Intelligent ankle-foot prosthesis based on human structure and motion bionics.

The ankle-foot prosthesis aims to compensate for the missing motor functions by fitting the motion characteristics of the human ankle, which contributes to enabling the lower-limb amputees to take care of themselves and improve mobility in daily life. To address the problems of poor bionic motion of the ankle-foot prosthesis and the lack of natural interaction among the patient, prosthesis, and the environment, we developed a complex reverse-rolling conjugate joint based on the human ankle-foot structure and motion characteristics, the rolling joint was used to simulate the rolling-sliding characteristics of the knee joint. Meanwhile, we established a segmental dynamics model of the prosthesis in the stance phase, and the prosthetic structure parameters were obtained with the optimal prosthetic structure dimensions and driving force. In addition, a carbon fiber energy-storage foot was designed based on the human foot profile, and the dynamic response of its elastic strain energy at different thicknesses was simulated and analyzed. Finally, we integrated a bionic ankle-foot prosthesis and experiments were conducted to verify the bionic nature of the prosthetic joint motion and the energy-storage characteristics of the carbon fiber prosthetic foot. The proposed ankle-foot prosthesis provides ambulation support to assist amputees in returning to social life normally and has the potential to help improve clinical viability to reduce medical rehabilitation costs.

Laparoscopic Sigmoid Vaginoplasty for the Treatment of Mayer-Rokitansky-Kuster-Hauser Syndrome in a Single Center: 20 years' Experience.

INTRODUCTION AND HYPOTHESIS: We investigate the feasibility, safety, and clinical therapeutic effect of laparoscopic sigmoid vaginoplasty in women with Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome. METHODS: We performed a retrospective case review cohort study of 56 patients with MRKHs undergoing laparoscopic sigmoid vaginoplasty in Wuhan Union Hospital between 2000 and 2020, and all patients were followed up. RESULTS: The median operating time was 165 min (120-420 min). The median hospital stay was 10 days (rang 7-15 days). A functional neovagina was created 11-15 cm in length and two fingers in breadth in all patients. No introitus stenosis was observed. No intra- or post-operative complications occurred. Two patients were lost to follow-up after 3 months of outpatient visits. Six patients had no intercourse and were required to wear a vaginal mold occasionally. None of the patients had complained of local irritation or dyspareunia. Patients who had post-surgery sexual intercourse were satisfied with their sexual life and the mean total Female Sexual Function Index (FSFI) score was 25.17 ± 0.63. The cosmetic results were excellent. CONCLUSIONS: The laparoscopic sigmoid vaginoplasty can achieve the goal of making a functional neovagina. The main advantage of this surgical technique is that it is minimally invasive and that there are fewer complications post-operation. It is an acceptable procedure for patients with MRKH syndrome.

Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges.

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination ( [Formula: see text] and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.

Application of 18ß-glycyrrhetinic acid in the structural modification of natural products: a review.

18ß-Glycyrrhetinic acid (GA) is an oleane-type pentacyclic triterpene saponin obtained from glycyrrhizic acid by removing 2 glucuronic acid groups. GA and its analogues are active substances of glycyrrhiza aicd, with similar structure and important pharmacological effects such as anti-inflammatory, anti-diabetes, anti-tumor and anti-fibrosis. Although GA combined compounds are in the clinical trial stages, its application potential is severely restricted by its low bioavailability, water solubility and membrane permeability. In this article, synthetic methods and structure-activity relationships (SARs) of GA derivatives from 2018 to present are reviewed based on pharmacological activity. It is hoped that this review can provide reference for the future development of potential GA preclinical candidate compounds, and furnish ideas for the development of pentacyclic triterpenoid lead compounds.

Towards humanlike grasp in robotic hands: mechanical implementation of force synergies.

