-- Prosthesis users can regain partial limb functionality, however, full natural limb mobility is rarely restored, often resulting in compensatory movements that lead to discomfort, inefficiency, and long-term physical strain. T o address this issue, we propose a novel human-robot collaboration framework to mitigate compensatory mechanisms in upper-limb prosthesis users by exploiting their residual motion capabilities while respecting task requirements. Our approach introduces a personalised mobility model that quantifies joint-specific functional limitations and the cost of compensatory movements. This model is integrated into a constrained optimisation framework that computes optimal user postures for task performance, balancing functionality and comfort. We validated the framework using a new body-powered prosthetic device for single-finger amputation, which enhances grasping capabilities through synergistic closure with the hand but imposes wrist constraints. Initial experiments with healthy subjects wearing the prosthesis as a supernumerary finger demonstrated that a robotic assistant embedding the user-specific mobility model outperformed human partners in handover tasks, improving both the efficiency of the prosthesis user's grasp and reducing compensatory movements in functioning joints. Prosthetic devices aim to mitigate these issues by restoring functionality and enabling users to regain independence in daily living and work-related activities.

Currently, people with disability or difficulty to move their arms (referred to as "patients") have very limited technological solutions to efficiently address their physiological limitations. It is mainly due to two reasons: (1) the non-invasive solutions like mind-controlled prosthetic devices are typically very costly and require expensive maintenance; and (2) other solutions require costly invasive brain surgery, which is high risk to perform, expensive, and difficult to maintain. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, a mechanized intelligent non-invasive neuro-driven prosthetic arm system. Our MindArm system employs a deep neural network (DNN) engine to translate brain signals into the intended prosthetic arm motion, thereby helping patients to perform many activities despite their physiological limitations. Here, our MindArm system utilizes widely accessible and low-cost surface electroencephalogram (EEG) electrodes coupled with an Open Brain Computer Interface and UDP networking for acquiring brain signals and transmitting them to the compute module for signal processing. In the compute module, we run a trained DNN model to interpret normalized micro-voltage of the brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. The experimental results on a fully working prototype demonstrate that, from the three defined actions, our MindArm system achieves positive success rates, i.e., 91\% for idle/stationary, 85\% for shake hand, and 84\% for pick-up cup. This demonstrates that our MindArm provides a novel approach for an alternate low-cost mind-controlled prosthetic devices for all patients.

This article presents a vision for the future of prosthetic devices, leveraging the advancements in large language models (LLMs) and Large Multimodal Models (LMMs) to revolutionize the interaction between humans and assistive technologies. Unlike traditional prostheses, which rely on limited and predefined commands, this approach aims to develop intelligent prostheses that understand and respond to users' needs through natural language and multimodal inputs. The realization of this vision involves developing a control system capable of understanding and translating a wide array of natural language and multimodal inputs into actionable commands for prosthetic devices. This includes the creation of models that can extract and interpret features from both textual and multimodal data, ensuring devices not only follow user commands but also respond intelligently to the environment and user intent, thus marking a significant leap forward in prosthetic technology.

Our research investigates vibrotactile perception in four prosthetic hands with distinct kinematics and mechanical characteristics. We found that rigid and simple socket-based prosthetic devices can transmit tactile information and surprisingly enable users to identify the stimulated finger with high reliability. This ability decreases with more advanced prosthetic hands with additional articulations and softer mechanics. We conducted experiments to understand the underlying mechanisms. We assessed a prosthetic user's ability to discriminate finger contacts based on vibrations transmitted through the four prosthetic hands. We also performed numerical and mechanical vibration tests on the prostheses and used a machine learning classifier to identify the contacted finger. Our results show that simpler and rigid prosthetic hands facilitate contact discrimination (for instance, a user of a purely cosmetic hand can distinguish a contact on the index finger from other fingers with 83% accuracy), but all tested hands, including soft advanced ones, performed above chance level. Despite advanced hands reducing vibration transmission, a machine learning algorithm still exceeded human performance in discriminating finger contacts. These findings suggest the potential for enhancing vibrotactile feedback in advanced prosthetic hands and lay the groundwork for future integration of such feedback in prosthetic devices.

