An Optical Flow- and Machine Learning-Based Fall Recognition Model for Stair Accessing Service Robots

Jun Hua Ong (), Abdullah Aamir Hayat, Mohan Rajesh Elara and Kristin Lee Wood
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Jun Hua Ong: ROAR Lab, Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
Abdullah Aamir Hayat: Department of Mechanical and Aerospace Engineering, College of Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
Mohan Rajesh Elara: 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, 2025, vol. 13, issue 12, 1-28

Abstract: One of the reasons for the lack of commercial staircase service robots is the risk and severe impact of them falling down the stairs. Thus, the development of robust fall damage mitigation mechanisms is important for the commercial adoption of staircase robots, which in turn requires a robust fall detection model. A machine-learning-based approach was chosen due to its compatibility with the given scenario and potential for further development, with optical flow chosen as the means of sensing. Due to the costs, complexity, and potential system damage of compiling training datasets physically, simulation was used to generate said dataset, and the approach was verified by evaluating the models produced using data from experiments with a physical setup. This approach, producing fall detection models trained purely with physics-based simulation-generated data, is able to create models that can classify real-life fall data with an average of 79.89% categorical accuracy and detect the occurrence of falls with 99.99% accuracy without any further modifications, making it easy and thus attractive for commercial adoption. A study was also performed to study the effects of moving objects on optical flow fall detection, and it showed that moving objects have minimal to no impact on sparse optical flow in an environment with otherwise sufficient features. An active fall damage mitigation measure is proposed based on the models developed with this method.

Keywords: deep learning; fall detection; LSTM; optical flow; service robots (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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