Existing human mesh recovery methods assume a standard human body structure, overlooking diverse anatomical conditions such as limb loss. This assumption introduces bias when applied to individuals with amputations--a limitation further exacerbated by the scarcity of suitable datasets. T o address this gap, we propose Amputated Joint Aware 3D Human Mesh Recovery (AJAHR), which is an adaptive pose estimation framework that improves mesh reconstruction for individuals with limb loss. Our model integrates a body-part amputation classifier, jointly trained with the mesh recovery network, to detect potential amputations. W e also introduce Amputee 3D (A3D), which is a synthetic dataset offering a wide range of amputee poses for robust training. While maintaining competitive performance on non-amputees, our approach achieves state-of-the-art results for amputated individuals. Additional materials can be found at: https://chojinie.github.

Our brain may not be as capable of rewiring following an amputation as we thought, which could have serious implications for how we treat a common complication called phantom limb pain. A part of the brain called the somatosensory cortex receives and processes sensory information across the body, such as touch and temperature. Some studies suggest the areas of the cortex are mapped to different parts of the body, so a different area will light up if you burn your hand versus your toe, for instance. It has also been suggested that the somatosensory cortex reorganises itself in the case of an amputation or severed nerve. For example, in a study of macaques whose arm nerves had been severed, neurons in the somatosensory cortex that normally respond to stimulation of the hand were instead activated by touching the face.

When the air ambulance brought Jim Ashworth-Beaumont to King's College hospital in south-east London, nobody thought he had a hope. He had been cycling home when a lorry driver failed to spot him alongside his trailer while turning left after a set of traffic lights. The vehicle's wheels opened his torso like a sardine tin, puncturing his lungs and splitting his liver in two. They also tore off his right arm. Weeks after the accident, in July 2020, Ashworth-Beaumont would see a photo of the severed limb taken by a doctor while it lay beside him in hospital. He had asked to see the picture and says it helped him come to terms with his loss. "My hand didn't look too bad," he says. "It was as if it was waving goodbye to me." Ashworth-Beaumont, a super-fit and sunny former Royal Marine from Edinburgh, would go on to spend six weeks in an induced coma as surgeons raced to repair his crushed body. But as he lay on the road, waiting for the paramedics, his only thoughts were that he was dying.

The quality of a transtibial prosthetic socket depends on the prosthetist's skills and expertise, as the fitting is performed manually. This study investigates multiple artificial intelligence (AI) approaches to help standardize transtibial prosthetic socket design. Data from 118 patients were collected by prosthetists working in the Dutch healthcare system. This data consists of a three-dimensional (3D) scan of the residual limb and a corresponding 3D model of the prosthetist-designed socket. Multiple data pre-processing steps are performed for alignment, standardization and optionally compression using Morphable Models and Principal Component Analysis. Afterward, three different algorithms - a 3D neural network, Feedforward neural network, and random forest - are developed to either predict 1) the final socket shape or 2) the adaptations performed by a prosthetist to predict the socket shape based on the 3D scan of the residual limb. Each algorithm's performance was evaluated by comparing the prosthetist-designed socket with the AI-generated socket, using two metrics in combination with the error location. First, we measure the surface-to-surface distance to assess the overall surface error between the AI-generated socket and the prosthetist-designed socket. Second, distance maps between the AI-generated and prosthetist sockets are utilized to analyze the error's location. For all algorithms, estimating the required adaptations outperformed direct prediction of the final socket shape. The random forest model applied to adaptation prediction yields the lowest error with a median surface-to-surface distance of 1.24 millimeters, a first quartile of 1.03 millimeters, and a third quartile of 1.54 millimeters.

Madeline Gardner is the youngest American person to get a Hero Arm, the world's first multi-grip bionic arm for children. Kicking a ball or climbing stairs with ease after a leg amputation above the knee is now within reach. Researchers at MIT have developed a bionic knee that redefines mobility for above-the-knee amputees. The result is faster, smoother, and more natural movement. Led by Professor Hugh Herr, the MIT team created a solution that doesn't just mimic motion, it responds to intent.

-- Partial hand amputations significantly affect the physical and psychosocial well-being of individuals, yet intuitive control of externally powered prostheses remains an open challenge. T o address this gap, we developed a force-controlled prosthetic finger activated by electromyography (EMG) signals. The prototype, constructed around a wrist brace, functions as a supernumerary finger placed near the index, allowing for early-stage evaluation on unimpaired subjects. A neural network-based model was then implemented to estimate fingertip forces from EMG inputs, allowing for online adjustment of the prosthetic finger grip strength. The force estimation model was validated through experiments with ten participants, demonstrating its effectiveness in predicting forces. Additionally, online trials with four users wearing the prosthesis exhibited precise control over the device. Our findings highlight the potential of using EMG-based force estimation to enhance the functionality of prosthetic fingers. I. INTRODUCTION Upper extremity amputations make up 3% to 23% of all amputations, with approximately 50% to 90% of these being related to trauma.

We present a system for decoding hand movements using surface EMG signals. The interface provides real-time (25 Hz) reconstruction of finger joint angles across 20 degrees of freedom, designed for upper limb amputees. Our offline analysis shows 0.8 correlation between predicted and actual hand movements. The system functions as an integrated pipeline with three key components: (1) a VR-based data collection platform, (2) a transformer-based model for EMG-to-motion transformation, and (3) a real-time calibration and feedback module called ALVI Interface. Using eight sEMG sensors and a VR training environment, users can control their virtual hand down to finger joint movement precision, as demonstrated in our video: youtube link.

Getting the neural interface hooked up to a prosthetic takes two steps. First is surgery involving the portions of muscle that remain after a lower-leg amputation. The operation reconnects shin muscle, which contracts to make the ankle flex upward, to calf muscle, which counteracts this movement. The prosthetic can also be fitted at this point. In addition to enabling the prosthetic to move more dynamically, the procedure can reduce phantom-limb pain, and patients are less likely to trip and fall.

Upper-limb amputees face tremendous difficulty in operating dexterous powered prostheses. Previous work has shown that aspects of prosthetic hand, wrist, or elbow control can be improved through "intelligent" control, by combining movement-based or gaze-based intent estimation with low-level robotic autonomy. However, no such solutions exist for whole-arm control. Moreover, hardware platforms for advanced prosthetic control are expensive, and existing simulation platforms are not well-designed for integration with robotics software frameworks. We present the Prosthetic Arm Control Testbed (ProACT), a platform for evaluating intelligent control methods for prosthetic arms in an immersive (Augmented Reality) simulation setting. Using ProACT with non-amputee participants, we compare performance in a Box-and-Blocks Task using a virtual myoelectric prosthetic arm, with and without intent estimation. Our results show that methods using intent estimation improve both user satisfaction and the degree of success in the task. To the best of our knowledge, this constitutes the first study of semi-autonomous control for complex whole-arm prostheses, the first study including sequential task modeling in the context of wearable prosthetic arms, and the first testbed of its kind. Towards the goal of supporting future research in intelligent prosthetics, the system is built upon on existing open-source frameworks for robotics.

A man who had his right arm amputated below the elbow has been able to feel hot and cold in his missing hand via a modified prosthetic arm with thermal sensors. After an amputation, some people can still perceive touch and pain sensations in their missing arm or leg, known as a phantom limb. Sometimes, these sensations can be triggered by nerve endings in the residual upper limb. The prosthetic works by applying heat or cold to the skin on the upper arm in specific locations that trigger a thermal sensation in the phantom hand. "In a previous study, we have shown the existence of these spots in the majority of amputee patients that we have treated," says Solaiman Shokur at the Swiss Federal Institute of Technology in Lausanne.