ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2017
Progress Estimation and Phase Detection for Sequential Processes.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory.BMC medical informatics and decision making · 2023Article
- Real-time medical phase recognition using long-term video understanding and progress gate method.Medical image analysis · 2021Article
- SD-Net: joint surgical gesture recognition and skill assessment.International journal of computer assisted radiology and surgery · 2021Article
- Designing Interactive Alerts to Improve Recognition of Critical Events in Medical Emergencies.DIS. Designing Interactive Systems (Conference) · 2021Article
- Design and Development of Diabetes Management System Using Machine Learning.International journal of telemedicine and applications · 2020Article
- An Analysis of Speech as a Modality for Activity Recognition during Complex Medical Teamwork.International Conference on Pervasive Computing Technologies for Healthcare : [proceedings]. International Conference on Pervasive Computing Technologies for Healthcare · 2018Article
- Region-based Activity Recognition Using Conditional GAN.Proceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia · 2017Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Process modeling and understanding are fundamental for advanced human-computer interfaces and automation systems. Most recent research has focused on activity recognition, but little has been done on sensor-based detection of process progress. We introduce a real-time, sensor-based system for modeling, recognizing and estimating the progress of a work process. We implemented a multimodal deep learning structure to extract the relevant spatio-temporal features from multiple sensory inputs and used a novel deep regression structure for overall completeness estimation. Using process completeness estimation with a Gaussian mixture model, our system can predict the phase for sequential processes. The performance speed, calculated using completeness estimation, allows online estimation of the remaining time. To train our system, we introduced a novel rectified hyperbolic tangent (rtanh) activation function and conditional loss. Our system was tested on data obtained from the medical process (trauma resuscitation) and sports events (Olympic swimming competition). Our system outperformed the existing trauma-resuscitation phase detectors with a phase detection accuracy of over 86%, an F1-score of 0.67, a completeness estimation error of under 12.6%, and a remaining-time estimation error of less than 7.5 minutes. For the Olympic swimming dataset, our system achieved an accuracy of 88%, an F1-score of 0.58, a completeness estimation error of 6.3% and a remaining-time estimation error of 2.9 minutes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.