3 research outputs found
The SPHERE Challenge:Activity Recognition with Multimodal Sensor Data
This paper outlines the Sensor Platform for HEalthcare in Residential
Environment (SPHERE) project and details the SPHERE challenge that will take
place in conjunction with European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery (ECML-PKDD) between March and
July 2016. The SPHERE challenge is an activity recognition competition where
predictions are made from video, accelerometer and environmental sensors.
Monetary prizes will be awarded to the top three entrants, with Euro 1,000
being awarded to the winner, Euro 600 being awarded to the first runner up, and
Euro 400 being awarded to the second runner up.Comment: Paper describing dataset. 11 pages; 4 figure
Talk, text, tag? Understanding self-annotation of smart home data from a user’s perspective
Delivering effortless interactions and appropriate interventions through pervasive systems requires making sense of multiple streams of sensor data. This is particularly challenging when these concern people’s natural behaviours in the real world. This paper takes a multidisciplinary perspective of annotation and draws on an exploratory study of 12 people, who were encouraged to use a multi-modal annotation app while living in a prototype smart home. Analysis of the app usage data and of semi-structured interviews with the participants revealed strengths and limitations regarding self-annotation in a naturalistic context. Handing control of the annotation process to research participants enabled them to reason about their own data, while generating accounts that were appropriate and acceptable to them. Self-annotation provided participants an opportunity to reflect on themselves and their routines, but it was also a means to express themselves freely and sometimes even a backchannel to communicate playfully with the researchers. However, self-annotation may not be an effective way to capture accurate start and finish times for activities, or location associated with activity information. This paper offers new insights and recommendations for the design of self-annotation tools for deployment in the real world