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In-Bed Human Pose Classification Using Sparse Inertial Signals

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Artificial Intelligence XXXVII (SGAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12498))

Abstract

Recent studies on sleep reveal its impact on the well-being of humans. Monitoring of in-bed body postures can provide clinicians with early indicators of a wide range of musculoskeletal disorders. Current work on sleep pose classification is directed at non-wearable technologies, with issues associated to limited body observability and concerns over personal privacy; or on wearable sensors that consider only a small number of sleep poses and thus have limited generalisation. This paper proposes a novel method for wearable-based human pose classification capable of classifying twelve benchmark sleeping poses. To overcome the scarcity of labelled inertial data, a new data augmentation technique is proposed to generate realistic synthetic datasets emulating real-world conditions. An Error-Correcting Output Codes model is used to employ a multi-class classifier based on an ensemble of Support Vector Machine based classifiers. For system validation, a computer graphics simulator was used to accurately emulate data recording of in-bed body postures, leveraging on a standard articulated body file format commonly used by commercial motion-capture technologies. Experiments show superior performance (as high as 100% classification accuracy), and resilience to noise contamination beyond what could be encountered in reality.

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Notes

  1. 1.

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Acknowledgements

The authors would like to thank Dr Lyndon Mason and Prof Rahul Savani for their input in the definition of the clinical problem.

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Authors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Omar Elnaggar, Frans Coenen & Paolo Paoletti

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  1. Omar Elnaggar
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  2. Frans Coenen
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  3. Paolo Paoletti
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Correspondence to Omar Elnaggar .

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Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. RKE Consulting, Micheldever, UK

    Richard Ellis

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Elnaggar, O., Coenen, F., Paoletti, P. (2020). In-Bed Human Pose Classification Using Sparse Inertial Signals. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVII. SGAI 2020. Lecture Notes in Computer Science(), vol 12498. Springer, Cham. https://doi.org/10.1007/978-3-030-63799-6_25

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  • DOI: https://doi.org/10.1007/978-3-030-63799-6_25

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-63799-6

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