Lynggaard Hale (olivecup3)

BACKGROUND Traditional Chinese medicine (TCM) massage has a better effect on treating infant diarrhea compared to medical treatment. The TCM doctors need to be trained to master professional massage techniques. Traditional Chinese massage training relies on the students' understanding ability, and cannot accurately record the students' operating information. This situation leads to insufficient clinical massage skills of the students. OBJECTIVE This paper proposes a novel massage training platform to quantitatively perceive the massage techniques of students. METHODS The paper proposed two types of flexible array sensors, which are arranged and placed into the bionic baby according to the position of the human acupoints. The massage techniques of the training object can be analyzed and evaluated during the massage process by studying the voltage from pressure sensors when the participants massage the bionic infant. RESULTS A medical student was invited to conduct the massage training experiment, and the massage information included the operating strength, massage frequency and the massage direction, which were recorded and analyzed through the training platform. CONCLUSION The platform can perceive the parameters related to the massage technique of students and can be used for medical training.BACKGROUND Minimally invasive surgery (MIS), unlike open surgery in which surgeons can perform surgery directly, is performed using miniaturized instruments with indirect but careful observation of the surgical site. OBJECTIVE Instrument detection is a crucial requirement in conventional and robot-assisted MIS, which can also be very useful during surgical training. In this paper, we propose a novel framework of using two three-layer convolutional neural networks (CNNs) in a series to detect surgical instrument in in-vivo video frames. METHODS The two convolutional neural networks proposed in this paper have different tasks. (i) The former CNN is trained to detect the edges points of the instrument shaft directly from images patches. (ii) The latter is trained to locate the instrument tip also from images patches after the former detection finishes. RESULTS We validated our method on the publicly available EndoVisSub dataset and a standard dataset, and it detected tools with an accuracy of 91.2% and 75% respectively. CONCLUSION Our two-step detection method achieves better performance than other existing approaches in terms of detection accuracy.BACKGROUND Studies on robot-assisted gait training rehabilitation in multiple sclerosis have reported positive effects on mobility and quality of life. However, their effects on cognitive functions are difficult to determine because not all trials have included cognition assessments. Virtual reality-based training provides enhanced opportunity for stimulating cognitive abilities by repetitive practice, feedback information, and motivation for endurance practice. OBJECTIVE To compare the effects of innovative robot-assisted gait training combined with virtual reality versus standard robot-assisted gait training on information processing speed, sustained attention, working memory, and walking endurance in patients with multiple sclerosis. METHODS Seventeen outpatients were randomly assigned to receive robot-assisted gait training either with or without virtual reality. The robot assisted gait training + virtual reality group underwent end-effector system training engendered by virtual reality. The standard traitions. However larger positive effects on gait ability were noted after robot-assisted gait training engendered by virtual reality with multiple sclerosis. Robot-assisted gait training provides a therapeutic alternative and motivational of traditional motor rehabilitation.BACKGROUND Emotionalism, i.e. uncontrolled episodes of crying (or less commonly laughing) post stroke that are not triggered by situations that would have previously provoked such behavior occur in stroke survivors, may pe