Electrical muscle stimulation (EMS) provides direct, felt feedback by actuating muscles, offering a novel haptic channel for extended reality (XR). We present EMSTouch, a system that augments XR scenes through controlled muscle activation for virtual object manipulation and handshake tasks. EMS was evaluated against vibrotactile and visual baselines, focusing on the perception of replayed socially meaningful gestures. In a user study (N=12), EMS feedback yielded higher realism scores than visual only interaction and produced greater naturalness and immersion than vibrotactile feedback. Findings highlight the realism–agency trade-off inherent in EMS-based interaction, showing how more realistic stimulation can influence autonomy. We further explore EMS in creative practice through a case study in XR art, where electromyography (EMG) captures the biodynamics of strokes during virtual sculpture creation and EMS replays the movements. Altogether, EMS emerges as a socially accepted feedback modality that elevates realism and immersion in both virtual interaction and creative expression.

Usability challenges and social acceptance of textual input in a context of extended realities (XR) motivate the research of novel input modalities. We investigate the fusion of inertial measurement unit (IMU) control and surface electromyography (sEMG) gesture recognition applied to text entry using a QWERTY-layout virtual keyboard. We design, implement, and evaluate the proposed multi-modal solution named MyoKey. The user can select characters with a combination of arm movements and hand gestures. MyoKey employs a lightweight convolutional neural network classifier that can be deployed on a mobile device with insignificant inference time. We demonstrate the practicality of interruption-free text entry with MyoKey, by recruiting 12 participants and by testing three sets of grasp micro-gestures in three scenarios: empty hand text input, tripod grasp (e.g., pen), and a cylindrical grasp (e.g., umbrella). With MyoKey, users achieve an average text entry rate of 9.33 words per minute (WPM), 8.76 WPM, and 8.35 WPM for the freehand, tripod grasp, and cylindrical grasp conditions, respectively.

Incremental and quantitative improvements of two-way interactions with e x tended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduce to the area of XR.

Cloud computing and, more particularly, cloud databases, is a great technology for remote centralized data managing.However, there are some drawbacks including privacy issues, insider threats and potential database thefts.Full encryption of remote database does solve the problem, but disables many operations that can be held on DBMS side; therefore problem requires much more complex solution and specific encryptions.In this paper, we propose a solution for secure private data storage that protects confidentiality of user's data, stored in cloud.Solution uses order preserving and homomorphic proprietary developed encryptions.Proposed approach includes analysis of user's SQL queries, encryption of vulnerable data and decryption of data selection, returned from DBMS.We have validated our approach through the implementation of SQL queries and DBMS replies processor, which will be discussed in this paper.Secure cloud database architecture and used encryptions also will be covered.