In the field of robotic hands, finger force coordination is usually achieved by complex mechanical structures and control systems. This study presents the design of a novel transmission system inspired from the physiological concept of force synergies, aiming to simplify the control of multifingered robotic hands. To this end, we collected human finger force data during six isometric grasping tasks, and force synergies (i.e. the synergy weightings and the corresponding activation coefficients) were extracted from the concatenated force data to explore their potential for force modulation. We then implemented two force synergies with a cable-driven transmission mechanism consisting of two spring-loaded sliders and five V-shaped bars. Specifically, we used fixed synergy weightings to determine the stiffness of the compression springs, and the displacements of sliders were determined by time-varying activation coefficients. The derived transmission system was then used to drive a five-finger robotic hand named SYN hand. We also designed a motion encoder to selectively activate desired fingers, making it possible for two motors to empower a variety of hand postures. Experiments on the prototype demonstrate successful grasp of a wide range of objects in everyday life, and the finger force distribution of SYN hand can approximate that of human hand during six typical tasks. To our best knowledge, this study shows the first attempt to mechanically implement force synergies for finger force modulation in a robotic hand. In comparison to state-of-the-art robotic hands with similar functionality, the proposed hand can distribute humanlike force ratios on the fingers by simple position control, rather than resorting to additional force sensors or complex control strategies. The outcome of this study may provide alternatives for the design of novel anthropomorphic robotic hands, and thus show application prospects in the field of hand prostheses and exoskeletons.

Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking.

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate classification of MUAPs. The goal of this study was to propose an effective method based on the convolutional neural network (CNN) to classify MUAPs with high levels of variation for MU tracking. MUAP variation was added artificially in the synthetic electromyogram (EMG) signals and was induced by changing the forearm postures in the experimental study. The proposed overlapped-segment-wise EMG decomposition method and the spike-triggered averaging method were combined to obtain the MUAP waveform samples of individual MUs in the experimental study, and the MUAP profile classification performance was tested. Since the ground-truth of MU discharge activities was known for the synthetic EMG, the MU tracking performance was further verified by mimicking the tracking procedure of MU discharge activities and the spike consistency with the true spike trains was tested in the simulation study. The conventional MUAP similarity index (SI)-based method was also performed as comparison. For both the experimental and the synthetic EMG signals, the CNN-based method significantly improved the MUAP tracking performance compared with the conventional SI-based method manifested as a higher classification accuracy (93.3%±5.4% vs 56.2%±13.9%) in the experimental study or higher spike consistency (71.1%±10.2% vs 29.2%±11.0%) in the simulation study with a smaller variation. These results demonstrated the efficiency and robustness of the proposed method to distinguish MUAPs with large variations accurately. Further development of the proposed method can promote the study on the physiological and pathological changes of the neuromuscular system where tracking MU activities is needed.

Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection.

OBJECTIVE: The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. METHODS: To tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. RESULTS: Unlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. CONCLUSION: This research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. SIGNIFICANCE: The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.

Introduction: In recent years, more and more attention has been paid to the visual fatigue caused by steady state visual evoked potential (SSVEP) paradigm. It is well known that the large-scale application of brain-computer interface is closely related to SSVEP, and the fatigue caused by SSVEP paradigm leads to the reduction of application effect. At present, the mainstream method of objectively quantifying visual fatigue in SSVEP paradigm is based on traditional canonical correlation analysis (CCA). Methods: In this paper, we propose a new SSVEP paradigm visual fatigue quantification algorithm based on underdamped second-order stochastic resonance (USSR) to accurately quantify visual fatigue caused by SSVEP paradigm in different working modes using single-channel electroencephalogram (EEG) signals. This scheme uses the fixed-step energy parameter optimization algorithm we designed, combined with the USSR model, to significantly improve the signal-to-noise ratio of the processed signal at the target characteristic frequency. We not only compared the new algorithm with CCA, but also with the traditional subjective quantitative visual fatigue gold standard Likert fatigue scale. Results: There was no significant difference (p = 0.090) between the quantitative value of paradigm fatigue obtained by the single channel SSVEP processed by the new algorithm and the gold standard of subjective fatigue quantification, while there was a significant difference (p < 0.001***) between the quantitative value of paradigm fatigue obtained by the traditional multi-channel CCA algorithm and the gold standard of subjective fatigue quantification. Discussion: The conclusion shows that the quantization value obtained by the new algorithm can better match the subjective gold standard score, which also shows that the new algorithm is more reliable, which reflects the superiority of the new algorithm.

Bionic intelligent ankle-foot prosthesis based on the conjugate curved surface.