The use of vibrotactile feedback is of growing interest in the field of prosthetics, but few devices fully integrate this technology in the prosthesis to transmit high-frequency contact information (such as surface roughness and first contact) arising from the interaction of the prosthetic device with external items. This study describes a wearable vibrotactile system for high-frequency tactile information embedded in the prosthetic socket. The device consists of two compact planar vibrotactile actuators in direct contact with the user's skin to transmit tactile cues. These stimuli are directly related to the acceleration profiles recorded with two IMUS placed on the distal phalanx of a soft under-actuated robotic prosthesis (SoftHand Pro). We characterized the system from a psychophysical point of view with fifteen able-bodied participants by computing participants' Just Noticeable Difference (JND) related to the discrimination of vibrotactile cues delivered on the index finger, which are associated with the exploration of different sandpapers. Moreover, we performed a pilot experiment with one SoftHand Pro prosthesis user by designing a task, i.e. Active Texture Identification, to investigate if our feedback could enhance users' roughness discrimination. Results indicate that the device can effectively convey contact and texture cues, which users can readily detect and distinguish.

Fox News medical contributor Dr. Marc Siegel joins'Fox & Friends' to discuss the benefits of artificial intelligence in the medical industry if used with caution. Artificial intelligence systems are modeled after the human brain, but a new branch of research at Columbia University in New York is examining whether developments in AI might contain clues as to how living brains work and how their function might be improved. Columbia was one of seven universities that the National Science Foundation designated as a new national AI research institute, and the $20 million it received will boost the school's AI Institute for Artificial and Natural Intelligence (ARNI). The goal is to conduct research "connecting the major progress made in AI systems to the revolution in our understanding of the brain." Richard Zemel, professor of computer science at Columbia, told Fox News Digital that the ambition is to bring together top AI and neuroscience researchers together in a cross-training exercise that can benefit AI systems and people.

Assistive robotic devices are a particularly promising field of application for neural networks (NN) due to the need for personalization and hard-to-model human-machine interaction dynamics. However, NN based estimators and controllers may produce potentially unsafe outputs over previously unseen data points. In this paper, we introduce an algorithm for updating NN control policies to satisfy a given set of formal safety constraints, while also optimizing the original loss function. Given a set of mixed-integer linear constraints, we define the NN repair problem as a Mixed Integer Quadratic Program (MIQP). In extensive experiments, we demonstrate the efficacy of our repair method in generating safe policies for a lower-leg prosthesis.

As Verne understood, the U.S. Civil War (during which 60,000 amputations were performed) inaugurated the modern prosthetics era in the United States, thanks to federal funding and a wave of design patents filed by entrepreneurial prosthetists. The two World Wars solidified the for-profit prosthetics industry in both the United States and Western Europe, and the ongoing War on Terror helped catapult it into a US $6 billion dollar industry across the globe. This recent investment is not, however, a result of a disproportionately large number of amputations in military conflict: Around 1,500 U.S. soldiers and 300 British soldiers lost limbs in Iraq and Afghanistan. Limb loss in the general population dwarfs those figures. A much smaller subset--between 1,500 to 4,500 children each year--are born with limb differences or absences, myself included.

My left arm extends all the way up to and just barely past my elbow, tapering into a small, fleshy stump. For prosthetists, I've always been a weird fit--that funny little kid in the office with my arm held out like a bird with a broken wing, waiting for the plaster mold to dry. Since I do not have a forearm, a prosthesis socket must fit over my elbow to stay on, but the socket necessarily limits the range of motion and makes it harder to prevent falling off during a full day of bending and extending. My most recent prosthetist had devised their own patented method of molding a socket that better accommodates bodies like mine. What I didn't realize was how else they have applied this knowledge, before I even became their patient.

Andrew Rubin sits with a Surface tablet, watching a white skeletal hand open and close on its screen. Rubin's right hand was amputated a year ago, but he follows these motions with a special device fitted to his upper arm. Electrodes on his arm connect to a box that records the patterns of nerve signals firing, allowing Rubin to train a prosthetic limb to act like a real hand. "When I think of closing a hand, it's going to contract certain muscles in my forearm," he says. "The software recognizes the patterns created when I flex or extend a hand that I do not have."