A Data Mining Approach to Predict Falls in Humanoid Robot Locomotion

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Andre, J (Author) Other; Faria, BM (Author) Other; Santos, C (Author) Other; reis, lp (Author) REIT

Conference proceedings International

Title: ROBOT 2015: SECOND IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 2

Pages: 273-285

2nd Iberian Robotics Conference (ROBOT)

Lisbon, PORTUGAL, NOV 19-21, 2015

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Other information

Authenticus ID: P-00K-2AX

DOI: 10.1007/978-3-319-27149-1_22

Abstract (EN): The inclusion of perceptual information in the operation of a dynamic robot (interacting with its environment) can provide valuable insight about its environment and increase robustness of its behaviour. In this regard, the concept of Associative Skill Memories (ASMs) has provided a great contributions regarding an effective and practical use of sensor data, under a simple and intuitive framework [2, 13]. Inspired by [2], this paper presents a data mining solution to the fall prediction problem in humanoid biped robotic locomotion. Sensor data from a large number of simulations was recorded and four data mining algorithms were applied with the aim of creating a classifier that properly identifies failure conditions. Using Support Vector Machines, on top of sensor data from a large number of simulation trials, it was possible to build an accurate and reliable offline fall predictor, achieving accuracy, sensitivity and specificity values up to 95.6%, 96.3% and 94.5%, respectively.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13