Internet of Things (IoT) devices play essential roles in both industry and daily scenarios. However, unlike smartphones and computers, IoT devices typically lack conventional user interfaces (UIs) such as keyboards and touchscreens. It renders the traditional user authentication designs, e.g., PINs and patterns, inapplicable. In this article, we proposeTouchAccessthat enables users to unlock an arbitrary IoT device by applying a simple touch. Our design is motivated by the key observation that IoT devices unavoidably generate electromagnetic emanations (EMM) while they are functioning. When the user touches the device, it causes time-varying coupling between those two and generates unique EMMs. Our feasibility studies further reveal that thesehuman-induced EMMsare distinct and strongly correlated with the circuitry properties of the user and the device, but are susceptible to environmental EM noises, thus lowering the authentication accuracy. To address this challenge, we develop signal processing techniques with a Siamese network learning scheme that clears the ambient electromagnetic (EM) noises, extracts robust signal features, and builds noise-resistant classifiers, enabling users to be correctly recognized. A significant advantage ofTouchAccessis that it requires only a low-cost analog-to-digital converter (ADC) to sense the EM signal. We implementTouchAccesson commercial off-the-shelf (COTS) IoT devices, which vary significantly in terms of UIs, sizes, and hardware designs. The performance evaluations show thatTouchAccessachieves an average authentication accuracy as high as 97.85%.

The popularity of IoT devices has penetrated our daily life while posing new challenges in user authentication. Today’s solutions, e.g., passwords, fingerprints, and FaceIDs, primarily rely on specialized sensors or user interfaces to collect user’s identification information, which may not universally exist on heterogeneous IoT devices. In this paper, we propose a novel user authentication method that exploits the Electromagnetic (EM) emanations radiated from IoT devices. Our design is motivated by the observation that human touches on the IoT device can lead to time-varying coupling between these two. Consequently, it impacts the device’s EM emanations that can be picked up by its inertial ADC (analog-to-digital converter) interfaces. We ask the user to tap on the device rhythmically following a self-determined melody, such that the human-coupled EM emanations vary accordingly. We thus extract the rhythm pattern as the user’s secure password named EM-Rhythm . To examine its effectiveness, EM-Rhythm is implemented on a wide range of IoT devices. We show that our scheme achieves authentication accuracy as high as 98.67% with less than three login attempts. Besides, it is robust against various types of attacks and maintains stable performances under various settings. EM-Rhythm also exhibits satisfactory usability in terms of memorability and time consumption.

In this paper, in order to combine intelligence of human operator and automatic function of the robot, we design a control scheme for the bimanual robot manipulation, in which the leading robot arm is directly manipulated by a human operator through a haptic device and the following robot arm will automatically adjust its motion to match the operator's motion. In this paper, we propose a fuzzy-based adaptive feedforward compensation controller and apply it into the robot control. According to the comparison results in the simulated experiment, we conclude that the fuzzy-adaptive controller performs better than the non-fuzzy controller, although they can both complete the specified task by tracking the leading robot arm controlled by the human operator. The techniques developed in this paper could be very useful for our future study on adaptation in human-robot interaction in improving the reliability, safety and intelligence.