Potential of In-Vehicle and Smartwatch Data Streams for Improved Diabetes Management - MARTIN MARITSCH, SIMON FÖLL, FELIX WORTMANN
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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
Bosch IoT Lab
Current Project Landscape
A Connected Business B IoT Platform Economy C IoT Technology Exploitation
From Connectivity to IoT Performance IoT Platform Business Blockchain-based In-Vehicle Affect Reco- In-Vehicle Hypoglycemia
Margin Management Models P2P Energy Markets gnition and Regulation Detection and Warning
Data Strategy for the Equipment as a Wearable-supported
IoT and AI Service Diabetes Management
Confidential | Bosch IoT Lab | 11.02.20
2 © 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.
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
Confidential | Bosch IoT Lab | 11.02.20
3 © 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.
Headwind
Why not CGM?
Time CGM No High
delay rejection reimbursement financial burden
Source: Basu et al., 2013; Keenan et al., 2009; Rebrin et al., 2010.
Confidential | Bosch IoT Lab | 11.02.20
4 © 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.
Headwind
What about autonomous driving and vision zero?
Source: NY Times, 2019.
Confidential | Bosch IoT Lab | 11.02.20
5 © 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.
Headwind
Goals and partners
Objectives Partner
To which degree of accuracy can hypoglycemia in
diabetic patients be detected from (a) today's and
RQ1 (b) future (including physiological and video data)
real-time vehicle car sensor data streams?
SENSE
How does diagnostic accuracy compare to state-
of-the-art methods to detect hypoglycemia (e.g.
RQ2 self-measurement of capillary blood glucose and
continuous glucose measurement)?
SUPPORT
How must in-vehicle hypoglycemia warnings be
designed that they are
RQ3 (a) perceived by drivers and
(b) that they lead to actual behavioral reactions?
Confidential | Bosch IoT Lab | 11.02.20
6 © 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.
Headwind
Approach
WP0: Preparation WP1: Driving Simulator WP2: Driving in the Field WP3: Driving in the Field
Demonstrate Build & Evaluate Enhance & Evaluate
Integrate & Evaluate
Proof of feasibility in pilot Basic Sensing Module Enhanced Sensing Module
study Integrated Sensing Module
triggers
Build & Evaluate Enhance & Evaluate Integrated Support Module
Q4 2017/Q1 2018 Basic Support Module Enhanced Support Module Vehicle Hypo Warning System
WP4: Project Management & Dissemination
Confidential | Bosch IoT Lab | 11.02.20
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applications for industrial property rights.
Headwind
WP1: Driving simulator
Rural
Highway
Town
Confidential | Bosch IoT Lab | 11.02.20
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applications for industrial property rights.
Headwind
WP1: Data gathering
1. CAN 2. Video 3. Consumer Eye Tracker
Recorded at 30 Hz
Logitech C920 Tobii 4C
Driver behavior
Throttle, brake, steering wheel, … Driver face recording 90Hz
Simulator values 2x Full-HD 30fps Gaze points
Distance to intersection, lateral position, H.264 encoded stream Head position/rotation
headway time, …
Confidential | Bosch IoT Lab | 11.02.20
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applications for industrial property rights.
Headwind
WP1: Data gathering
4. Professional ECG 5. Consumer Smartwatch 6. Glucose
Countour XT,
Garmin vivoactive 3 Biosen C-Line, Dexcom G6
Lifecard CF
Heart rate inter-beat-intervals XT: venous blood glucose
3-lead ECG
Sensor fusion with C-Line: venous blood glucose
Heart rate inter-beat-intervals
accelerometer data*
G6: sensor glucose
Confidential | Bosch IoT Lab | 11.02.20
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applications for industrial property rights.Headwind
WP1: Clamp procedure
Confidential | Bosch IoT Lab | 11.02.20
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applications for industrial property rights.Headwind
First results from WP0
Training of Statistical significance
predictive models • Key variables (e.g., “velocity” and
“steering speed”) are significant at
the 1% level
• Driving behavior of 5 individuals (3 non-
diabetic and 2 with type 1 diabetes)
• Data for training and testing predictive Early prediction
models are from disjoint groups of
Predictive models model shows
subjects
• Random forest between-subject
• We run 1-fold cross-validation on predictability
• ROC AUC: 0.72
subject level, i.e. we train the model on
all subjects except for one, which is used • Balanced Accuracy: 0.62
for testing and repeat this until every • Deep neural networks
subject has been in the testing set
• ROC AUC: 0.74
• Balanced Accuracy: 0.66
Source: Kraus et al., 2018.
Confidential | Bosch IoT Lab | 11.02.20
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applications for industrial property rights.Headwind
Outlook WP2: Driving in the Field
Test track: tank training field of the Swiss Army
!
Measure CAN/Video/Audio Intervention interface
Instructor pedals Medical equipment
Confidential | Bosch IoT Lab | 11.02.20
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applications for industrial property rights.RADAR
Wearable-Based Dysglycemia Detection and Warning
1 ML-based classifier for
hypoglycemia 2 Explainable AI to rely on sound
cause-effect relationships 3 Explainable decision-making
for everyday life
3.9 mmol/L 10 mmol/L
Hypoglycemia Normoglycemia
Model evaluation
Your blood glucose
level is probably low
Empatica E4 +
Baseline* TIME OF DAY
fasting glucose TENDENCY
SITUATION
AUC 0.5 0.815 PHYSIOLOGY
Accuracy 0% 88.1%
Sensitivity 0% 72.3%
Specificity 100% 90.6%
Smartwatch shows reasonable Classification model captures physio-
classification performance logical response during hypoglycemia
Source: Maritsch et al., 2020.
Confidential | Bosch IoT Lab | 11.02.20
14 © 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.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
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