Skin condition estimation via sensing data from mobile and wearable devices

Skin condition estimation via sensing data from mobile and wearable devices - Skin condition estimation via sensing data from mobile and wearable devices

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000045853

Enrollment

Registered

2021-10-25

Start date

2021-10-25

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Acne

Interventions

None listed

Eligibility

Sex/Gender

Female

Inclusion criteria

Inclusion criteria: 1) Age: healthy adult aged 20 years or older, but less than 30 years old 2)2) Gender: Japanese females 3)Females who were fully informed of the study's purpose, contents, and conditions of participation via the web site, and who gave their free written consent with sufficient understanding.

Exclusion criteria

Exclusion criteria: 1)Who are not pregnant 2)Who are not breastfeeding 3)Who have not had acne within a year

Design outcomes

Primary

Measure	Time frame
The skin images recorded during the 60 days of the study will be judged according to the criteria of Joint Acne Image Grading and Counting via Label Distribution Learning (1) to determine with or without acne, and will be compared with lifelog data obtained from lifestyle. (1) X. Wu et al., "Joint Acne Image Grading and Counting via Label Distribution Learning," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 10641-10650, doi: 10.1109/ICCV.2019.01074.	—

Measure

Time frame

The skin images recorded during the 60 days of the study will be judged according to the criteria of Joint Acne Image Grading and Counting via Label Distribution Learning (1) to determine with or without acne, and will be compared with lifelog data obtained from lifestyle. (1) X. Wu et al., "Joint Acne Image Grading and Counting via Label Distribution Learning," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 10641-10650, doi: 10.1109/ICCV.2019.01074.

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Countries

Japan

Contacts

Public ContactYui Kakino

Keio University Graduate School of Media and Governance

yuui@sfc.keio.ac.jp0466-47-0836

Outcome results