Write in a clean editor, then format for Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies in one click — DocuGuru applies the official ACM template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies is a peer-reviewed journal published by ACM, covering Indoor and Outdoor Localization Technologies, Context-Aware Activity Recognition Systems, Tactile and Sensory Interactions. Over its lifetime it has published 1,872 papers, which have received 49,531 citations.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 12, 3, Article 45 (2023).
Formats any DOI in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies style. No sign-up. |
| Publishes research in | Indoor and Outdoor Localization Technologies Context-Aware Activity Recognition Systems Tactile and Sensory Interactions Interactive and Immersive Displays Innovative Human-Technology Interaction |
| ISSN | 2474-9567 |
| Citation impact (2-yr) | 5.74 |
| h-index | 91 |
| i10-index | 1,169 |
| Top institutions publishing here | Tsinghua University |
| Journal website | imwut.acm.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR applications. Even though DL-based approaches now outperform the state-of-the-art…
The vision of embedding connectivity into billions of everyday objects runs into the reality of existing communication technologies -- there is no existing wireless technology that can provide reliable and long-range communication at tens of microwatts of power as well as cost less than a dime. While backscatter is low-power and low-cost, it is known…
The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only…
Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because…
We propose SignFi to recognize sign language gestures using WiFi. SignFi uses Channel State Information (CSI) measured by WiFi packets as the input and a Convolutional Neural Network (CNN) as the classification algorithm. Existing WiFi-based sign gesture recognition technologies are tested on no more than 25 gestures that only involve hand and/or finger gestures. SignFi…
Proceedings of the ACM on Measurement and Analysis of Computing Systems
ACM AI Letters
ACM Journal on Autonomous Transportation Systems
ACM Transactions on Accessible Computing
ACM Transactions on Applied Perception
ACM Transactions on Computation Theory
IEEE Photonics Technology Letters
IEEE Reliability Magazine
