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About this Research Topic

Manuscript Submission Deadline 05 October 2022
Manuscript Extension Submission Deadline 04 November 2022

Sensing technologies have become an active area of research, due to interest in mobile devices and wireless systems that use a variety of different sensors (e.g., IMU, RF, camera, sound, radar, lora, etc.) to enable a broad range of practical internet of things (IoT) applications, including human health monitoring, human-computer interaction, augmented and virtual reality (AR/VR), and location-based services. For example, Wi-Fi signals have been used for several wireless sensing applications (such as indoor localization, gesture recognition, and vital sign monitoring). In addition, low-cost radar devices have been developed for novel wireless sensing applications in both indoor and outdoor scenarios. Beyond RF sensing, sensors in smartphones and wearable devices (IMU, acoustic signals, and cameras) have also been used for sensing applications (including AR/VR, gesture recognition, and face recognition). To improve the sensing performance, multimodal or cross-modal methods can be exploited by fusing camera/IMU and RF signals. Additionally, deep learning models can be used in sensing technologies to improve classification or detection performance. The rapid development in IoT devices, and new sensing techniques and methods, is fueling an equally fast past pace in the introduction of next-generation IoT applications.

Although sensing techniques have been developed for many IoT applications, there are still many challenges that prevent existing sensing systems from being practical in commercial use. Firstly, robustness is a major issue in RF-based health monitoring and acoustic/RF-based motion tracking, etc. For example, changes in the wireless environment (e.g., different rooms or a walking person) can reduce sensing performance in RF-based techniques. Secondly, the application of different devices and sensing techniques requires extra effort to adjust the model being used. Thirdly, power consumption and form factors also prevent many novel sensing systems from being practical in wearables. Trade-offs between power efficiency and performance still require significant research efforts. In addition, user privacy and data security are areas of concern in sensing techniques. Furthermore, big data (i.e., high quality labeled sensing data) is greatly needed to develop robust deep learning models. However, with the latest advances in deep learning, hardware acceleration, and IoT devices, there is increasing promise that such limitations can be addressed.

This Research Topic welcomes original submissions and extended versions of conference papers that contain at least 30% significant new content (for further guidelines, see: https://www.frontiersin.org/about/policies-and-publication-ethics#ConferencesProceedingsAbstracts). Topics of interest include, but are not limited to:

- Applications of machine learning to mobile sensing research
- Wearable and non-contact sensing
- Mobile health monitoring
- Mobile perception, motion tracking, and interaction in AR/VR
- Sensing with radio, light, sound, and magnetism
- Low-power sensing systems
- Wireless localization and tracking
- Wearable sensing
- Backscatter localization
- FMCW/UWB radar-based sensing
- Fusion techniques for multimodal or cross-modal sensing (e.g., camera and RF)
- Federated learning for sensing and localization
- Integrated sensing/localization and communication
- Robotic, self-driving, and drone-based applications
- Sensing in intelligent transportation systems
- Sensing in smart farming
- Edge computing, ubiquitous sensing, and context sensing
- Sensing testbeds and public datasets

Keywords: Mobile Computing and Sensing, Mobile Health, Human-Computer Interaction (HCI), Internet of Things (IoT), Localization and Motion Tracking, Augmented Reality (AR), Virtual Reality (VR)


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Sensing technologies have become an active area of research, due to interest in mobile devices and wireless systems that use a variety of different sensors (e.g., IMU, RF, camera, sound, radar, lora, etc.) to enable a broad range of practical internet of things (IoT) applications, including human health monitoring, human-computer interaction, augmented and virtual reality (AR/VR), and location-based services. For example, Wi-Fi signals have been used for several wireless sensing applications (such as indoor localization, gesture recognition, and vital sign monitoring). In addition, low-cost radar devices have been developed for novel wireless sensing applications in both indoor and outdoor scenarios. Beyond RF sensing, sensors in smartphones and wearable devices (IMU, acoustic signals, and cameras) have also been used for sensing applications (including AR/VR, gesture recognition, and face recognition). To improve the sensing performance, multimodal or cross-modal methods can be exploited by fusing camera/IMU and RF signals. Additionally, deep learning models can be used in sensing technologies to improve classification or detection performance. The rapid development in IoT devices, and new sensing techniques and methods, is fueling an equally fast past pace in the introduction of next-generation IoT applications.

Although sensing techniques have been developed for many IoT applications, there are still many challenges that prevent existing sensing systems from being practical in commercial use. Firstly, robustness is a major issue in RF-based health monitoring and acoustic/RF-based motion tracking, etc. For example, changes in the wireless environment (e.g., different rooms or a walking person) can reduce sensing performance in RF-based techniques. Secondly, the application of different devices and sensing techniques requires extra effort to adjust the model being used. Thirdly, power consumption and form factors also prevent many novel sensing systems from being practical in wearables. Trade-offs between power efficiency and performance still require significant research efforts. In addition, user privacy and data security are areas of concern in sensing techniques. Furthermore, big data (i.e., high quality labeled sensing data) is greatly needed to develop robust deep learning models. However, with the latest advances in deep learning, hardware acceleration, and IoT devices, there is increasing promise that such limitations can be addressed.

This Research Topic welcomes original submissions and extended versions of conference papers that contain at least 30% significant new content (for further guidelines, see: https://www.frontiersin.org/about/policies-and-publication-ethics#ConferencesProceedingsAbstracts). Topics of interest include, but are not limited to:

- Applications of machine learning to mobile sensing research
- Wearable and non-contact sensing
- Mobile health monitoring
- Mobile perception, motion tracking, and interaction in AR/VR
- Sensing with radio, light, sound, and magnetism
- Low-power sensing systems
- Wireless localization and tracking
- Wearable sensing
- Backscatter localization
- FMCW/UWB radar-based sensing
- Fusion techniques for multimodal or cross-modal sensing (e.g., camera and RF)
- Federated learning for sensing and localization
- Integrated sensing/localization and communication
- Robotic, self-driving, and drone-based applications
- Sensing in intelligent transportation systems
- Sensing in smart farming
- Edge computing, ubiquitous sensing, and context sensing
- Sensing testbeds and public datasets

Keywords: Mobile Computing and Sensing, Mobile Health, Human-Computer Interaction (HCI), Internet of Things (IoT), Localization and Motion Tracking, Augmented Reality (AR), Virtual Reality (VR)


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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