Due to the ever-growing powers in sensing, computing, communicating and storing, mobile devices (e.g., smartphone, smartwatch, smart glasses) become ubiquitous and an indispensable part of people’s daily life. Until now, mobile devices have been adopted in many applications, e.g., exercise assessment, daily life monitoring, human-computer interactions, user authentication, etc. Among the various applications, Human Activity Recognition (HAR) is the core technology behind them. Specifically, HAR gets the sensor data corresponding to human activities based on the built-in sensors of mobile devices, and then adopts suitable recognition approaches to infer the type of activity based on sensor data. The last two decades have witnessed the ever-increasing research in HAR. However, new challenges and opportunities are emerging, especially for HAR based on mobile devices. Therefore, in this paper, we review the research of HAR based on mobile devices, aiming to advance the following research in this area. Firstly, we give an overview of HAR based on mobile devices, including the general rationales, main components and challenges. Secondly, we review and analyze the research progress of HAR based on mobile devices from each main aspect, including human activities, sensor data, data preprocessing, recognition approaches, evaluation standards and application cases. Finally, we present some promising trends in HAR based on mobile devices for future research.

In recent years, healthcare IoT have been helpful in mitigating pressures of hospital and medical resources caused by aging population to a large extent. As a safety-critical system, the rapid response from the health care system is extremely important. To fulfill the low latency requirement, fog computing is a competitive solution by deploying healthcare IoT devices on the edge of clouds. However, these fog devices generate huge amount of sensor data. Designing a specific framework for fog devices to ensure reliable data transmission and rapid data processing becomes a topic of utmost significance. In this paper, a Reduced Variable Neighborhood Search (RVNS)-based sEnsor Data Processing Framework (REDPF) is proposed to enhance reliability of data transmission and processing speed. Functionalities of REDPF include fault-tolerant data transmission, self-adaptive filtering and data-load-reduction processing. Specifically, a reliable transmission mechanism, managed by a self-adaptive filter, will recollect lost or inaccurate data automatically. Then, a new scheme is designed to evaluate the health status of the elderly people. Through extensive simulations, we show that our proposed scheme improves network reliability, and provides a faster processing speed.

The advances of Radio-Frequency Identification (RFID) technology have significantly enhanced the capability of capturing data from pervasive space. It becomes a great challenge in the information era to effectively understand human behavior, mobility and activity through the perceived RFID data. Focusing on RFID data management, this article provides an overview of current challenges, emerging opportunities and recent progresses in RFID. In particular, this article has described and analyzed the research work on three aspects: algorithm, protocol and performance evaluation. We investigate the research progress in RFID with anti-collision algorithms, authentication and privacy protection protocols, localization and activity sensing, as well as performance tuning in realistic settings. We emphasize the basic principles of RFID data management to understand the state-of-the-art and to address directions of future research in RFID.

The human-computer interactions have moved from the conventional approaches of entering inputs into the keyboards/touchpads to the brand-new approaches of performing interactions in the air. In this paper, we propose RF-glove, a system that recognizes concurrent multiple finger micromovement using RF signals, so as to realize the vision of “multi-touch in the air.” It uses a commercial-off-the-shelf (COTS) RFID reader with three antennas and five COTS tags attached to the five fingers of a glove, one tag per finger. During the process of a user performing finger micromovements, we let the RFID reader continuously interrogate these tags and obtain the backscattered RF signals from each tag. For each antenna-tag pair, the reader obtains a sequence of RF phase values called a phase profile from the tag's responses over time. To tradeoff between accuracy and robustness in terms of matching resolution, we propose a two phase approach, including coarse-grained filtering and fine-grained matching. To tackle the variation of template phase profiles at different positions, we propose a phase-model-based solution to reconstruct the template phase profiles based on the exact locations. Experiment results show that we achieve an average accuracy of 92.1% under various moving speeds, orientation deviations, and so on.

Recent research advancement of wireless sensing technology has made device-free interaction in the WiFi-enabled IoT environment possible. Although gesture-based interaction with such a smart environment greatly improves usability, it also introduces many security problems, such as shoulder surfing attacks. By spoofing the gestures of legitimate users, the attacker could easily access private information or services and cause even worse consequences. A secure interaction mechanism for this environment is required to prevent attackers without compromising the usability, while the limited recognition ability and low robustness of WiFi sensing make this target extremely challenging. To this end, we propose a secure interaction mechanism called secure interaction via WiFi Signal (SiWi), which provides the ability to resist shoulder surfing attacks without compromising the usability by using just WiFi signals. SiWi innovates in a concurrent interaction/authentication framework with only three elemental gestures (push, swing, and wave) and four types of identity-related imperceptible/hidden features (time distribution, direction, angle, and distance). HMM and Fresnel model-based algorithms are used to recognize the gestures and extract hidden features robustly and efficiently. Extensive experiments in a real implemented system were conducted to investigate the effectiveness of the proposed secure interaction system. The results show that our system can achieve an average accuracy of 93% to identify legitimate users and 97% to resist the spoofer.

Currently, the logistic industry has introduced 3D reconstruction to monitor the package placement in the warehouse. Previous 3D reconstruction solutions mainly utilize computer vision or sensor-based methods, which are restricted to the line-of-sight or the battery life. Therefore, we propose a passive RFID-based solution, called RF-3DScan, to perform 3D reconstruction on tagged packages, including the package orientation and the package stacking. The basic idea is that a moving antenna can obtain RF-signals from the tags attached on packages with the 1D linear mobile scanning. Through extracting phase differences to build angle profiles for each tag, RF-3DScan derives their relative positions, further determine the package orientation and the coarse-grained package stacking. By simply performing the 2D scanning, RF-3DScan can provide fine-grained package stacking determination. We implement a prototype system of RF-3DScan and evaluate its performance in real settings. Our experiment results show that RF-3DScan can achieve about 92.5% identification accuracy of the bottom face, and average error about 4.08° of the rotation angle. For the package stacking, 1D scanning can achieve the comparable performance in comparison with 2D scanning.