“Edge Machine Learning for Mobile Health Technologies”
Transcript of “Edge Machine Learning for Mobile Health Technologies”
“Edge Machine Learning for Mobile Health Technologies”
Amir Aminifar - Lund University
August 24, 2021
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Amir Aminifar
Amir Aminifar is currently a WASP Assistant Professor in the Department of Electrical and Information Technology at Lund University, Sweden. He received his Ph.D. degree from the Swedish National Computer Science Graduate School, Linköping University, Sweden, in 2016. During 2016-2020, he held a Scientist position in the Institute of Electrical Engineering at the Swiss Federal Institute of Technology (EPFL), Switzerland. Amir Aminifar has been involved in several national/international projects, including the Medical Informatics Platform (MIP) of the European Human Brain Project (HBP), the ML-edge Swiss National Science Foundation (SNSF) project, the e-Glass Swiss Federal Institute of Technology (EPFL) project, and the Wallenberg AI, Autonomous Systems and Software Program (WASP). He has a history of successful collaboration with industrial companies and medical partners, including General Motors, Texas Instruments, SmartCardia, and the Lausanne University Hospital. His research interests are centered around tiny/edge machine learning on Internet of Things (IoT), mobile health (m-Health), and wearable technologies.
Internet of Things (IoT)
Remote health monitoring is one of the main IoT applications towards the realization of preventive and precision medicine/care.
Epilepsy
Epilepsy is the second neurological cause of years of potential life lost, mainly due to seizure-related accidents and sudden unexpected death.
Epilepsy affects 65M people worldwide, with 40% higher premature mortality rate compared to the corresponding healthy population.
Source: https://mnepilepsy.org/
Real-Time Epilepsy Monitoring
Real-time seizure detection will reduce the mortality rate, based on early warnings to family members and emergency units for rescue.
● Higher quality of life● Better healthcare system ● Lift socioeconomic burden
State-of-the-Art in Real-Time Epilepsy Monitoring
New wearables
Bands & e-Glass
- Restrictive size and form factors
- Limited resources
Invasive
VNS therapy:- Only adjunctive- Side effects
Intrusive
Hats & handsfree- Stigma - Cumbersome
Source: https://www.emotiv.com/eeg-machine-example/
Resource-Constrained Medical IoT and Mobile Health
Resource-Constrained IoT[Sopic et al., ISCAS2018]
Complex Learning Algorithms
Porting
D. Sopic, A. Aminifar, and D. Atienza. "e-Glass: A Wearable System for Real-Time Detection of Epileptic Seizures." IEEE International Symposium on Circuits and Systems (ISCAS). 2018.
Example: settings
Feature set 1
Feat
ure
set 2
● Binary classification (green vs red)● Size of the input (N): 1024● Features:
● Set 1: features with complexity N● Set 2: features with complexity N.log(N)
● We are interested in:● Accuracy of the classification● Complexity of the classification
Input size N 1024
Feature set 1 complexity N 1024
Feature set 2 complexity N.log(N) 10240
Example: the Ugly (Low Quality & High Complexity)
Feature set 1
Feat
ure
set 2
low quality &
high complexity
● Accuracy● 47/50=94%
● Complexity● 10240+1024
Input size N 1024
Feature set 1 complexity N 1024
Feature set 2 complexity N.log(N) 10240
Summary of the Example
Complexity
Acc
urac
y Low quality & high complexity
(the ugly)
100%
94%
1024 3072 10240+1024
D. Sopic, A. Aminifar, A. Aminifar, and D. Atienza. “Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems.” In IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), 2018.
Example: the Bad (Low Quality & Low Complexity)
Feature set 1
Feat
ure
set 2
Low quality & complexity ● Accuracy
● 47/50=94%● Complexity
● 1024
Input size N 1024
Feature set 1 complexity N 1024
Feature set 2 complexity N.log(N) 10240
Summary of the Example
Complexity
Acc
urac
y Low quality & high complexity
(the ugly)
Low quality & low complexity
(the bad)
100%
94%
1024 3072
D. Sopic, A. Aminifar, A. Aminifar, and D. Atienza. “Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems.” In IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), 2018.
10240+1024
Example: the Good (High Quality & Medium Complexity)
Low-complexityclassifier (only set 1)
Confident?
Feature set 1
Feat
ure
set 2
No
High-complexityclassifier (sets 1 & 2)
Yes
low quality &
high complexity
Low quality & complexity
Example: the Good (High Quality & Medium Complexity)
Feature set 1
Feat
ure
set 2
low quality &
high complexity
Low quality & complexity ● Accuracy
● 50/50=100%● Complexity
● P=10/50=0.2● 1024+0.2*(10240)=3072
Input size N 1024
Feature set 1 complexity N 1024
Feature set 2 complexity N.log(N) 10240
Summary of the Example
Complexity
Acc
urac
y Low quality & high complexity
(the ugly)
High quality & medium complexity
(the good)Low quality &
low complexity(the bad)
100%
94%
1024 3072
D. Sopic, A. Aminifar, A. Aminifar, and D. Atienza. “Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems.” In IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), 2018.
10240+1024In our case, the battery lifetime is increased by a factor of 2.6, without any major machine-learning performance loss (<3%).
Distributed Resource-Aware Machine Learning
Cloud
Confident?
NoEdge
F. Forooghifar, A. Aminifar, and D. Atienza. “Resource-Aware Distributed Epilepsy Monitoring using Self-Awareness from Edge to Cloud.” IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), 2019.
In our case, the battery lifetime is increased by a factor of 3.6, without any major machine-learning performance loss (<3%).
Machine 2 Machine 3 Machine 4
Machine 1
Real-Time Federated Machine Learning
e-Glass
Cloud
We demonstrate that it is possible to train our deep neural networkin 1.86 hours using 344.34 mAh energy.
S. Baghersalimi, T. Teijeiro, D. Atienza, A. Aminifar, “Personalized Real-Time Federated Learning for Epileptic Seizure Detection.” IEEE Journal of Biomedical and Health Informatics (JBHI). 2021.
Questions?
Thank you!
Looking for a Ph.D. position in Sweden, please do not hesitate to contact me: [email protected]
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