The technical deficiencies in traditional medical imagining methods limit the study of in vivo ankle biomechanics. A dual fluoroscopic imaging system (DFIS) provides accurate and non-invasive measurements of dynamic and static activities in joints of the body. This approach can be used to quantify the movement in the single bones of the ankle and analyse different morphological and complex bone positions and movement patterns within these organs and has been widely used in the field of image diagnosis and evaluation of clinical biomechanics. This paper reviews the applications of DFIS that were used to measure the in vivo kinematics of the ankle in the field of clinical and sports medicine. The advantages and shortcomings of DFIS in the practical application are summarised. We further put forward effective research programs for understanding the movement as well as injury mechanism of the ankle in vivo, and provide constructive research direction for future study.
Objective To investigate the effects of children’s crawling-promotion-training-robot on gross motor function and cognitive function in children with global developmental delay (GDD). Methods A total of 40 children with GDD admitted to the Department of Rehabilitation Medicine, Children’s Hospital of Nanjing Medical University were selected as the research subjects. By envelope method, the children were randomly and equally divided into experimental group and control group, with 20 cases in each group. The experimental group received children’s crawling-promotion-training-robot combined with conventional rehabilitation therapy, while the control group received manual crawling training combined with conventional rehabilitation therapy. Before and after treatment, the scores of Gross Motor Function Measure Scale-88 (GMFM-88) and Gesell Developmental Scale (GDS) were respectively used to evaluate gross motor function and cognitive function. Results There was no significant difference in gender (χ2=0.100, P=0.752) and age (t=0.053, P=0.962) between the two groups. Before treatment, there was no significant difference in GMFM-88 and GDS scores between the two groups (P>0.05). After treatment, there were statistically significant differences in GMFM-88 and GDS scores between the two groups (P<0.05). The comparison within the group showed that there were statistically significant differences in GMFM-88 and GDS scores between the two groups before and after treatment. Conclusion Children’s crawling-promotion-training-robot is more effective than manual crawling training in improving gross motor function and cognitive function in children with GDD.
How to realize the control of limb movement and apply it to intelligent robot systems at the level of cerebellar cortical neurons is a hot topic in the fields of artificial intelligence and rehabilitation medicine. At present, the cerebellar model usually used is only for the purpose of controlling the effect, borrowing from the functional mode of the cerebellum, but it ignores the structural characteristics of the cerebellum. In fact, in addition to being used for controlling purposes, the cerebellar model should also have the interpretability of the control process and be able to analyze the consequences of cerebellar lesions. Therefore, it is necessary to establish a bionic cerebellar model which could better express the characteristics of the cerebellum. In this paper, the process that the cerebellum processes external input information and then generates control instructions at the neuron level was explored. By functionally segmenting the cerebellum into homogeneous structures, a novel bionic cerebellar motion control model incorporating all major cell types and connections was established. Simulation experiments and force feedback device control experiments show that the bionic cerebellar motion control model can achieve better control effect than the currently widely used cerebellar model articulation controller, which verifies the effectiveness of the bionic cerebellar motion control model. It has laid the foundation for real brain-like artificial intelligence control.
Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: −0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.
ObjectiveTo summarize the design and the biomechanical characteristics of Sivash-range of motion femoral modular stem (S-ROM) prosthesis and mainly to introduce its clinical use in developmental dysplasia of hip (DDH) and hip revision. MethodsLiterature concerning S-ROM prosthesis was extensively reviewed and analyzed. ResultsThe S-ROM prosthesis based on the modularity feature can reach press-fit in metaphysis and diaphysis of femur concurrently. Additionaly, S-ROM prosthesis can fit for anatomic differences of the DDH femur and is capable of use in correction osteotomy and hip revision. ConclusionModular junctions of S-ROM prosthesis increase the potentials of implant fracture and metallic debris production, so further follow-up study is needed to verify the long-term effectiveness.
Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise K fold cross validation. Moreover, we compare the accuracy of emotion recognition with different time window length. The experimental results indicate that 4 s time window is appropriate for sampling. Filter-bank long short-term memory networks (FBLSTM) using differential entropy features as input was proposed. The average accuracy of low and high in valance dimension, arousal dimension and combination of the four in valance-arousal plane is 78.8%, 78.4% and 70.3%, respectively. These results demonstrate the advantage of our emotion recognition model over the current studies in terms of classification accuracy. Our model might provide a novel method for emotion recognition in affective brain-computer interactions.
Objective To investigate and analyze the difficulties of nosocomial infection management in different-level medical institutions in Shanghai, and to provide scientific basis for improving the level of nosocomial infection management. Methods A questionnaire was designed to include 10 difficulties in nosocomial infection management such as professional title promotion, salary, and personnel allocation. In October 2023, the Shanghai Nosocomial Infection Quality Control Center, in collaboration with the Shanghai Hospital Association, conducted a questionnaire survey among the heads of nosocomial infection management departments in medical institutions in Shanghai. The scores of difficulties were analyzed by stratification according to hospital level, allocation and changes of full-time personnel. Results A total of 548 questionnaires were distributed, and 530 valid questionnaires were retrieved, with a recovery rate of 96.72%. There were 55 public tertiary, 93 public secondary, 169 public primary and 213 social medical institutions. The rates of full-time personnel allocation meeting standards were 76.36% (42/55), 72.04% (67/93), 31.95% (54/169), and 21.60% (46/213), respectively. There was a statistically significant difference in the rates of full-time personnel allocation meeting standards among different levels of medical institutions (χ2=105.149, P<0.001). There was no statistical difference in the total scores of nosocomial infection management difficulties among different-level medical institutions (F=1.657, P=0.176). There were statistically significant differences in the scores of difficulties in professional title promotion, cumbersome daily norms and requirements, insufficient allocation of full-time personnel, and high personnel turnover (P<0.05). Conclusions The main difficulties in nosocomial management of medical institutions at all levels in Shanghai include the difficulty in career promotion, cumbersome daily norms and requirements, insufficient allocation of full-time personnel and lack of experience. In the future, medical institutions should strengthen the allocation of full-time personnel and enhance their capabilities, provide smooth promotion channels, to promote the high-quality development of nosocomial infection management ultimately.
In order to study the effect of light with different wavelengths on the motion behavior of carp robots, phototaxis experiment, anatomical experiment, light control experiment and speed measurement experiment were carried out in this study. Blue, green, yellow and red light with different wavelength were used to conduct phototaxis experiments on carp to observe their movement behavior. By dissecting the skull bones of the carp to determine the appropriate location to carry the light control device, we independently developed a light control carrying device which was suitable for any illumination intensity environment. The experiment of the light-controlled carp robots was carried out. The motion behavior of the carp robot was checked by using computer binocular stereo vision technology. The motion trajectory of the carp robot was tracked and obtained by applying kernel correlation filter (KCF) algorithm. The motion velocity of the carp robot at different wavelengths was calculated according to their motion trajectory. The results showed that carps’ sensitivity to different light changed from strong to weak in the order of blue, red, yellow and green, so that using light with different wavelengths to control the speed of the carp robot has certain laws to follow. A new method to avoid brain damage in carp robots control can be provided in this study.
Emotion recognition refers to the process of determining and identifying an individual's current emotional state by analyzing various signals such as voice, facial expressions, and physiological indicators etc. Using electroencephalogram (EEG) signals and virtual reality (VR) technology for emotion recognition research helps to better understand human emotional changes, enabling applications in areas such as psychological therapy, education, and training to enhance people’s quality of life. However, there is a lack of comprehensive review literature summarizing the combined researches of EEG signals and VR environments for emotion recognition. Therefore, this paper summarizes and synthesizes relevant research from the past five years. Firstly, it introduces the relevant theories of VR and EEG signal emotion recognition. Secondly, it focuses on the analysis of emotion induction, feature extraction, and classification methods in emotion recognition using EEG signals within VR environments. The article concludes by summarizing the research’s application directions and providing an outlook on future development trends, aiming to serve as a reference for researchers in related fields.