Abstract

Parkinson’s disease (PD) is a chronic neurodegenerative condition that affects a patient’s everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD. Specifically, our proposed architecture, named MCPD-Net, uses two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder (VAE) models. These are modality-specific VAEs that predict effective representations of human movements to be fused and given to a classification module. During our end-to-end training, we minimise the difference between the latent spaces corresponding to the two data modalities. This makes our method capable of dealing with missing modalities during inference. We show that our proposed multimodal method outperforms unimodal and other multimodal approaches by an average increase in F1-score of 0.25 and 0.09, respectively, on a data set with real patients. We also show that our method still outperforms other approaches by an average increase in F1-score of 0.17 when a modality is missing during inference, demonstrating the benefit of training on multiple modalities.
Original languageEnglish
Article number4133
JournalSensors
Volume21
Issue number12
DOIs
Publication statusPublished - 16 Jun 2021

Bibliographical note

Funding Information:
Funding: This research was funded by the UK Engineering and Physical Sciences Research Council (EPSRC), grant number EP/R005273/1. This work is also supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund, grant code: 204813/Z/16/Z.

Funding Information:
Acknowledgments: This work was performed under the SPHERE Next Steps Project funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/R005273/1. This work made use of wearable biosensors (AX3, Axivity) from IXICO to collect accelerometry data.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Structured keywords

  • SPHERE

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