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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2508.01576 |
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| _version_ | 1866911088487956480 |
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| author | Dao, Jeanelle Dao, Jadelynn |
| author_facet | Dao, Jeanelle Dao, Jadelynn |
| contents | According to the World Health Organization, 430 million people experience disabling hearing loss. For them, recognizing spoken commands such as one's name is difficult. To address this issue, Lumename, a real-time smartwatch, utilizes on-device machine learning to detect a user-customized name before generating a haptic-visual alert. During training, to overcome the need for large datasets, Lumename uses novel audio modulation techniques to augment samples from one user and generate additional samples to represent diverse genders and ages. Constrained random iterations were used to find optimal parameters within the model architecture. This approach resulted in a low-resource and low-power TinyML model that could quickly infer various keyword samples while remaining 91.67\% accurate on a custom-built smartwatch based on an Arduino Nano 33 BLE Sense. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01576 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lumename: Wearable Device for Hearing Impaired with Personalized ML-Based Auditory Detection and Haptic-Visual Alerts Dao, Jeanelle Dao, Jadelynn Audio and Speech Processing According to the World Health Organization, 430 million people experience disabling hearing loss. For them, recognizing spoken commands such as one's name is difficult. To address this issue, Lumename, a real-time smartwatch, utilizes on-device machine learning to detect a user-customized name before generating a haptic-visual alert. During training, to overcome the need for large datasets, Lumename uses novel audio modulation techniques to augment samples from one user and generate additional samples to represent diverse genders and ages. Constrained random iterations were used to find optimal parameters within the model architecture. This approach resulted in a low-resource and low-power TinyML model that could quickly infer various keyword samples while remaining 91.67\% accurate on a custom-built smartwatch based on an Arduino Nano 33 BLE Sense. |
| title | Lumename: Wearable Device for Hearing Impaired with Personalized ML-Based Auditory Detection and Haptic-Visual Alerts |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.01576 |