Gait Recovery System for Parkinson’s Disease using Machine Learning on Embedded Platforms
Abstract
Freezing of Gait (FoG) is a common gait deficit among patients diagnosed with Parkinson's Disease (PD). In order to help these patients recover from FoG episodes, Rhythmic Auditory Stimulation (RAS) is needed. The authors propose a ubiquitous embedded system that detects FOG events with a Machine Learning (ML) subsystem from accelerometer signals. By making inferences on-device, we avoid issues prevalent in cloud-based systems such as latency and network connection dependency. The resource-efficient classifier used, reduces the model size requirements by approximately 400 times compared to the best performing standard ML systems, with a trade-off of a mere 1.3% in best classification accuracy. The aforementioned trade-off facilitates deployability in a wide range of embedded devices including microcontroller based systems. The research also explores the optimization procedure to deploy the model on an ATMega2560 microcontroller with a minimum system latency of 44.5 ms. The smallest model size of the proposed resource efficient ML model was 1.4 KB with an average recall score of 93.58%.
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Authors in [99] achieved a moderate satisfactory rating for their model despite constraints of limited storage on the edge device.
“Authors in [99] acknowledged the constraints imposed by the edge device, since they required to train large neural networks with large data-sets due to the limited storage feature; nonetheless, the authors were able to achieve moderate satisfactory rating for their model.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [99]
Authors in [99] built on RNN models but used TinyML for hearing aids.
“Many models were developed through usage of RNN [98], the authors in [99] built on these models, but using TinyML.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [99]
Authors in [99] used RNNs and pruning techniques to create a model to enhance the hearing of speech.
“The base of the hardware was chosen to be the Hearing Aid, and the authors used RNNs and pruning techniques to create the model that would enhance the hearing of speech.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [99]
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- Authors
- Gokul, H., Suresh, P., Hari Vignesh, B., Pravin Kumaar, R., & Vijayaraghavan, V.
- Affiliations
- SRM Institute of Science and Technology (IN)
- Venue
- 2020 IEEE International Systems Conference (SysCon)
- Publisher
- IEEE
- Published
- 2020-08-24
- Pages
- 1-8
- ISSN
- 1944-7620 (ISSN-L)
- OpenAlex
- W3110889465
- Cited by
- 23
- References
- 30
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 6 · 2023: 5 · 2022: 7 · 2021: 2
- FWCI
- 17.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Systems Conference (SysCon) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Parkinson's disease, freezing of gait, gait recovery, rhythmic auditory stimulation, accelerometer signals, on-device inference, embedded systems, microcontroller deployment, model compression
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