Given the poor biomimetic motion of traditional ankle-foot prostheses, it is of great significance to develop an intelligent prosthesis that can realize the biomimetic mechanism of human feet and ankles. To this end, we presented a bionic intelligent ankle-foot prosthesis based on the complex conjugate curved surface. The proposed prosthesis is mainly composed of the rolling conjugated joints with a bionic design and the carbon fiber energy-storage foot. We investigated the flexibility of the prosthetic ankle joint movement, and the ability of the prosthetic foot to absorb ground impact during the gait cycle. Experimental results showed the matching of the ankle/toe position relationship of the human foot during simulated walking, which is helpful to realize the biomimetic motion of the human foot and ankle. It can also help therapists and clinicians provide better rehabilitation for lower-limb amputees.

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study.

Objective.The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control.Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure.Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R2= 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R2= 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability.Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach may lay the foundation for high-performance myoelectric interfaces that allow users to control robotic hands in a more natural and intuitive manner.

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance.

Objective: Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR). Methods: To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features. Results: In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components. Conclusion: This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness. Significance: This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.

The effect of motion frequency and sound source frequency on steady-state auditory motion evoked potential.

The ability of humans to perceive motion sound sources is important for accurate response to the living environment. Periodic motion sound sources can elicit steady-state motion auditory evoked potential (SSMAEP). The purpose of this study was to investigate the effects of different motion frequencies and different frequencies of sound source on SSMAEP. The stimulation paradigms for simulating periodic motion of sound sources were designed utilizing head-related transfer function (HRTF) techniques in this study. The motion frequencies of the paradigm are set respectively to 1-10 Hz, 15 Hz, 20 Hz, 30 Hz, 40 Hz, 60 Hz, and 80 Hz. In addition, the frequencies of sound source of the paradigms were set to 500 Hz, 1000 Hz, 2000 Hz, 3000 Hz, and 4000 Hz at motion frequencies of 6 Hz and 40 Hz. Fourteen subjects with normal hearing were recruited for the study. SSMAEP was elicited by 500 Hz pure tone at motion frequencies of 1-10 Hz, 15 Hz, 20 Hz, 30 Hz, 40 Hz, 60 Hz, and 80 Hz. SSMAEP was strongest at motion frequencies of 6 Hz. Moreover, at 6 Hz motion frequency, the SSMAEP amplitude was largest at the tone frequency of 500 Hz and smallest at 4000 Hz. Whilst SSMAEP elicited by 4000 Hz pure tone was significantly the strongest at motion frequency of 40 Hz. SSMAEP can be elicited by periodic motion sound sources at motion frequencies up to 80 Hz. SSMAEP also has a strong response at lower frequency. Low-frequency pure tones are beneficial to enhance SSMAEP at low-frequency sound source motion, whilst high-frequency pure tones help to enhance SSMAEP at high-frequency sound source motion. The study provides new insight into the brain's perception of rhythmic auditory motion.

Enhancing Performance of Single-Channel SSVEP-Based Visual Acuity Assessment via Mode Decomposition.

This study aimed to improve the performance of single-channel steady-state visual evoked potential (SSVEP)-based visual acuity assessment by mode decomposition methods. Using the SSVEP dataset induced by the vertical sinusoidal gratings at six spatial frequency steps from 11 subjects, 3-40-Hz band-pass filtering and other four mode decomposition methods, i.e., empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and variational mode decomposition (VMD), were used to preprocess the single-channel SSVEP signals from Oz electrode. After comparing the SSVEP signal characteristics corresponding to each mode decomposition method, the visual acuity threshold estimation criterion was used to obtain the final visual acuity results. The agreement between subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for band-pass filtering (-0.095 logMAR), EMD (-0.112 logMAR), EEMD (-0.098 logMAR), ICEEMDAN (-0.093 logMAR), and VMD (-0.090 logMAR) was all pretty good, with an acceptable difference between FrACT and SSVEP acuity for band-pass filtering (0.129 logMAR), EMD (0.083 logMAR), EEMD (0.120 logMAR), ICEEMDAN (0.103 logMAR), and VMD (0.108 logMAR), finding that the visual acuity obtained by these four mode decompositions had a lower limit of agreement and a lower or close difference compared to the traditional band-pass filtering method. This study proved that the mode decomposition methods can enhance the performance of single-channel SSVEP-based visual acuity assessment, and also recommended ICEEEMDAN as the mode decomposition method for single-channel electroencephalography (EEG) signal denoising in the SSVEP visual acuity assessment.

