Intelligent scanning collision avoidance device with risk assessment

Malik Haddad; Jamal Shamieh; David Sanders; Amir Gharavi; Giles Tewkesbury; Mohamed Hassan-Sayed; Martin Langner

doi:10.1007/978-3-031-66336-9_8

Intelligent scanning collision avoidance device with risk assessment

Malik Haddad^*, Jamal Shamieh, David Sanders, Amir Gharavi, Giles Tewkesbury, Mohamed Hassan-Sayed, Martin Langner

^*Corresponding author for this work

Faculty of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents an Intelligent Scanning Collision Avoidance Device (Intelligent-SCAD) which is used to detect obstacles in a powered wheelchair surroundings to avoid related risk consequences. The Intelligent-SCAD provides a safe direction for the wheelchair. Inputs to the Intelligent-SCAD originate from a single rotating ultrasonic transducer fixed to the wheelchair. Readings from the ultrasonic transducer are used to train and test different Artificial Intelligence (AI) algorithms. The AI algorithms used were: Artificial Neural Network, Decision Tree, optimised Tree and optimised K-Nearest Neighbour. An algorithm is selected based on a compromise between accuracy and complexity. The optimised K-Nearest Neighbour algorithm provided the highest testing accuracy and relatively straightforward operation when compared with the other algorithms used. The new device applies optimised K-Nearest Neighbour to predict a safe direction for a wheelchair. The user can override the new system if necessary.

Original language	English
Title of host publication	Intelligent Systems and Applications - Proceedings of the 2024 Intelligent Systems Conference IntelliSys Volume 4
Editors	Kohei Arai
Publisher	Springer
Pages	112-123
Number of pages	12
ISBN (Electronic)	9783031663369
ISBN (Print)	9783031663352
DOIs	https://doi.org/10.1007/978-3-031-66336-9_8
Publication status	Published - 1 Aug 2024
Event	Intelligent Systems Conference, IntelliSys 2024 - Amsterdam, Netherlands Duration: 5 Sept 2024 → 6 Sept 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1068 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	Intelligent Systems Conference, IntelliSys 2024
Country/Territory	Netherlands
City	Amsterdam
Period	5/09/24 → 6/09/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial intelligence
Collision avoidance
Disabled
Risk
Steer
Wheelchair

Access to Document

10.1007/978-3-031-66336-9_8

Cite this

Haddad, M., Shamieh, J., Sanders, D., Gharavi, A., Tewkesbury, G., Hassan-Sayed, M., & Langner, M. (2024). Intelligent scanning collision avoidance device with risk assessment. In K. Arai (Ed.), Intelligent Systems and Applications - Proceedings of the 2024 Intelligent Systems Conference IntelliSys Volume 4 (pp. 112-123). (Lecture Notes in Networks and Systems; Vol. 1068 LNNS). Springer. https://doi.org/10.1007/978-3-031-66336-9_8

@inproceedings{dd3ada4735214961a759e14058c63e69,

title = "Intelligent scanning collision avoidance device with risk assessment",

abstract = "This paper presents an Intelligent Scanning Collision Avoidance Device (Intelligent-SCAD) which is used to detect obstacles in a powered wheelchair surroundings to avoid related risk consequences. The Intelligent-SCAD provides a safe direction for the wheelchair. Inputs to the Intelligent-SCAD originate from a single rotating ultrasonic transducer fixed to the wheelchair. Readings from the ultrasonic transducer are used to train and test different Artificial Intelligence (AI) algorithms. The AI algorithms used were: Artificial Neural Network, Decision Tree, optimised Tree and optimised K-Nearest Neighbour. An algorithm is selected based on a compromise between accuracy and complexity. The optimised K-Nearest Neighbour algorithm provided the highest testing accuracy and relatively straightforward operation when compared with the other algorithms used. The new device applies optimised K-Nearest Neighbour to predict a safe direction for a wheelchair. The user can override the new system if necessary.",

keywords = "Artificial intelligence, Collision avoidance, Disabled, Risk, Steer, Wheelchair",

author = "Malik Haddad and Jamal Shamieh and David Sanders and Amir Gharavi and Giles Tewkesbury and Mohamed Hassan-Sayed and Martin Langner",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; Intelligent Systems Conference, IntelliSys 2024 ; Conference date: 05-09-2024 Through 06-09-2024",

