Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment
Abstract
The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.
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Several works have introduced systems and methods for monitoring and assessing Parkinson disease symptoms.
“Specifically, several works have introduced systems and methods for monitoring and assessing Parkinson disease (PD) symptoms [31,32,33,34,35,36].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [32]
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- Authors
- Eskofier, B. M., Lee, S. I., Daneault, J.-F., Golabchi, F. N., Ferreira-Carvalho, G., Vergara-Diaz, G., Sapienza, S., Costante, G., Klucken, J., Kautz, T., & Bonato, P.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Harvard University (US)
- Universitätsklinikum Erlangen (DE)
- ORCID
- Venue
- 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2016-08
- Volume
- 2016
- Pages
- 655-658
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2531966375
- Cited by
- 180
- References
- 26
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 14 · 2023: 17 · 2022: 28 · 2021: 19 · 2020: 31 · 2019: 22 · 2018: 13 · 2017: 10 · 2016: 1
- FWCI
- 5.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Repository of Futwangen University of Applied Sciences (Furtwangen University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- Parkinson's disease, wearable sensors, inertial measurement unit, movement disorders, motor symptom monitoring, convolutional neural networks, motor task classification
- MeSH terms
- Machine Learning, Aged, Extremities, Female, Humans, Male, Middle Aged, Parkinson Disease, Severity of Illness Index, Software, Hypokinesia
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