The logging of Activities of Daily Living (ADLs) is becoming increasingly popular mainly thanks to wearable devices. Currently, most sensors used for ADLs logging are queried and filtered mainly by location and time. However, in an Internet of Things future, a query will return a large amount of sensor data. Therefore, existing approaches will not be feasible because of resource constraints and performance issues. Hence more fine-grained queries will be necessary. We propose to filter on the likelihood that a sensor is relevant for the currently sensed activity. Our aim is to improve system efficiency by reducing the amount of data to query, store and process by identifying which sensors are relevant for different activities during the ADLs logging by relying on Distributional Semantics over public text corpora and unsupervised hierarchical clustering. We have evaluated our system over a public dataset for activity recognition and compared our clusters of sensors with the sensors involved in the logging of manually-annotated activities. Our results show an average precision of 89% and an overall accuracy of 69%, thus outperforming the state of the art by 5% and 32% respectively. To support the uptake of our approach and to allow replication of our experiments, a Web service has been developed and open sourced.

1. Distributional Semantics and Unsupervised Clustering
for Sensor Relevancy Prediction
Myriam Leggieri
PhD, Insight Centre for Data Analytics,
National University of Ireland, Galway
myriam.leggieri@insight-centre.org
Myriam Leggieri, Brian Davis, John Breslin
25h August 2015, IWCMC2015

2. 2
Relevancy Prediction
in Activity Logging

3. 3
Relevancy Prediction
in Activity Logging
Distributional
Semantics
Hierarchical
Clustering
Feature of
interest
(FoI)

4. 4
Relevancy Prediction
in Activity Logging
1. from DataHub:
Algorithm
Sensors sharing
Location & Time
Activated
Sensors
2. EasyESA Similarity (X,Y)
X
Y
3. Add to Distance Matrix
4. Clustering
Sensors in the same cluster are relevant for the same activity.
Activity = Cluster
Relevant
Z

5. Relevancy Prediction:
Distributional Semantics
Term frequency
Inverse document frequency
model
term frequency of term t in document d
tot documents
tot documents containing the term t

6. Relevancy Prediction:
Hierarchical Clustering
Unweighted Pair Group Method
with Arithmetic mean (UPGMA)
Weighted Pair Group Method with
Arithmetic mean (WPGMA)
Farthest Point or VoorHees (VH)
Reflection of
Semantic
Distribution
Reflection of
Structural
Subdivision
Reflection of
Centrality

7. 7
Relevancy Prediction:
Evaluation Data
27 FoIs, 200 Sensors —> 351 Similarity Pairs
Worst case scenario:
only one of the sensors sharing the same location at the same time
range has recently sensed a change in status for the current ongoing
activity

8. Relevancy Prediction:
Evaluation: Precision
VH: 89.5%
WPGMA: 85.6%
UPGMA: 75.2%

9. Relevancy Prediction:
Evaluation: Accuracy

10. Relevancy Prediction:
Hierarchical Clustering
Unweighted Pair Group Method
with Arithmetic mean (UPGMA)
Weighted Pair Group Method with
Arithmetic mean (WPGMA)
Farthest Point or VoorHees (VH)
Reflection of
Semantic
Distribution
Distance
Reflection of
Structural
Subdivision
Reflection of
Centrality

11. Relevancy Prediction:
Evaluation: Comparison with SoTA

12. Relevancy Prediction:
Evaluation: Performance
HTTP PUT requests: 3ms
Overall Execution: 18ms
Dataset Discovery on DataHub: 3ms
(20 datasets)
LD4S SPARQL response: 246ms
ESA: 14ms (351 similarity pairs)
Easy-ESA response: 9ms

13. Conclusions
Activity-wise prediction of sensor relevancy automated with outstanding precision
Support uptake and reproducibility of our methodology
use of publicly available services and datasets
Future Work
Reasoning over the clustering centroids to label activities
Improve the similarity calculations by running ESA on domain-specific corpus
Exploit the specification-based sensor representation
13

14. Myriam Leggieri
PhD, Insight Centre for Data Analytics,
National University of Ireland, Galway
myriam.leggieri@insight-centre.org
Thanks!