Improving Activity Recognition Without Sensor Data: A Comparison Study of Time Use Surveys

Provided by: Association for Computing Machinery
Topic: Mobility
Format: PDF
Wearable sensing systems, through their proximity with their user, can be used to automatically infer the wearer's activity to obtain detailed information on availability, behavioural patterns and health. For this purpose, classifiers need to be designed and evaluated with sufficient training data from these sensors and from a representative set of users, which requires starting this procedure from scratch for every new sensing system and set of activities. To alleviate this procedure and optimize classification performance, the use of time use surveys has been suggested: these large databases contain typically several days worth of detailed activity information from a large population of hundreds of thousands of participants.

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