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Automatic Detection of Visual Search for the Elderly using Eye and Head Tracking Data

Michael Dietz, Daniel Schork, Ionut Damian, Anika Steinert, Marten Haesner, Elisabeth André


erschienen 17.08.2017 KI - Künstliche Intelligenz

Verlag: Springer Berlin Heidelberg

DOI: 10.1007/s13218-017-0502-z


With increasing age we often find ourselves in situations where we search for certain items, such as keys or wallets, but cannot remember where we left them before. Since finding these objects usually results in a lengthy and frustrating process, we propose an approach for the automatic detection of visual search for older adults to identify the point in time when the users need assistance. In order to collect the necessary sensor data for the recognition of visual search, we develop a completely mobile eye and head tracking device specifically tailored to the requirements of older adults. Using this device, we conduct a user study with 30 participants aged between 65 and 80 years (avg=71.7, 50% female) to collect training and test data. During the study, each participant is asked to perform several activities including the visual search for objects in a real-world setting. We use the recorded data to train a support vector machine (SVM) classifier and achieve a recognition rate of 97.55% with the leave-one-user-out evaluation method. The results indicate the feasibility of an approach towards the automatic detection of visual search in the wild.