Capturing synchronization with complexity measure of ordinal pattern transition network constructed by crossplot.

To evaluate the synchronization of bivariate time series has been a hot topic, and a number of measures have been proposed. In this work, by introducing the ordinal pattern transition network into the crossplot, a new method for measuring the synchronization of bivariate time series is proposed. After the crossplot been partitioned and coded, the coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. The crossplot transition entropy of the network is proposed as an indicator of the synchronization between two time series. To test the characteristics and performance of the method, it is used to analyse the unidirectional coupled Lorentz model and compared it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, robustness, good consistency and suitability for short time series. Finally, electroencephalogram (EEG) data from auditory-evoked potential EEG-biometric dataset are investigated, and some useful and interesting results are obtained.

Stimulation enhancement effect of the combination of exoskeleton-assisted hand rehabilitation and fingertip haptic stimulation.

Introduction: Providing stimulation enhancements to existing hand rehabilitation training methods may help stroke survivors achieve better treatment outcomes. This paper presents a comparison study to explore the stimulation enhancement effects of the combination of exoskeleton-assisted hand rehabilitation and fingertip haptic stimulation by analyzing behavioral data and event-related potentials. Methods: The stimulation effects of the touch sensations created by a water bottle and that created by cutaneous fingertip stimulation with pneumatic actuators are also investigated. Fingertip haptic stimulation was combined with exoskeleton-assisted hand rehabilitation while the haptic stimulation was synchronized with the motion of our hand exoskeleton. In the experiments, three experimental modes, including exoskeleton-assisted grasping motion without haptic stimulation (Mode 1), exoskeleton-assisted grasping motion with haptic stimulation (Mode 2), and exoskeleton-assisted grasping motion with a water bottle (Mode 3), were compared. Results: The behavioral analysis results showed that the change of experimental modes had no significant effect on the recognition accuracy of stimulation levels (p = 0.658), while regarding the response time, exoskeleton-assisted grasping motion with haptic stimulation was the same as grasping a water bottle (p = 0.441) but significantly different from that without haptic stimulation (p = 0.006). The analysis of event-related potentials showed that the primary motor cortex, premotor cortex, and primary somatosensory areas of the brain were more activated when both the hand motion assistance and fingertip haptic feedback were provided using our proposed method (P300 amplitude 9.46 µV). Compared to only applying exoskeleton-assisted hand motion, the P300 amplitude was significantly improved by providing both exoskeleton-assisted hand motion and fingertip haptic stimulation (p = 0.006), but no significant differences were found between any other two modes (Mode 2 vs. Mode 3: p = 0.227, Mode 1 vs. Mode 3: p = 0.918). Different modes did not significantly affect the P300 latency (p = 0.102). Stimulation intensity had no effect on the P300 amplitude (p = 0.295, 0.414, 0.867) and latency (p = 0.417, 0.197, 0.607). Discussion: Thus, we conclude that combining exoskeleton-assisted hand motion and fingertip haptic stimulation provided stronger stimulation on the motor cortex and somatosensory cortex of the brain simultaneously; the stimulation effects of the touch sensations created by a water bottle and that created by cutaneous fingertip stimulation with pneumatic actuators are similar.

A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation.

Introduction: Hemiparesis is a common consequence of stroke that severely impacts the life quality of the patients. Active training is a key factor in achieving optimal neural recovery, but current systems for wrist rehabilitation present challenges in terms of portability, cost, and the potential for muscle fatigue during prolonged use. Methods: To address these challenges, this paper proposes a low-cost, portable wrist rehabilitation system with a control strategy that combines surface electromyogram (sEMG) and electroencephalogram (EEG) signals to encourage patients to engage in consecutive, spontaneous rehabilitation sessions. In addition, a detection method for muscle fatigue based on the Boruta algorithm and a post-processing layer are proposed, allowing for the switch between sEMG and EEG modes when muscle fatigue occurs. Results: This method significantly improves accuracy of fatigue detection from 4.90 to 10.49% for four distinct wrist motions, while the Boruta algorithm selects the most essential features and stabilizes the effects of post-processing. The paper also presents an alternative control mode that employs EEG signals to maintain active control, achieving an accuracy of approximately 80% in detecting motion intention. Discussion: For the occurrence of muscle fatigue during long term rehabilitation training, the proposed system presents a promising approach to addressing the limitations of existing wrist rehabilitation systems.

