This document describes a proposed activity monitor system to prevent sedentary behavior. The system would use sensors like accelerometers, Bluetooth, WiFi, and GPS to collect user context including location, activity level, and biometric data. It would use an ontology and embedded reasoner to analyze the sensor data and detect the user's current activity. If it determines the user has been sedentary for too long based on short-term and long-term evaluation windows, it would generate alerts like sounds to encourage movement. It would fuse location data from multiple sensors to determine the user's precise location using an algorithm. The system would have a lightweight service-oriented framework including components for sensing, management, inference

1. josue@grpss.ssr.upm.es
A ubiquitous activity-monitor to
prevent sedentariness
Josué Iglesias, Jesús Cano, Ana M. Bernardos, José R. Casar
Data Processing and Simulation Group – GPDS
Technical University of Madrid – UPM
PerCom 2011 Work in Progress – Teaser Madness

2. Activity Monitor
overview josue@grpss.ssr.upm.es
user context
location user’s activity level
activity evaluation
biometric data
temporal information
...
ALERT
type: sound
ontology-based
… embedded reasoner
when
how
context-aware
notifications
embedded service-oriented framework
management
subsystem KBmanager inferenceEngine … locationFusion
sensing
subsystem accelerometers BT WiFi … biomedical

3. Activity Monitor
overview josue@grpss.ssr.upm.es
user context
location user’s activity level
activity evaluation
biometric data
temporal information
...
ALERT
type: sound
ontology-based
… embedded reasoner
when
how
context-aware
notifications
features overview

4. Activity Monitor
embedded service-oriented framework josue@grpss.ssr.upm.es
Applications
... Ontology Model Inference
other enablers Location Fusion ... Engine
Manager
Management Subsystem Light Inference System - LIS
DataConsumer Communicat. Manager
Subscription Calendar ...
Registry Measurement Action SensorManager
WatchSensor
Core Subsystem
Sensing Subsystem BT WiFi ... other sensors ... GPS
CASanDRA

5. Activity Monitor
embedded service-oriented framework
e josue@grpss.ssr.upm.es
A c tiv it y A p p lic a tio n L o g ic A c t iv it y A p p lic a t io n G U I
CASanDRA core
A e
In fe re n c e A to m ic A c t iv ity A c t iv ity A la rm
W iF i G PS
E n g in e D e te c to r E v a lu a to r G e n e ra to r
C ASanD R A L o c a t io n E n e rg y C o s t
T im e BT
m odel F u s io n M e te r
sensors
s CASanDRA enablers
A s application-specific enablers
c s

6. Activity Monitor
atomic-activity detector josue@grpss.ssr.upm.es
ALERT
type: sound
[ hmartin@grpss.ssr.upm.es
]

7. Activity Monitor
location fusion josue@grpss.ssr.upm.es
offline Point Of Interest configuration online location
POI1
1 1 POI1
POI1 fusion
POI1
POI1
GPS GPS
WiFi WiFi algorithm
Bluetooth Bluetooth
… …
POI1
POI1
POIN fusion
POI1
POIN
GPS GPS
WiFi WiFi algorithm
Bluetooth Bluetooth
[ eduardo.metola@grpss.ssr.upm.es ]

8. Activity Monitor
activity level evaluation josue@grpss.ssr.upm.es
temporal evaluation windows:
short-term (t = 1 hour)
s alarm generation
long-term (t = 24 hours)
l
Energy cost is specifically measured in PARs (Physical
Activity Ratio). The number of PARs associated to any
activity only depends on the activity itself and not in the
person performing the activity: PARs are multiple of BMR
(Basal Metabolic Rate) per minute (BMR is the minimal
rate of energy expenditure compatible with life).

9. Activity Monitor
Light Inference System – overview josue@grpss.ssr.upm.es
Application
Output
Adapter External Context Manager
μJena OWL
...
... (Ontology
Context
Manager)
Aggregator
External Context Handlers
GenericEnabler OntologyModelManager
SQWRL SWRL
Bossam (Ontology Reasoner)
InferenceEngine
LIS – Light Inference System
Enabler internal data exchange
Light Framework Core Subsystem event exchange

10. Activity Monitor
Light Inference System – data flow josue@grpss.ssr.upm.es