Idiobionics: The Unification of Privacy and Intelligent Robotic Prostheses
Authors: Kwesi Afari Darfoor, Patrick M. Pilarski, Bailey Kacsmar
Organizations: Department of Computing Science, University of Alberta, Edmonton, Canada · Alberta Machine Intelligence Institute (Amii), Edmonton, Canada · Department of Medicine, University of Alberta, Edmonton, Canada
The human body is at the center of a growing family of technologies designed to tightly and persistently couple biological and digital systems. Robotic prostheses are a representative example of this tight coupling. Also referred to as bionic limbs, robotic prostheses are devices that support people who have lost limbs in pursuing daily life activities such as walking and grasping objects. Bionic limbs are now perceptive and responsive owing to their integration with advanced sensors and artificial intelligence-based control approaches. Consequently, such robotic prostheses can now be viewed as semiautonomous wearable robotic systems that can co-adapt with their users. However, the same sensing and control advancements that increase the capability of robotic prostheses also introduce threat vectors that could be exploited by malicious entities to violate the privacy of users. To fully realize the benefits of next-generation bionic limbs, we maintain it is important to directly understand and address these privacy risks and the barriers they might present to user adoption. This paper therefore introduces a new line of inquiry we term idiobionics to holistically investigate issues at the intersection of privacy and intelligent bionic limbs. As the main contribution of this paper, we define idiobionics, ground it in related literature, and provide preliminary evidence showing and discussing potential adversarial attacks that could exploit intelligent bionic limb designs. We then contribute a curated list of open research questions within idiobionics that are relevant to researchers in wearable robotics and other human-facing autonomous systems. We expect that idiobionics research will help unlock the full potential of robotic prostheses and related bionic devices.
Video recording on smart glasses exposes more than faces. Continuous capture reveals full-body biometric signatures, including gait, posture, and silhouette, that enable person re-identification (ReID) even after conventional face sanitization. We present MIRAGE, a three-tier architecture for privacy-preserving smart glasses that enforces full-body privacy, supports synthetic full-body replacement, and retains encrypted recovery material for consent-based restoration. We implement MIRAGE on a Raspberry Pi~5 (a CPU-only proxy for smart-glasses compute), companion phones, and a cloud generative backend. Compared to prior systems, MIRAGE achieves 0.948 AP and 0.976 AR while accurately detecting the complete visible body. Its bounding box masking reduces learned silhouette-based ReID to essentially random guessing, with 10.86% Rank-1 accuracy compared with an 11.12% measured chance level. Even against an adaptive adversary retrained on MIRAGE's sanitized pose signals, Rank-1 gait identification drops from 90.25% to 26.20%, removing 72.5% of the adversary's identification advantage.
Muhammad Umair, Muhammad Danial Maqbool, Fatima Arshad Cheema +4
Brain-computer interfaces (BCIs) are moving rapidly from laboratory research into clinical, edge, and real-world settings. Under ISO/IEC 8663:2025, a BCI is a direct communication link between central nervous system activity and external software or hardware systems. This link expands privacy risk beyond raw neural-signal leakage: neural data, derived representations, model assets, and decoded outputs can be re-associated with individuals across collection, transmission, storage, training, inference, and feedback, or used to infer information beyond what a task requires. Starting from the general BCI paradigm, this review deffnes privacy-protection boundaries, protection objects, and the relationship between user data privacy and model privacy within a shared risk pathway. It then proposes a three-dimensional framework - protection object, lifecycle stage, and dominant protection-strength level - to classify existing work into four levels of protection strength. Finally, mental privacy and neuroethical risks are treated as open issues, emphasizing that BCI privacy protection should not only obscure data but also disentangle task-irrelevant sensitive information while preserving downstream utility. Keywords: Brain-computer interface, Neural data privacy, User data privacy, Model privacy, Disentanglement of task-irrelevant sensitive information, Protection-strength grading, Neuroethical risks
This paper presents the design and evaluation of a myoelectric tentacle-shaped prosthesis integrating electromyographic (EMG) control, sensorless object detection, and vibrotactile feedback. The objective was to develop a responsive and intuitive assistive device that adapts to various object shapes while providing sensory feedback to the user. The system relies on EMG signals to control the motion of a flexible, biomimetic structure whose curling geometry follows a logarithmic spiral, enabling it to coil around objects. To ensure stable control, the EMG signal is normalized and filtered, and a threshold-based method identifies user intention. Object contact is detected through a slope-based analysis of motor current, eliminating the need for external sensors, and a haptic feedback strategy based on cumulative vibrotactile stimulation conveys spatial information about the tentacle's configuration. The system was evaluated through quantitative and qualitative tests. The results demonstrate a low response time (77 ms on average), enabling smooth real-time interaction; an object-detection success rate above 90%, confirming robustness despite EMG variability; and an effective haptic feedback strategy that allowed users to reliably identify the folding zone of the tentacle. The proposed biomimetic design promotes further investigation of expressive artificial limbs by prioritizing expressive functionality over adherence to a predefined, anthropomorphic form factor.