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.

Relationship between characteristics of human lifting motion and predicted mass When a human performs a lifting operation using the power assist device at the production site, if the operatoru0027s weight perception is different from what heshe assumed, the operation may be different from the assumed one, which may cause discomfort. The discomfort in the situation of using the assist device can be prevented by bringing the predicted mass and the perceived mass closer. In order to do that, it is necessary for the assist device to estimate the predicted mass before human perceives weight. Therefore, in this report, we examine the quantitative relationship between the characteristics of human lifting motion before the object is lifted and the predicted mass. For that purpose, we prepared some mass and lifting time and carried out lifting experiments based on them.