Abstract
PurposeTo improve the acceptance and compliance of treatment of amblyopia, the aim of this study was to show that it is feasible to design an electronic frame for context-sensitive liquid crystal glasses, which can measure the state of wear position in a robust manner and detect distinct motion patterns for activity recognition.MethodsDifferent temple designs with integrated temperature and capacitive sensors were developed to realize the detection of the state of wear position to distinguish three states (correct position/wrong position/glasses taken off). The electronic glasses frame was further designed as a tool for accelerometer data acquisition, which was used for algorithm development for activity classification. For this purpose, training data of 20 voluntary healthy adult subjects (5 females, 15 males) were recorded and a 10-fold cross-validation was computed for classifier selection. In order to perform functional testing of the electronic glasses frame, a proof of concept study was performed in a small group of healthy adults. Four healthy adult subjects (2 females, 2 males) were included to wear the electronic glasses frame and to protocol their activities in their everyday life according to a defined test protocol. Individual and averaged results for the precision of the state of wear position detection and of the activity recognition were calculated.ResultsContext-sensitive control algorithms were developed which detected the state of wear position and activity in a proof of concept. The pilot study revealed an average of 91.4% agreement of the detected states of wear position. The activity recognition match was 82.2% when applying an additional filter criterion. Removing the glasses was always detected 100% correctly.ConclusionThe principles investigated are suitable for detecting the glasses’ state of wear position and for recognizing the wearer´s activity in a smart glasses concept.
Highlights
Amblyopia is one of the most common visual disorders in children [1]
The electronic glasses frame was further designed as a tool for accelerometer data acquisition, which was used for algorithm development for activity classification
Context-sensitive control algorithms were developed which detected the state of wear position and activity in a proof of concept
Summary
Amblyopia is one of the most common visual disorders in children [1] It affects young patients in their daily life, influences job options and increases the risk for visual loss and trauma of the healthy eye throughout the children’s entire future [2,3,4,5,6]. This visual developmental disorder is best reversible in the sensitive phase of vision development [7]. Because the better seeing eye is covered with an uncomfortable, often disfiguring adhesive plaster the acceptance of the therapeutic approach and adherence is often problematic [9]
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