Deep Learning Based Fall Recognition and Forecasting for Reconfigurable Stair-Accessing Service Robots Article Swipe
Jun Hua Ong
,
Abdullah Aamir Hayat
,
Braulio Félix Gómez
,
Mohan Rajesh Elara
,
Kristin L. Wood
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/math12091312
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/math12091312
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math12091312
- https://www.mdpi.com/2227-7390/12/9/1312/pdf?version=1714055629
- OA Status
- gold
- Cited By
- 4
- References
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.3390/math12091312Digital Object Identifier
- Title
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Deep Learning Based Fall Recognition and Forecasting for Reconfigurable Stair-Accessing Service RobotsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-25Full publication date if available
- Authors
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Jun Hua Ong, Abdullah Aamir Hayat, Braulio Félix Gómez, Mohan Rajesh Elara, Kristin L. WoodList of authors in order
- Landing page
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https://doi.org/10.3390/math12091312Publisher landing page
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https://www.mdpi.com/2227-7390/12/9/1312/pdf?version=1714055629Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/12/9/1312/pdf?version=1714055629Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Robot, Machine learning, Deep learning, Categorical variable, Task (project management), Artificial neural network, Service (business), Engineering, Systems engineering, Economy, EconomicsTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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