Identification and Classification of Human Body Exercises on Smart Textile Bands by Combining Decision Tree and Convolutional Neural Networks.

In recent years, human activity recognition (HAR) has gained significant interest from researchers in the sports and fitness industries. In this study, the authors have proposed a cascaded method including two classifying stages to classify fitness exercises, utilizing a decision tree as the first stage and a one-dimension convolutional neural network as the second stage. The data acquisition was carried out by five participants performing exercises while wearing an inertial measurement unit sensor attached to a wristband on their wrists. However, only data acquired along the z-axis of the IMU accelerator was used as input to train and test the proposed model, to simplify the model and optimize the training time while still achieving good performance. To examine the efficiency of the proposed method, the authors compared the performance of the cascaded model and the conventional 1D-CNN model. The obtained results showed an overall improvement in the accuracy of exercise classification by the proposed model, which was approximately 92%, compared to 82.4% for the 1D-CNN model. In addition, the authors suggested and evaluated two methods to optimize the clustering outcome of the first stage in the cascaded model. This research demonstrates that the proposed model, with advantages in terms of training time and computational cost, is able to classify fitness workouts with high performance. Therefore, with further development, it can be applied in various real-time HAR applications.

Risk assessment of gastric cancer associated with asbestosis: a case report.

BACKGROUND: The International Agency for Research on Cancer classifies asbestos as belonging to Carcinogen Group 2A for gastric cancer. We herein report a case of gastric cancer associated with asbestosis and describe the work-related and risk assessments of asbestos exposure for gastric cancer. CASE PRESENTATION: The 66-year-old male patient in our case worked in asbestos spinning factories. His level of cumulated asbestos fiber exposure was estimated to be 38.0-71.0 f-yr/cc. Thus, the Excess Life Cancer Risk for lung cancer associated with asbestos exposure was 9,648×10(-5), almost 9,600 times the value recommended by the United States of America Environmental Protection Agency (1 × 10(-5)). The relative risk of developing lung cancer for this patient was more than 25 f-yr/cc, a well-known criterion for doubling the risk of lung cancer. CONCLUSION: The patient's exposure to high-dose asbestos was sufficient to increase his risk of gastric cancer because as the risk of lung cancer increased, the risk of gastric cancer was due to increase as well. Therefore, occupational asbestos fiber exposure might be associated with gastric cancer in this case.