Potential of In-Vehicle and Smartwatch Data Streams for ... · Potential of In-Vehicle and...

Potential of In-Vehicle and Smartwatch Data Streams for

Improved Diabetes Management

MARTIN MARITSCH, SIMON FÖLL, FELIX WORTMANN

Bosch IoT Lab, a cooperation of ETH Zurich, University of St. Gallen and Bosch

Confidential | Bosch IoT Lab | 11.02.20

© Bosch Software Innovations GmbH. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution as well as in the event of

applications for industrial property rights. 2

Bosch IoT Lab

Current Project Landscape

A Connected Business B IoT Platform Economy C IoT Technology Exploitation

Blockchain-based

P2P Energy Markets

In-Vehicle Hypoglycemia

Detection and Warning

IoT Platform Business

Models

In-Vehicle Affect Reco-

gnition and Regulation

IoT Performance

Management

From Connectivity to

Margin

Data Strategy for the

IoT and AI

Wearable-supported

Diabetes Management

Equipment as a

Service




Headwind

Starting Point and Goal

Boris Vukcevic (midfielder at TSG 1899 Hoffenheim)

cause of accident: hypoglycemia (2012)

Source: welt.de, 2020; hackingdiabetes.org, 2020.

Goal: Design and Evaluation of a Vehicle Hypoglycemia Warning System in Diabetes




Headwind

Why not CGM?

Time

delay

CGM

rejection

No

reimbursement

High

financial burden

Source: Basu et al., 2013; Keenan et al., 2009; Rebrin et al., 2010.




Headwind

What about autonomous driving and vision zero?

Source: NY Times, 2019.




Headwind

Goals and partners

How must in-vehicle hypoglycemia warnings be

designed that they are

(a) perceived by drivers and

(b) that they lead to actual behavioral reactions?

RQ3

How does diagnostic accuracy compare to state-

of-the-art methods to detect hypoglycemia (e.g.

self-measurement of capillary blood glucose and

continuous glucose measurement)?

RQ2

To which degree of accuracy can hypoglycemia in

diabetic patients be detected from (a) today's and

(b) future (including physiological and video data)

real-time vehicle car sensor data streams?

RQ1

Objectives

SE

NS

ES

UP

PO

RT

Partner




HeadwindApproach

WP3: Driving in the FieldWP2: Driving in the Field

Basic Sensing Module

Basic Support Module

WP1: Driving Simulator

Enhanced Sensing Module

Enhanced Support Module

Integrated Sensing Module

Integrated Support Module

WP4: Project Management & Dissemination

Vehicle Hypo Warning System

triggersBuild & Evaluate

Build & Evaluate

Enhance & Evaluate

Enhance & Evaluate

Integrate & Evaluate

Proof of feasibility in pilot study

Demonstrate

WP0: Preparation

Q4 2017/Q1 2018




HeadwindWP1: Driving simulator

Highway

Town

Rural




HeadwindWP1: Data gathering

Recorded at 30 Hz

Driver behaviorThrottle, brake, steering wheel, …

Simulator valuesDistance to intersection, lateral position,

headway time, …

Logitech C920

Driver face recording

2x Full-HD 30fps

H.264 encoded stream

1. CAN 2. Video 3. Consumer Eye Tracker

Tobii 4C

90Hz

Gaze points

Head position/rotation




HeadwindWP1: Data gathering

4. Professional ECG 5. Consumer Smartwatch

Garmin vivoactive 3

Heart rate inter-beat-intervals

Sensor fusion with

accelerometer data*

Lifecard CF

3-lead ECG

Heart rate inter-beat-intervals

Countour XT,

Biosen C-Line, Dexcom G6

XT: venous blood glucose

C-Line: venous blood glucose

G6: sensor glucose

6. Glucose




HeadwindWP1: Clamp procedure




HeadwindFirst results from WP0

• Key variables (e.g., “velocity” and

“steering speed”) are significant at

the 1% level

Statistical significance

• Random forest

• ROC AUC: 0.72

• Balanced Accuracy: 0.62

• Deep neural networks

• ROC AUC: 0.74

• Balanced Accuracy: 0.66

Predictive models

• Driving behavior of 5 individuals (3 non-

diabetic and 2 with type 1 diabetes)

• Data for training and testing predictive

models are from disjoint groups of

subjects

• We run 1-fold cross-validation on

subject level, i.e. we train the model on

all subjects except for one, which is used

for testing and repeat this until every

subject has been in the testing set

Training of

predictive models

Source: Kraus et al., 2018.

Early prediction

model shows

between-subject

predictability




HeadwindOutlook WP2: Driving in the Field

Test track: tank training field of the Swiss Army

Measure CAN/Video/Audio Intervention interface

Medical equipment

!

Instructor pedals




RADARWearable-Based Dysglycemia Detection and Warning

Hypoglycemia Normoglycemia

3.9 mmol/L 10 mmol/L

Smartwatch shows reasonable

classification performance

Classification model captures physio-

logical response during hypoglycemia

Your blood glucose

level is probably low

TIME OF DAY

TENDENCY

SITUATION

PHYSIOLOGY

ML-based classifier for

hypoglycemia1 Explainable AI to rely on sound

cause-effect relationships2 Explainable decision-making

for everyday life3

Model evaluation

Baseline*Empatica E4 +

fasting glucose

AUC 0.5 0.815

Accuracy 0% 88.1%

Sensitivity 0% 72.3%

Specificity 100% 90.6%

Source: Maritsch et al., 2020.

Potential of In-Vehicle and Smartwatch Data Streams for

Improved Diabetes Management

MARTIN MARITSCH, SIMON FÖLL, FELIX WORTMANN

Bosch IoT Lab, a cooperation of ETH Zurich, University of St. Gallen and Bosch

Potential of In-Vehicle and Smartwatch Data Streams for ... · Potential of In-Vehicle and...

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