: The lower leg muscle activation patterns in cerebral palsy children were found to be abnormal.

In pursuit of reconstructing missing human hands.

Exploring bio-intelligence of human limbs could provide a new perspective for reconstructing missing limbs.

Flexible Capacitive Sensing and Ultrasound Calibration for Skeletal Muscle Deformations.

Skeletal muscles are critical to human-limb motion dynamics and energetics, where their mechanical states are seldom explored in vitro due to practical limitations of sensing technologies. This article aims to capture mechanical deformations of muscle contraction using wearable flexible sensors, which is justified with model calibration and experimental validation. The capacitive sensor is designed with the composite of conductive fabric electrodes and the porous dielectric layer to increase the pressure sensitivity and prevent lateral expansions. In this way, the compressive displacement of muscle deformation is captured in the muscle-sensor coupling model in terms of sensor deformation and parameters of pretension, material, and shape properties. The sensing model is calibrated in a linear form using ultrasound medical imaging. The sensor is capable of measuring muscle strain of 70% with an error of <3.6% and temperature disturbance of <5.6%. After 10K cycles of compression, the drift is only 3.3%. Immediate application of the proposed method is illustrated by gait pattern identification, where the K-nearest neighbor prediction accuracy of squats, level walking, stair ascent/descent, and ramp ascent is over 97% with a standard deviation below 2.6% compared to that of 94.61 ± 4.24% for ramp descent, and the response time is 14.37 ± 0.52 ms. The wearable sensing method is valid for muscle deformation monitoring and gait pattern identification, and it provides an alternative approach to capture mechanical motions of muscles, which is anticipated to contribute to understand locomotion biomechanics in terms of muscle forces and metabolic landscapes.

A Flexible Iontronic Capacitive Sensing Array for Hand Gesture Recognition Using Deep Convolutional Neural Networks.

Hand gesture recognition, one of the most popular research topics in human-machine interaction, is extensively used in visual and augmented reality, sign language translation, prosthesis control, and so on. To improve the flexibility and interactivity of wearable gesture sensing interfaces, flexible electronic systems for gesture recognition have been widely studied. However, these systems are limited in terms of wearability, stability, scalability, and robustness. Herein, we report a flexible wearable hand gesture recognition system that is based on an iontronic capacitive pressure sensing array and deep convolutional neural networks. The entire capacitive array is integrated into a flexible silicone wristband and can be comfortably and conveniently wrapped around the wrist. The pressure sensing array, which is composed of an iontronic film sandwiched between two flexible screen-printed electrode arrays, exhibits a high sensitivity (775.8 kPa-1), fast response time (65 ms), and high durability (over 6000 cycles). Image processing techniques and deep convolutional neural networks are applied for sensor signal feature extraction and hand gesture recognition. Several contexts such as intertrial test (average accuracy of 99.9%), intersession rewearing (average accuracy of 93.2%), electrode shift (average accuracy of 83.2%), and different arm positions during measurement (average accuracy of 93.1%) are evaluated.

Expert consensus on the bone repair strategy for osteoporotic fractures in China.

Osteoporotic fractures, also known as fragility fractures, are prevalent in the elderly and bring tremendous social burdens. Poor bone quality, weak repair capacity, instability, and high failure rate of internal fixation are main characteristics of osteoporotic fractures. Osteoporotic bone defects are common and need to be repaired by appropriate materials. Proximal humerus, distal radius, tibia plateau, calcaneus, and spine are common osteoporotic fractures with bone defect. Here, the consensus from the Osteoporosis Group of Chinese Orthopaedic Association concentrates on the epidemiology, characters, and management strategies of common osteoporotic fractures with bone defect to standardize clinical practice in bone repair of osteoporotic fractures.

Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications.

