Development of a Multi-Device Nutrition Logging Prototype Including a Smartscale

Seiderer, Andreas; André, Elisabeth

doi:10.1145/3079452.3079486

Cited by 2 publications

(1 citation statement)

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“…This is likely due to privacy concerns and feelings of discomfort when recording voice in public places [16]. As privacy was noted as a particular concern of users [16], [32] using speech recordings may introduce a new form of bias if data collection requires using speech in public venues. This potential bias requires further investigation to mitigate data loss.…”

Section: A Speech Recordingmentioning

confidence: 99%

Speech Recording for Dietary Assessment: A Systematic Literature Review

2022

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Traditional methods of capturing people's dietary intake are complex and labour-intensive, requiring a high level of literacy and time. Speech recording has potential to reduce these barriers, and recent technological advances have greatly increased the viability of this approach. The aim of this paper is to establish the current state of research on the usage of speech records in dietary assessment. To this end, we performed a systematic literature review and summarised the current state of research along a conceptual framework that captures the components involved in using speech records for dietary assessment. Six databases from the nutrition and computing domains were interrogated, resulting in 21 relevant papers. Speech recording in an unstructured format was preferred when compared against other methods by all three studies reporting comparisons. High technological satisfaction and ease of use were noted by all eight studies reporting user acceptance. When recording data, 78% of studies focused on collecting prospective food records. The choice of device reflected this, with 15 of 18 studies reporting a form of handheld, portable collection device intended to be always available. To process data, nine studies performed automated speech transcription achieving an average accuracy of 83%, seven of which utilized a readily available commercial service. Of the five studies that used natural language processing to further automate analysis, an average accuracy of 82% was reported. Further research is required to adapt these prototypes to address practical challenges in dietary assessment and monitoring (e.g. self-monitoring for low-literacy users).

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Section: A Speech Recordingmentioning

confidence: 99%