“Edge Machine Learning for Mobile Health Technologies”

Amir Aminifar - Lund University

August 24, 2021

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Local Committee in Sweden

Ali Balador, Senior Researcher RISE, Assistant Professor MDH, ali.balador@ri.se

Johan Malm, AI Engineer, PhD, Imagimob AB, johan.malm@imagimob.com

Magnus Melander, Evangelist and Co-founder THINGS, magnus@wbird.se

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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.

Edge Machine Learning for Mobile Health TechnologiesAmir AminifarLund University (LU), Sweden

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/

Epilepsy Monitoring in Hospitals

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.

Edge Machine Learning

Example: the Good, the Bad, and the Ugly...

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%).

Fog

Distributed Resource-Aware Machine Learning

CloudEdge

Confident?

No

Confident?No

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: amir.aminifar@eit.lth.se

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