On-Device Deep Personalization for Robust Activity Data Collection
One of the biggest challenges of activity data collection is the need to rely on users and keep them engaged to continually provide labels. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. This study proposes a...
Main Authors: | Nattaya Mairittha, Tittaya Mairittha, Sozo Inoue |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-12-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/1/41 |
Similar Items
-
On-Device Deep Learning Inference for Efficient Activity Data Collection
by: Nattaya Mairittha, et al.
Published: (2019-08-01) -
Evaluating a Spoken Dialogue System for Recording Systems of Nursing Care
by: Tittaya Mairittha, et al.
Published: (2019-08-01) -
Automatic Labeled Dialogue Generation for Nursing Record Systems
by: Tittaya Mairittha, et al.
Published: (2020-07-01) -
Fine-Tuning of Pre-Trained Deep Face Sketch Models Using Smart Switching Slime Mold Algorithm
by: Khaled Mohammad Alhashash, et al.
Published: (2023-04-01) -
User Authentication by Gait Data from Smartphone Sensors Using Hybrid Deep Learning Network
by: Qian Cao, et al.
Published: (2022-06-01)