Location-Aware Information Delivery with ComMotion

Marmasse, N. and Schmandt, C. 2000. Location-Aware Information Delivery with ComMotion. In Proceedings of the 2nd international Symposium on Handheld and Ubiquitous Computing (Bristol, UK, September 25 – 27, 2000). P. J. Thomas and H. Gellersen, Eds. Lecture Notes In Computer Science, vol. 1927. Springer-Verlag, London, 157-171.

ComMotion is a location-aware computing environment which links personal information to locations. Its uses GPS position sensing to gradually learn about the locations of the users’ daily life based on travel patterns. The authors use a simple learning algorithm to exploit the GPS signals loss to detect building. When the GPS signal was lost and then later re–acquired within a certain radius, comMotion considered this to be indicative of a building. This approach avoided false detection of buildings when passing through urban canyons or suffering from hardware issues such as battery loss.

Unfortuantally, the evalutation of the system was only done with 4 people. Therefore the feedback on location precision is rather weak:

Precision and Alert Timing. GPS data is intentionally imprecise –when the user evaluation was done, accuracy was within 100 metres. For this application, exact position information is not required. When two different virtual locations are physically within meters of each other, however, due to the inaccuracy of the position data, one location is identified and not the other –that is, location shadowing. This can be solved by clustering the virtual locations and providing alerts for all the locations within the cluster. The lack of precision of position data also strongly affects the alert timing and auditory cues were sometimes given too late. Loss of GPS signal due to shadowing by tall buildings was also experienced.

Relation to my thesis: The authors acknowledge that location accuracy and reliability must be taken into account for the design of location-aware application. They integrate the signal loses to make sense of the space. They also mention the importance of the granularity of the location information. However, the feedback of only 4 people is really limiting to evaluate if the learning algorithm and the design. This work is very much related to Learning Significant Locations and Predicting User Movement with GPS.