Radar-based Recognition of Activities of Daily Living in the Palliative Care Context Using Deep Learning

Bräunig J, Mejdani D, Krauß D, Grießhammer S, Richer R, Schüßler C, Yip J, Steigleder T, Ostgathe C, Eskofier B, Vossiek M (2023)

Publication Type: Conference contribution, Original article

Publication year: 2023

Publisher: IEEE

Conference Proceedings Title: 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

Event location: Pittsburgh US

URI: https://www.mad.tf.fau.de/files/2023/12/braeunig23_radar_palliative_care.pdf

DOI: 10.1109/BHI58575.2023.10313506

Abstract

The accurate detection and quantification of activities of daily life (ADL) are crucial for assessing the health status of palliative patients to allow an optimized treatment in the last phase of life. Current evaluation methods heavily rely on subjective self-reports or external observations by clinical staff, lacking objectivity. To address this limitation, we propose a radar-based approach for recognizing ADLs in a palliative care context. In our proof of concept study, we recorded five different ADLs relevant to palliative care, all occurring within a hospital bed, from N=14 healthy participants (57% women, aged 28.6 ± 5.3years). All movements were recorded using two frequency-modulated continuous wave radar systems measuring velocity, range, and angle. A convolutional neural network combined with long short-term memory achieved a classification accuracy of 99.8 ± 0.4% across five cross-validation folds. Furthermore, we compare our initial approach, which takes into account all dimensions of the available radar data, to a simplified version, where only velocity information over time is fed into the network. While these results demonstrate the high potential of radar-based sensing to automatically detect and quantify activities in a palliative care context, future work is still necessary to assess the applicability to real-world hospital scenarios.

Authors with CRIS profile

Johanna Bräunig Lehrstuhl für Hochfrequenztechnik Desar Mejdani Lehrstuhl für Hochfrequenztechnik Daniel Krauß Machine Learning and Data Analytics Lab Stefan Grießhammer Department of Anaesthesiology Robert Richer Machine Learning and Data Analytics Lab Christian Schüßler Lehrstuhl für Hochfrequenztechnik Julia Yip Department of Palliative Medicine Tobias Steigleder Department of Palliative Medicine Christoph Ostgathe Professur für Palliativmedizin Björn Eskofier Machine Learning and Data Analytics Lab Martin Vossiek Lehrstuhl für Hochfrequenztechnik

Additional Organisation(s)

Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung FAU Profilzentrum Licht.Materie.Quantentechnologien (FAU LMQ)

Related research project(s)

Empathokinästhetische Messung und Bewegungsmustererkennung als Biomarker für Gesundheitszustand (SFB 1483 EmpkinS D05) Empatho-Kinaesthetic Sensor Technology (SFB 1483 EmpkinS) July 1, 2021 - June 30, 2025 Berührungslose Messung von Stress, seiner Determinanten und Konsequenzen (SFB 1483 EmpkinS D03) Empatho-Kinaesthetic Sensor Technology (SFB 1483 EmpkinS) July 1, 2021 - June 30, 2025 Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (SFB 1483 EmpkinS A01) Empatho-Kinaesthetic Sensor Technology (SFB 1483 EmpkinS) July 1, 2021 - June 30, 2025

How to cite

APA:

Bräunig, J., Mejdani, D., Krauß, D., Grießhammer, S., Richer, R., Schüßler, C.,... Vossiek, M. (2023). Radar-based Recognition of Activities of Daily Living in the Palliative Care Context Using Deep Learning. In 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Pittsburgh, US: IEEE.

MLA:

Bräunig, Johanna, et al. "Radar-based Recognition of Activities of Daily Living in the Palliative Care Context Using Deep Learning." Proceedings of the 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Pittsburgh IEEE, 2023.

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