Deep Learning Based Fall Recognition and Forecasting for Reconfigurable Stair-Accessing Service Robots

Jun Hua Ong, Abdullah Aamir Hayat (), Braulio Felix Gomez, Mohan Rajesh Elara and Kristin Lee Wood
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Jun Hua Ong: Robotics and Automation Research (ROAR) Lab, Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
Abdullah Aamir Hayat: Robotics and Automation Research (ROAR) Lab, Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
Braulio Felix Gomez: Robotics and Automation Research (ROAR) Lab, Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
Mohan Rajesh Elara: Robotics and Automation Research (ROAR) Lab, Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
Kristin Lee Wood: College of Engineering, Design and Computing, University of Colorado Denver, 1200 Larimer St, Ste. 3034, Denver, CO 80204, USA

Mathematics, 2024, vol. 12, issue 9, 1-29

Abstract: This paper presents a comprehensive study on fall recognition and forecasting for reconfigurable stair-accessing robots by leveraging deep learning techniques. The proposed framework integrates machine learning algorithms and recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM), for fall detection of service robots on staircases. The reconfigurable stair-accessing robot sTetro serves as the platform, and the fall data required for training models are generated in a simulation environment. The two machine learning algorithms are compared and their effectiveness on the fall recognition task is reported. The results indicate that the BiLSTM model effectively classifies falls with a median categorical accuracy of 94.10% in simulation and 90.02% with limited experiments. Additionally, the BiLSTM model can be used for forecasting, which is practically valuable for making decisions well before the onset of a free fall. This study contributes insights into the design and implementation of fall detection systems for service robots used to navigate staircases through deep learning approaches. Our experimental and simulation data, along with the simulation steps, are available for reference and analysis via the shared link.

Keywords: reconfigurable robots; deep learning; fall detection; design principles; recurrent neural network; LSTM; BiLSTM; service robots (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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