Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review
The use of machine learning in medical and assistive applications is receiving significant attention thanks to the unique potential it offers to solve complex healthcare problems for which no other solutions had been found. Particularly promising in this field is the combination of machine learning...
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| Published in | IEEE access Vol. 10; p. 1 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Online Access | Get full text |
| ISSN | 2169-3536 2169-3536 |
| DOI | 10.1109/ACCESS.2022.3206782 |
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| Abstract | The use of machine learning in medical and assistive applications is receiving significant attention thanks to the unique potential it offers to solve complex healthcare problems for which no other solutions had been found. Particularly promising in this field is the combination of machine learning with novel wearable devices. Machine learning models, however, suffer from being computationally demanding, which typically has resulted on the acquired data having to be transmitted to remote cloud servers for inference. This is not ideal from the system's requirements point of view. Recently, efforts to replace the cloud servers with an alternative inference device closer to the sensing platform, has given rise to a new area of research Tiny Machine Learning (TinyML). In this work, we investigate the different challenges and specifications trade-offs associated to existing hardware options, as well as recently developed software tools, when trying to use microcontroller units (MCUs) as inference devices for health and care applications. The paper also reviews existing wearable systems incorporating MCUs for monitoring, and management, in the context of different health and care intended uses. Overall, this work can be used as a kick-start for embedding machine learning models on MCUs, focusing on healthcare wearables. |
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| AbstractList | The use of machine learning in medical and assistive applications is receiving significant attention thanks to the unique potential it offers to solve complex healthcare problems for which no other solutions had been found. Particularly promising in this field is the combination of machine learning with novel wearable devices. Machine learning models, however, suffer from being computationally demanding, which typically has resulted on the acquired data having to be transmitted to remote cloud servers for inference. This is not ideal from the system's requirements point of view. Recently, efforts to replace the cloud servers with an alternative inference device closer to the sensing platform, has given rise to a new area of research Tiny Machine Learning (TinyML). In this work, we investigate the different challenges and specifications trade-offs associated to existing hardware options, as well as recently developed software tools, when trying to use microcontroller units (MCUs) as inference devices for health and care applications. The paper also reviews existing wearable systems incorporating MCUs for monitoring, and management, in the context of different health and care intended uses. Overall, this work can be used as a kick-start for embedding machine learning models on MCUs, focusing on healthcare wearables. The use of machine learning in medical and assistive applications is receiving significant attention thanks to the unique potential it offers to solve complex healthcare problems for which no other solutions had been found. Particularly promising in this field is the combination of machine learning with novel wearable devices. Machine learning models, however, suffer from being computationally demanding, which typically has resulted on the acquired data having to be transmitted to remote cloud servers for inference. This is not ideal from the system's requirements point of view. Recently, efforts to replace the cloud servers with an alternative inference device closer to the sensing platform, has given rise to a new area of research Tiny Machine Learning (TinyML). In this work, we investigate the different challenges and specifications trade-offs associated to existing hardware options, as well as recently developed software tools, when trying to use microcontroller units (MCUs) as inference devices for health and care applications. The paper also reviews existing wearable systems incorporating MCUs for monitoring, and management, in the context of different health and care intended uses. Overall, this work addresses the gap in literature targeting the use of MCUs as edge inference devices for healthcare wearables. Thus, can be used as a kick-start for embedding machine learning models on MCUs, focusing on healthcare wearables. |
| Author | Diab, Maha S. Rodriguez-Villegas, Esther |
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| SubjectTerms | Cloud computing Data acquisition Edge ML embedded machine learning Embedding Health care healthcare Inference Machine learning Machine learning algorithms Medical services microcontroller Microcontrollers Power demand Software TinyML wearable Wearable computers Wearable technology |
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| Title | Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review |
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