year = "2024",

month = aug,

day = "1",

doi = "10.1007/978-3-031-66336-9\_8",

language = "English",

isbn = "9783031663352",

series = "Lecture Notes in Networks and Systems",

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editor = "Kohei Arai",

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address = "Netherlands",

}

Haddad, M, Shamieh, J, Sanders, D, Gharavi, A, Tewkesbury, G, Hassan-Sayed, M & Langner, M 2024, Intelligent scanning collision avoidance device with risk assessment. in K Arai (ed.), Intelligent Systems and Applications - Proceedings of the 2024 Intelligent Systems Conference IntelliSys Volume 4. Lecture Notes in Networks and Systems, vol. 1068 LNNS, Springer, pp. 112-123, Intelligent Systems Conference, IntelliSys 2024, Amsterdam, Netherlands, 5/09/24. https://doi.org/10.1007/978-3-031-66336-9_8

Intelligent scanning collision avoidance device with risk assessment. / Haddad, Malik; Shamieh, Jamal; Sanders, David et al.
Intelligent Systems and Applications - Proceedings of the 2024 Intelligent Systems Conference IntelliSys Volume 4. ed. / Kohei Arai. Springer, 2024. p. 112-123 (Lecture Notes in Networks and Systems; Vol. 1068 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Intelligent scanning collision avoidance device with risk assessment

AU - Haddad, Malik

AU - Shamieh, Jamal

AU - Sanders, David

AU - Gharavi, Amir

AU - Tewkesbury, Giles

AU - Hassan-Sayed, Mohamed

AU - Langner, Martin

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024/8/1

Y1 - 2024/8/1

N2 - This paper presents an Intelligent Scanning Collision Avoidance Device (Intelligent-SCAD) which is used to detect obstacles in a powered wheelchair surroundings to avoid related risk consequences. The Intelligent-SCAD provides a safe direction for the wheelchair. Inputs to the Intelligent-SCAD originate from a single rotating ultrasonic transducer fixed to the wheelchair. Readings from the ultrasonic transducer are used to train and test different Artificial Intelligence (AI) algorithms. The AI algorithms used were: Artificial Neural Network, Decision Tree, optimised Tree and optimised K-Nearest Neighbour. An algorithm is selected based on a compromise between accuracy and complexity. The optimised K-Nearest Neighbour algorithm provided the highest testing accuracy and relatively straightforward operation when compared with the other algorithms used. The new device applies optimised K-Nearest Neighbour to predict a safe direction for a wheelchair. The user can override the new system if necessary.

AB - This paper presents an Intelligent Scanning Collision Avoidance Device (Intelligent-SCAD) which is used to detect obstacles in a powered wheelchair surroundings to avoid related risk consequences. The Intelligent-SCAD provides a safe direction for the wheelchair. Inputs to the Intelligent-SCAD originate from a single rotating ultrasonic transducer fixed to the wheelchair. Readings from the ultrasonic transducer are used to train and test different Artificial Intelligence (AI) algorithms. The AI algorithms used were: Artificial Neural Network, Decision Tree, optimised Tree and optimised K-Nearest Neighbour. An algorithm is selected based on a compromise between accuracy and complexity. The optimised K-Nearest Neighbour algorithm provided the highest testing accuracy and relatively straightforward operation when compared with the other algorithms used. The new device applies optimised K-Nearest Neighbour to predict a safe direction for a wheelchair. The user can override the new system if necessary.

KW - Artificial intelligence

KW - Collision avoidance

KW - Disabled

KW - Risk

KW - Steer

KW - Wheelchair

UR - https://www.scopus.com/pages/publications/85201121576

U2 - 10.1007/978-3-031-66336-9_8

DO - 10.1007/978-3-031-66336-9_8

M3 - Conference contribution

AN - SCOPUS:85201121576

SN - 9783031663352

T3 - Lecture Notes in Networks and Systems

SP - 112

EP - 123

BT - Intelligent Systems and Applications - Proceedings of the 2024 Intelligent Systems Conference IntelliSys Volume 4

A2 - Arai, Kohei

PB - Springer

T2 - Intelligent Systems Conference, IntelliSys 2024

Y2 - 5 September 2024 through 6 September 2024

ER -

Haddad M, Shamieh J, Sanders D, Gharavi A, Tewkesbury G, Hassan-Sayed M et al. Intelligent scanning collision avoidance device with risk assessment. In Arai K, editor, Intelligent Systems and Applications - Proceedings of the 2024 Intelligent Systems Conference IntelliSys Volume 4. Springer. 2024. p. 112-123. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-66336-9_8

Intelligent scanning collision avoidance device with risk assessment

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this