A Precise Frequency Recognition Method of Short-Time SSVEP Signals Based on Signal Extension.

OBJECTIVE: Improving the Information Transfer Rate (ITR) is a popular research topic in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The higher recognition accuracy of short-time SSVEP signal is critical to improving ITR and achieving high-speed SSVEP-BCIs. However, the existing algorithms have unsatisfactory performance on recognizing short-time SSVEP signals, especially for calibration-free methods. METHOD: This study for the first time proposed improving the recognition accuracy of short-time SSVEP signals based on the calibration-free method by extending the SSVEP signal length. A signal extension model based on Multi-channel adaptive Fourier decomposition with different Phase (DP-MAFD) is proposed to achieve signal extension. Then the Canonical Correlation Analysis based on signal extension (SE-CCA) is proposed to complete the recognition and classification of SSVEP signals after extension. RESULT: The similarity study and SNR comparison analysis on public SSVEP datasets demonstrate that the proposed signal extension model has the ability to extend SSVEP signals. The classification results show that the proposed method outperforms Canonical Correlation Analysis (CCA) and Filter Bank Canonical Correlation Analysis (FBCCA) significantly in the measure of classification accuracy and information transmission rate (ITR), especially for short-time signals. The highest ITR of SE-CCA is improved to 175.61 bits/min at around 1s, while CCA is 100.55 bits/min at 1.75s and FBCCA is 141.76 bits/min at 1.25s. CONCLUSION: The signal extension method can improve the recognition accuracy of short-time SSVEP signals and further improve the ITR of SSVEP-BCIs.

Computed tomography Osteoabsorptiometry: Review of bone density, mechanical strength of material and clinical application.

Computed Tomography (CT) imaging is an effective non-invasive examination. It is widely used in the diagnosis of fractures, arthritis, tumor, and some anatomical characteristics of patients. The density value (Hounsfield unit, HU) of a material in computed tomography can be the same for materials with varying elemental compositions. This value depends on the mass density of the material and the degree of X-ray attenuation. Computed Tomography Osteoabsorptiometry (CTOAM) imaging technology is developed on the basis of CT imaging technology. By applying pseudo-color image processing to the articular surface, it is used to analyze the distribution of bone mineralization under the articular cartilage, evaluate the position of prosthesis implantation, track the progression of osteoarthritis, and determine the joint injury prognosis. Furthermore, this technique was combined with indentation testing to discuss the relationship between the high bone density area of the articular surface, the mechanical strength of the bone, and the anchorage stability of the implant, in addition to the study of the relationship between mechanical strength and bone density. This narrative study discusses the pre- and postoperative evaluation of medical device implantation position, orthopedic surgery, and the clinical treatment of bone injury and degeneration. It also discusses the research status of CTOAM technology in image post-processing engineering and the relationship between bone material and mechanical strength.

A Training-Free Infant Spontaneous Movement Assessment Method for Cerebral Palsy Prediction Based on Videos.

OBJECTIVE: Early diagnosis of infant cerebral palsy (CP) is very important for infant health. In this paper, we present a novel training-free method to quantify infant spontaneous movements for predicting CP. METHODS: Unlike other classification methods, our method turns the assessment into a clustering task. First, the joints of the infant are extracted by the current pose estimation algorithm, and the skeleton sequence is segmented into multiple clips through a sliding window. Then we cluster the clips and quantify infant CP by the number of cluster classes. RESULTS: The proposed method was tested on two datasets, and achieved state-of-the-arts (SOTAs) on both datasets using the same parameters. What's more, our method is interpretable with visualized results. CONCLUSION: The proposed method can quantify abnormal brain development in infants effectively and be used in different datasets without training. SIGNIFICANCE: Limited by small samples, we propose a training-free method for quantifying infant spontaneous movements. Unlike other binary classification methods, our work not only enables continuous quantification of infant brain development, but also provides interpretable conclusions by visualizing the results. The proposed spontaneous movement assessment method significantly advances SOTAs in automatically measuring infant health.