This paper proposes a method for quantitatively evaluating sprinting motions using the videos of runners. Specifically, this paper explores the coordination between physical motions, which has been recognized as very important in sprinting. After detecting and normalizing the joint coordinates from sprinting videos, the cross-correlations of two windowed time-series data are calculated using the windowing cross-correlation function, and the coordination between the motions of the two joints is quantified. Experiments that use 20 subjects are conducted. As a result of classifying the cross-correlation obtained from the subjects' data into two clusters using k-means clustering, conditions in which the obtained cluster includes a high percentage of inexperienced sprinters are found. To verify whether the motions corresponding to these conditions are valid as the evaluation criterion of sprinting, Spearman's rank correlation coefficients between cross-correlations and 30-m time records are calculated. The results show a weak correlation with respect to the coordination between the elbow and knee motions. Therefore, it can be said that the cross-correlation corresponding to the coordination can be used as a quantitative criterion in sprinting.

As a novel therapy for peritoneal dissemination, it is desired to actualize an endoscopic photothermal therapy, which is minimally invasive and is highly therapeutically effective. However, since the endoscopic tumor temperature control has not been actualized, conventional therapies could damage healthy tissues by overhearing. In this paper, we develop a thermal endoscope system that controls the tumor temperature so that the heated tumor gets necrotic. In fact, our thermal endoscope contains a thermal image sensor, a visible light endoscope and a laser fiber. Concerning the thermal image sensor, the conventional thermal endoscope has the problem that the diameter is too large, because the conventional endoscope loads a large thermal image sensor with high-resolution. Therefore, this paper uses a small thermal image sensor with low resolution, because the diameter of the thermal endoscope needs to be smaller than 15mm in order to be inserted into the trocar. However, this thermal image sensor is contaminated by much noise. Thus, we develop a tumor temperature control system using a feedback control and tumor temperature estimation based on Gaussian function, so that the noisy, small thermal image sensor can be used. As experimental results of the proposed endoscopic photothermal therapy for the hepatophyma carcinoma model of rats, it turns out that the tumor temperature by which the heated tumor gets necrotic can be kept stable. It can be said that our endoscopic photothermal therapy achieves a certain degree of therapy effect.

This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using ECG (Electrocardiogram) images by the deep learning whose training ECG images are annotated by the diagnoses given by the medical doctor who observes the echocardiograms. Besides, it explores the relationship between the trained deep learning network and its determinations, using the Grad-CAM.In this study, one-beat ECG images for 12-leads and 4-leads are generated from ECG's and train CNN's (Convolutional neural network). By applying the Grad-CAM to the trained CNN's, feature areas are detected in the early time range of the one-beat ECG image. Also, by limiting the time range of the ECG image to that of the feature area, the CNN for the 4-lead achieves the best classification performance, which is close to expert medical doctors' diagnoses.Clinical Relevance - This paper achieves as high AS classification performance as medical doctors' diagnoses based on echocardiograms by proposing an automatic method for detecting AS only using ECG.

This paper proposes a method that can classify foot strike types using a deep learning model and can extract unknown factors, which enables to evaluate running motions without being influenced by biases of sports experts, using the contribution degree of input values (CDIV). Accelerometers are attached to the runner's body, and when the runner runs, a fixed camera observes the runner and acquires a video sequence synchronously with the accelerometers. To train a deep learning model for classifying foot strikes, we annotate foot strike acceleration data for RFS (Rearfoot strike) or non-RFS objectively by watching the video. To interpret the unknown factors extracted from the learned model, we calculate two CDIVs: the contributions of the resampling time and the accelerometer value to the output (foot strike type). Experiments on classifying unknown runners' foot strikes were conducted. As a common result to sport science, it is confirmed that the CDIVs contribute highly at the time of the right foot strike, and the sensor values corresponding to the right and left tibias contribute highly to classifying the foot strikes. Experimental results show the right tibia is important for classifying foot strikes. This is because many of the training data represent difference between the two foot strikes in the right tibia. As a conclusion, our proposed method could extract unknown factors from the classifier and could interpret the factors that contain similar knowledge to the prior knowledge of experts, as well as new findings that are not included in conventional knowledge.

To improve running performances, each runner's skill, such as characteristics and habits, needs to be known, and feedback on the performance should be outputted according to the runner's skill level. In this paper, we propose a new coaching system for detecting the skill of a runner and a method of giving feedback using a sprint motion dataset. Our proposed method calculates an extracted feature to detect the skill using an autoencoder whose middle layer is an LSTM layer; we analyse the feature using hierarchical clustering, and we analyse the human joints that affect the skill. As a result of experiments, five clusters are obtained using hierarchical clustering. This paper clarifies how to detect the skill and to output feedback to achieve a level of performance one step higher than the current level.