An Ontology for Wearables Data Interoperability and Ambient Assisted Living Application Development
1. An ontology for wearables data
interoperability and Ambient Assisted
Living application development
Natalia Díaz-Rodríguez, S Grönroos, F Wickström, J Lilius, H Eertink, A
Braun, P Dillen, J Crowley, J Alexandersson
World Conference on Soft Computing, Berkeley, California. May 23rd
2016
50th Anniversary of Fuzzy Logic and Its Applications
and 95th Birthday Anniversary of LOTFI A. ZADEH
2. Background: Ambient Assisted Living
• Usage of technology to provide assistance to people who need
it in their daily activities, in the less obtrusive way
• Aim: support older/disadvantaged people, independent living,
safety
USE CASE: ACTIVE HEALTHY AGEING Project
EIT Digital Action Line on Health and well-being
[Img credit: M.Ros et al.2011]
3. Vertical activities in Active Healthy Ageing
Project
• Cognitive Endurance: detection of mild cognitive impairment
(1st stages of dementia). Reminiscence therapy. Tracking:
physical activity and HR
• Burn-out turnout: stress, HR, activity (lifestyle patterns),
sleep
• Virtual social gym: physical fitness (HR and calories)
• ConnectedCare: Personalized alarm management, online
collaboration platform for caregivers and professionals
4.
5. Interaction between AHA Platform
components with PHL data-store
1. Home Application Gateway (INRIA -Grenoble/DFKI/Åbo
Akademi):
• Sensor data interpretation
2. Mobidot MoveSmarter platform (Novay)
• Interprets mobility data (GPS and accelerometer data)
• Detects individual trips and travel modalities
3. Philips bracelet:
• Monitors HR and derived stress levels.
6. Case study: A Kinect ontology for physical
exercise annotation and recognition
• Active Healthy Ageing project (EIT Digital)
• Philips Personal Health Labs (PHL)
• Sensor data
aggregation platform
7. AHA Project -> Wearables Ontology:
• Different datatypes, units, frequency and update rate
• Wearable devices and vital signs for health continuous monitoring
• Person’s weight
• Height
• Location
• Step & calorie count
• Sleep
• Location
• Activity level
• Activity energy expenditure
• Heart rate
• Stress level and valence (GSR)
• Ambient light level
• Ambient temperature
• Skin temperature
• Skin conductance
11. Sit to stand session for elders activity
monitoring
SitStandSessionDDMMYY:
{"name": "SitStandSession",
"ended_at": "2013-08-13 11:32:29”
"started_at": "2013-08-13 11:20:34”}
12.
13. Examples of use
Example 1: Defining basic movement (Stand, BendDown, TwistRight, MoveObject, etc).
Example 2: When defining, e.g. SitStandExercise workout, the N of series done in time as well as the
exercise quality can be measured and compared with predefined medical guidelines, to give feedback.
14. Examples of use 2
Example 3: Historic analysis can be provided to monitor posture quality in time. E.g. having back
less straight than 1 year ago can be notified to correct/prevent on time.
Example 4: An office worker can be notified when he is not having straight back and neck or when
he has been sitting for too long.
16. Future work
Activity recognition:
• Multiple human sensing
• Parallel/interleaved activities
• Automatic ontology learning and evolution
work
• (FOL/DL) Logics support for temporal
constraints
A folkloric dance flamenco virtual tutor
17. Future work
• FPV Wearable cameras for AAL (elders, visually impaired)
• Unsupervised activity modelling and automatic dataset
annotation
• EGOSHOTS Dataset: https://github.com/NataliaDiaz/Egoshots
18. More info? collaborations? Master students
without topic?
Welcome!
ndiaz@decsai.ugr.es
diaz.rodriguez.natalia@gmail.com
https://about.me/NataliaDiazRodriguez
• Wearables, security and access control, activity recognition and
Kinect ontologies available: https://github.com/NataliaDiaz/
Ontologies
• AHA Platform Project: https://www.eitdigital.eu/fileadmin/files/
HWB-pictures/EIT-Handout-HWB_EoY-1213-spreads-HR.pdf