My(o) Armband Leaks Passwords

Wearables that constantly collect various sensor data of their users increase the chances for inferences of unintentional and sensitive information such as passwords typed on a physical keyboard. We take a thorough look at the potential of using electromyographic (EMG) data, a sensor modality which is new to the market but has lately gained attention in the context of wearables for augmented reality (AR), for a keylogging side-channel attack. Our approach is based on neural networks for a between-subject attack in a realistic scenario using the Myo Armband to collect the sensor data. In our approach, the EMG data has proven to be the most prominent source of information compared to the accelerometer and gyroscope, increasing the keystroke detection performance. For our end-to-end approach on raw data, we report a mean balanced accuracy of about 76 % for the keystroke detection and a mean top-3 key accuracy of about 32 % on 52 classes for the key identification on passwords of varying strengths. We have created an extensive dataset including more than 310 000 keystrokes recorded from 37 volunteers, which is available as open access along with the source code used to create the given results.

An Improvement of Robot Stiffness-Adaptive Skill Primitive Generalization Using the Surface Electromyography in Human–Robot Collaboration

Learning from Demonstration in robotics has proved its efficiency in robot skill learning. The generalization goals of most skill expression models in real scenarios are specified by humans or associated with other perceptual data. Our proposed framework using the Probabilistic Movement Primitives (ProMPs) modeling to resolve the shortcomings of the previous research works; the coupling between stiffness and motion is inherently established in a single model. Such a framework can request a small amount of incomplete observation data to infer the entire skill primitive. It can be used as an intuitive generalization command sending tool to achieve collaboration between humans and robots with human-like stiffness modulation strategies on either side. Experiments (human–robot hand-over, object matching, pick-and-place) were conducted to prove the effectiveness of the work. Myo armband and Leap motion camera are used as surface electromyography (sEMG) signal and motion capture sensors respective in the experiments. Also, the experiments show that the proposed framework strengthened the ability to distinguish actions with similar movements under observation noise by introducing the sEMG signal into the ProMP model. The usage of the mixture model brings possibilities in achieving automation of multiple collaborative tasks.

A Wearable MYO Gesture Armband Controlling Sphero BB-8 Robot

In this paper we present the development and preliminary validation of a wearable system which is combined with an algorithm interfacing the MYO gesture armband with a Sphero BB-8 robotic device. The MYO armband is a wearable device which measures real-time EMG signals of the end user’s forearm muscles as the user is executing a set of upper limb gestures. These gestures are interpreted and transmitted to a computing hardware via a Bluetooth Low Energy IEEE 802.15.1 wireless protocol. The algorithm analyzes and sorts the data and sends a set of commands to the Sphero robotic device while performing navigation movements. After designing and integrating the software and hardware architecture, we have validated the system with two sets of trials involving a series of commands performed in multiple iterations. The consequent reactions of the robots, due to these commands, were recorded and the performance of the system was analyzed in a confusion matrix to obtain an average accuracy of the system outcome vs. the expected and desired actions. Results show that our integrated system can satisfactorily interface with the system in an intuitive way with an accuracy rating of 85.7 % and 92.9 % for the two tests, respectively. Doi: 10.28991/HIJ-2020-01-04-05 Full Text: PDF

Recognition of additional myo armband gestures for myoelectric prosthetic applications

Myoelectric prostheses are a viable solution for people with amputations. The challenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo arm band device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.