Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized controllers from user data. While promising, they often suffer from distribution shift during long-term usage, requiring costly model re-training. Moreover, most prosthetic sEMG sensors have low spatial density, which limits accuracy and the number of controllable motions. In this work, we address both challenges by introducing a novel myoelectric prosthetic system integrating a high density-sEMG (HD-sEMG) setup and incremental learning methods to accurately control 7 motions of the Hannes prosthesis. First, we present a newly designed, compact HD-sEMG interface equipped with 64 dry electrodes positioned over the forearm. Then, we introduce an efficient incremental learning system enabling model adaptation on a stream of data. We thoroughly analyze multiple learning algorithms across 7 subjects, including one with limb absence, and 6 sessions held in different days covering an extended period of several months. The size and time span of the collected data represent a relevant contribution for studying long-term myocontrol performance. Therefore, we release the DELTA dataset together with our experimental code.

Three children are feeling pure joy this December about "the best Christmas present in the world." Ettie Baker, age 8, Zoey Hampton-Pigeon, age 8, and Finn Jarvis, age 11, were all given "life-changing" bionic arms this week courtesy of The Big Hero 3 campaign. Launched by a mom named Sarah Lockey whose own daughter faced physical challenges, the campaign helps families of children with missing limbs fundraise for bionic arms, news agency SWNS reported. Ettie Baker's mother, Alyse, said her daughter "screamed" when she found out about her new arm for Christmas this year. "Ettie has always shown so much love for her difference and loves celebrating differences," said mom Alyse Baker about her daughter.

Myoelectric prosthetic hands are typically controlled to move between discrete positions and do not provide sensory feedback to the user. In this work, we present and evaluate a closed-loop, continuous myoelectric prosthetic hand controller, that can continuously control the position of multiple degrees of freedom of a prosthesis while rendering proprioceptive feedback to the user via a haptic feedback armband. Twenty-eight participants without and ten participants with limb difference were recruited to holistically evaluate the physical and psychological effects of the controller via isolated control and sensory tasks, dexterity assessments, embodiment and task load questionnaires, and post-study interviews. The combination of proprioceptive feedback and continuous control enabled accurate positioning, to within 10% mean absolute motor position error, and grasp-force modulation, to within 20% mean absolute motor force error, and restored blindfolded object identification ability to open-loop discrete controller levels. Dexterity assessment and embodiment questionnaire results revealed no significant physical performance or psychological embodiment differences between control types, with the exception of perceived sensation, which was significantly higher (p < 0.001) for closed-loop controllers. Key differences between participants with and without upper limb difference were identified, including in perceived body completeness and frustration, which can inform future prosthesis development and rehabilitation.

An IoT-enabled system for transporting dairy products earned its designers the top prize in a competition run by Cisco. The Global Problem Solver Challenge, which is one of Cisco's corporate social responsibility (CSR) initiatives, pays cash awards to entrepreneurial companies using technology to solve the world's biggest challenges. The program also gives out four $25,000 awards and seven $10,000 prizes. This year, I was honored to be invited to help judge the 2020 winners. In full disclosure, I agreed to be a judge but I received no compensation, as I believe we all have to work together to make the world a better place.

An IoT-enabled system for transporting dairy products earned its designers the top prize in a competition run by Cisco. The Global Problem Solver Challenge, which is one of Cisco's corporate social responsibility (CSR) initiatives, pays cash awards to entrepreneurial companies using technology to solve the world's biggest challenges. The program also gives out four $25,000 awards and seven $10,000 prizes. This year, I was honored to be invited to help judge the 2020 winners. In full disclosure, I agreed to be a judge but I received no compensation, as I believe we all have to work together to make the world a better place.

Superhero Boost is a weeklong program committed to helping kids reframe a limb difference as an opportunity to create cool prosthetics and other body mods. Sponsored by Google, Autodesk, Born Just Right, and KIDmob, the program is open to kids age 11–17 who have upper-limb differences or who use wheelchairs. The workshop introduces kids to new technologies such as 3D printing, robotics, and artificial intelligence, which the kids use to create their own personal wearable devices designed to release their own inner superheroes. Watch this inspiring video to see what the kids came up with this year.