|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'504'928
Articles rated: 2609
26 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Process Progress Estimation and Phase Detection | | Xinyu Li
; Yanyi Zhang
; Jianyu Zhang
; Yueyang Chen
; Shuhong Chen
; Yue Gu
; Moliang Zhou
; Richard A. Farneth
; Ivan Marsic
; Randall S. Burd
; | | Date: |
28 Feb 2017 | | Abstract: | Process modeling and understanding is fundamental for advanced human-computer
interfaces and automation systems. Recent research focused on activity
recognition, but little work has focused on process progress detection from
sensor data. We introduce a real-time, sensor-based system for modeling,
recognizing and estimating the completeness of a process. We implemented a
multimodal CNN-LSTM structure to extract the spatio-temporal features from
different sensory datatypes. We used a novel deep regression structure for
overall completeness estimation. By combining process completeness estimation
with a Gaussian mixture model, our system can predict the process phase using
the estimated completeness. We also introduce the rectified hyperbolic tangent
(rtanh) activation function and conditional loss to help the training process.
Using the completeness estimation result and performance speed calculations, we
also implemented an online estimator of remaining time. We tested this system
using data obtained from a medical process (trauma resuscitation) and sport
events (swim competition). Our system outperformed existing implementations for
phase prediction during trauma resuscitation and achieved over 80% of process
phase detection accuracy with less than 9% completeness estimation error and
time remaining estimation error less than 18% of duration in both dataset. | | Source: | arXiv, 1702.8623 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER