Data Acquisition, Processing, and Reduction for Home-Use Trial of a Wearable Video Camera-Based Mobility Aid
| Autor: | Mojtaba Moharrer, Alex R. Bowers, Vilte Baliutaviciute, Shrinivas Pundlik, Gang Luo |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Biomedical Engineering Wearable computer Video camera Blindness Machine learning computer.software_genre Article law.invention Machine Learning Wearable Electronic Devices Data acquisition law Humans Collision detection Mobility aid wearable video camera business.industry Collision naturalistic mobility Ophthalmology mobility aid Artificial intelligence business computer Algorithms Visually Impaired Persons Data reduction Coding (social sciences) |
| Zdroj: | Translational Vision Science & Technology |
| ISSN: | 2164-2591 |
| DOI: | 10.1167/tvst.9.7.14 |
| Popis: | Purpose Evaluating mobility aids in naturalistic conditions across many days is challenging owing to the sheer amount of data and hard-to-control environments. For a wearable video camera-based collision warning device, we present the methodology for acquisition, reduction, review, and coding of video data for quantitative analyses of mobility outcomes in blind and visually impaired participants. Methods Scene videos along with collision detection information were obtained from a chest-mounted collision warning device during daily use of the device. The recorded data were analyzed after use. Collision risk events flagged by the device were manually reviewed and coded using a detailed annotation protocol by two independent masked reviewers. Data reduction was achieved by predicting agreements between reviewers based on a machine learning algorithm. Thus, only those events for which disagreements were predicted would be reviewed by the second reviewer. Finally, the ultimate disagreements were resolved via consensus, and mobility-related outcome measures such as percentage of body contacts were obtained. Results There were 38 hours of device use from 10 participants that were reviewed by both reviewers, with an agreement level of 0.66 for body contacts. The machine learning algorithm trained on 2714 events correctly predicted 90.5% of disagreements. For another 1943 events, the trained model successfully predicted 82% of disagreements, resulting in 81% data reduction. Conclusions The feasibility of mobility aid evaluation based on a large volume of naturalistic data is demonstrated. Machine learning-based disagreement prediction can lead to data reduction. Translational relevance These methods provide a template for determining the real-world benefit of a mobility aid. |
| Databáze: | OpenAIRE |
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