Human-machine interfaces for hand gesture recognition across multiple sessions and days of doffing and re-donning while maintaining acceptable recognition accuracy are still challenging. In this paper, a flexible wristband, which was integrated with a highly sensitive capacitive pressure sensing array, was used for inter-day hand gesture recognition. The performance of the entire system was further improved by utilizing a triplet network for deep feature embedding. Seven hand gestures were included into the gesture set, and inter-day experiments which lasted for five consecutive days with three sessions on each day were conducted. Five healthy subjects participated in the experiment. Between each session, the wristband was doffed, and re-donned before the next session. The triplet network achieved an average recognition accuracy of 91.98% across all the sessions of all the subjects, and yielded a higher classification result (p < 0.05) over the convolutional neural network trained with softmax-cross-entropy loss (with an average accuracy of 84.65%). Furthermore, we also found that the capacitive array size had an evident influence on the inter-day classification result. The array with the full size (thirty-two channels) achieved a higher average recognition accuracy over all the down-sampled arrays. This work demonstrated the feasibility of improving the hand gesture recognition performance over days of usage by fabricating a wearable, flexible multi-channel capacitive wristband and implementing the triplet network.

A Low-Cost Flexible Perforated Respiratory Sensor Based on Platinum for Continuous Respiratory Monitoring.

Monitoring sleep conditions is of importance for sleep quality evaluation and sleep disease diagnosis. Accurate respiration detection provides key information about sleep conditions. Here, we propose a perforated temperature sensor that can be worn below the nasal cavity to monitor breath. The sensing system consists of two perforated temperature sensors, signal conditioning circuits, a transmission module, and a supporting analysis algorithm. The perforated structure effectively enhances the sensitivity of the system and shortens the response time. The sensor's response time is 0.07 s in air and sensitivity is 1.4‱°C-1. The device can achieve a monitoring respiratory temperature range between normal room temperature and 40 °C. The simple and standard micromachining process ensures low cost and high reproducibility. We achieved the monitoring of different breathing patterns, such as normal breathing, panting, and apnea, which can be applied to sleep breath monitoring and exercise information recording.

Data-Mined Continuous Hip-Knee Coordination Mapping With Motion Lag for Lower-Limb Prosthesis Control.

Trajectory planning of the knee joint plays an essential role in controlling the lower limb prosthesis. Nowadays, the idea of mapping the trajectory of the healthy limb to the motion trajectory of the prosthetic joint has begun to emerge. However, establishing a simple and intuitive coordination mapping is still challenging. This paper employs the method of experimental data mining to explore such a coordination mapping. The coordination indexes, i.e., the mean absolute relative phase (MARP) and the deviation phase (DP), are obtained from experimental data. Statistical results covering different subjects indicate that the hip motion possesses a stable phase difference with the knee, inspiring us to construct a hip-knee Motion-Lagged Coordination Mapping (MLCM). The MLCM first introduces a time lag to the hip motion to avoid conventional integral or differential calculations. The model in polynomials, which is proved more efficient than Gaussian process regression and neural network learning, is then constructed to represent the mapping from the lagged hip motion to the knee motion. In addition, a strong linear correlation between hip-knee MARP and hip-knee motion lag is discovered for the first time. By using the MLCM, one can generate the knee trajectory for the prosthesis control only via the hip motion of the healthy limb, indicating less sensing and better robustness. Numerical simulations show that the prosthesis can achieve normal gaits at different walking speeds.

Recognizing Continuous Multiple Degrees of Freedom Foot Movements With Inertial Sensors.

Recognition of continuous foot motions is important in robot-assisted lower limb rehabilitation, especially in prosthesis and exoskeleton design. For instance, perceiving foot motion is essential feedback for the robot controller. However, few studies have focused on perceiving multiple-degree of freedom (DOF) foot movements. This paper proposes a novel human-machine interaction (HMI) recognition wearable system for continuous multiple-DOF ankle-foot movements. The proposed system uses solely kinematic signals from inertial measurement units and multiclass support vector machines by creating error-correcting output codes. We conducted a study with multiple participants to validate the performance of the system using two strategies, a general model and a subject-specific model. The experimental results demonstrated satisfactory performance. The subject-specific approach achieved 98.45% ± 1.17% (mean ± SD) overall accuracy within a prediction time of 10.9 ms ± 1.7 ms, and the general approach achieved 85.3% ± 7.89% overall accuracy within a prediction time of 14.1 ms ± 4.5 ms. The results prove that the proposed system can more effectively recognize multiple continuous DOF foot movements than existing strategies. It can be applied to ankle-foot rehabilitation and fills the HMI high-level control demand for multiple-DOF wearable lower-limb robotics.