an ontology for wearables data interoperability and ambient assisted living application development

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

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]

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

Interaction between AHA Platform components with PHL data-store1. 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.

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

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

Kinect Ontology Classes

Kinect Ontology classes, data & object properties

Sit to stand session for elders activity monitoringSitStandSessionDDMMYY: {"name": "SitStandSession", "ended_at": "2013-08-13 11:32:29” "started_at": "2013-08-13 11:20:34”}

Examples of useExample 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.

Examples of use 2Example 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.

Further integrationActivity recognition in AAL

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

Future work• FPV Wearable cameras for AAL (elders, visually impaired) • Unsupervised activity modelling and automatic dataset

annotation • EGOSHOTS Dataset: https://github.com/NataliaDiaz/Egoshots

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