Building A Prototype of a Pair of Weighing Shoes Based on Machine Learning Techniques Changes in the human body weight may be caused by diseases, overeating, lack of exercise, or other health problems. In this paper, we built a prototype of a pair of weighing shoes to enable people to constantly monitor their weight changes so that they can detect and treat their health problems earlier. We placed several thin and flexible pressure sensors under the shoes and used machine learning techniques to learn the relationship between their values and the actual weight of the human body being measured. The trained model then was used to predict the weight of a human body. In our experiments, we varied the number of used pressure sensors to study its impact on the prediction error. We found that the prediction error of the trained model could be controlled within 5.8% when 8 or more pressure sensors were used for the current prototype.

Development of Load Weight and Height Classifier in LiftingUp Task Using Body Motion Metrics This paper presents a new weight and height class classifier for the liftup task, focusing on future use in the control of wearable power assist devices. The proposed classifier retrieves body motion feature metrics using small accelerometers and determines the weight class (0 ky40% of the subject body weight but not exceeding 25 kg) and the height of the handle (0cm30cm60 cm) of the load. Three different classifiers have been trainedconfigured and tested. The trained classifiers are shown to correctly classify the evaluation dataset with 8090% accuracy. An additional experiment was conducted to collect feature quantities in which subjects were requested to lift up a weight placed inside a jute bag on the floor. A discrimination accuracy of approximately 80% for the jute bag experiment verifies the versatility of the classifier for more general onthejob lifting tasks.