Exploring audio and kinetic sensing on earable devices

Author:

Min Chulhong1,Mathur Akhil1,Kawsar Fahim2

Affiliation:

1. Nokia Bell Labs

2. Nokia Bell Labs and TU Delft

Publisher

ACM

Reference17 articles.

1. Analysis of Chewing Sounds for Dietary Monitoring

2. EarBit

3. Yujie Dong Jenna Scisco Mike Wilson Eric Muth and Adam Hoover. 2014. Detecting periods of eating during free-living by tracking wrist motion. IEEE journal of biomedical and health informatics 18 4 (2014) 1253--1260. Yujie Dong Jenna Scisco Mike Wilson Eric Muth and Adam Hoover. 2014. Detecting periods of eating during free-living by tracking wrist motion. IEEE journal of biomedical and health informatics 18 4 (2014) 1253--1260.

4. Preprocessing techniques for context recognition from accelerometer data

5. Nils Y Hammerla Shane Halloran and Thomas Ploetz. 2016. Deep convolutional and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880 (2016). Nils Y Hammerla Shane Halloran and Thomas Ploetz. 2016. Deep convolutional and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880 (2016).

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1. Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary Assessment;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2024-08-22

2. Towards a Personal Audio Space in Homes: Investigating Future Sound Management with Personal Audio Technologies;ACM International Conference on Interactive Media Experiences;2024-06-07

3. IMChew: Chewing Analysis using Earphone Inertial Measurement Units;Proceedings of the Workshop on Body-Centric Computing Systems;2024-06-03

4. The EarSAVAS Dataset;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2024-05-13

5. EarDA: Towards Accurate and Data-Efficient Earable Activity Sensing;2024 IEEE Coupling of Sensing & Computing in AIoT Systems (CSCAIoT);2024-05-13

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