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Tiny Machine Learning for Real-Time Postural Stability Analysis
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).
University of Catania, Catania, Italy.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).ORCID iD: 0000-0001-9572-3639
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2023 (English)In: 2023 IEEE Sensors Applications Symposium (SAS), IEEE conference proceedings, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Postural sway is a critical measure for evaluating postural control, and its analysis plays a vital role in preventing falls among the elderly. Typically, physiotherapists assess an individual's postural control using tests such as the Berg Balance Scale, Tinetti Test, and time up-and-go test. Sensor-based analysis is available based on devices such as force plates or inertial measurement units. Recently, machine learning methods have demonstrated promising results in the sensor-based analysis of postural control. However, these models are often complex, slow, and energy-intensive. To address these limitations, this study explores the design space of lightweight machine learning models deployable to microcontrollers to assess postural stability. We developed an artificial neural network (ANN) model and compare its performance to that of random forests, gaussian naive bayes, and extra tree classifiers. The models are trained using a sway dataset with varying input sizes and signal-to-noise ratios. The dataset comprises two feature vectors extracted from raw accelerometer data. The developed models are deployed to an ARM Cortex M4-based microcontroller, and their performance is evaluated and compared. We show that the ANN model has 99.03% accuracy, higher noise immunity, and the model performs better with a window size of one second with 590.96 us inference time. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2023.
Keywords [en]
embedded systems, fall prevention, machine learning, postural sway, real-time postural assessment, TinyML
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:miun:diva-49644DOI: 10.1109/SAS58821.2023.10254126ISI: 001086399500071Scopus ID: 2-s2.0-85174026290ISBN: 9798350323078 (print)OAI: oai:DiVA.org:miun-49644DiVA, id: diva2:1807283
Conference
2023 IEEE Sensors Applications Symposium, SAS 2023
Available from: 2023-10-25 Created: 2023-10-25 Last updated: 2023-11-10Bibliographically approved

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Adin, VeysiZhang, YuxuanOelmann, BengtBader, Sebastian

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