Embedded ethics: a proposal for integrating ethics into the development of medical AI

The emergence of ethical concerns surrounding artificial intelligence (AI) has led to an explosion of high-level ethical principles being published by a wide range of public and private organizations. However, there is a need to consider how AI developers can be practically assisted to anticipate, identify and address ethical issues regarding AI technologies. This is particularly important in the development of AI intended for healthcare settings, where applications will often interact directly with patients in various states of vulnerability. In this paper, we propose that an ‘embedded ethics’ approach, in which ethicists and developers together address ethical issues via an iterative and continuous process from the outset of development, could be an effective means of integrating robust ethical considerations into the practical development of medical AI.

New Method for Surgical Diagnostics - a Robotic Telemedical Approach

Apart from the tremendous increase in the demand for telemedicine during the COVID-19 pandemic, the use of telemedical technology offers many advantages, such as better coverage of rural areas and improved access to specialists. While current telediagnostic possibilities are often limited to a verbal consultation, the field of surgery has already made use of robotics for one of the most challenging areas of medicine: invasive procedures. Since comprehensive diagnostics are a prerequisite for each surgery, we built upon the knowledge gained in telesurgery and developed a telediagnostic system that allows for an extensive perioperative and emergency examination. It is based on a robotic platform consisting of a remote lead robotic arm at the physician's site and a follower robot at the patient's site. Mirroring all movements directly and using force-feedback, both parties can precisely interact, enabling tasks such as auscultation, percussion, and palpation without the need for extensive training. Our overall setup also includes the possibility to measure and monitor all relevant vital parameters and can be used to perform ear and nasopharyngeal inspections as well as an automatic swab to screen for COVID or other contagious diseases prior to hospital admission. In this paper, we focus on the potential of this technology for the surgical community by demonstrating the ease of adding an ultrasound probe to our modular setup to perform a high-quality emergency ultrasound examination. While the system is not yet ready for everyday use in a hospital and drawbacks such as a high cost persist, our setup paves the way for the future use of telediagnostics in surgery.

An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition

Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset-i.e., representing variations in limb position or external loads-to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development.

An Adaptive Mechatronic Exoskeleton for Force-Controlled Finger Rehabilitation

This paper presents a novel mechatronic exoskeleton architecture for finger rehabilitation. The system consists of an underactuated kinematic structure that enables the exoskeleton to act as an adaptive finger stimulator. The exoskeleton has sensors for motion detection and control. The proposed architecture offers three main advantages. First, the exoskeleton enables accurate quantification of subject-specific finger dynamics. The configuration of the exoskeleton can be fully reconstructed using measurements from three angular position sensors placed on the kinematic structure. In addition, the actuation force acting on the exoskeleton is recorded. Thus, the range of motion (ROM) and the force and torque trajectories of each finger joint can be determined. Second, the adaptive kinematic structure allows the patient to perform various functional tasks. The force control of the exoskeleton acts like a safeguard and limits the maximum possible joint torques during finger movement. Last, the system is compact, lightweight and does not require extensive peripherals. Due to its safety features, it is easy to use in the home. Applicability was tested in three healthy subjects.

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions

BACKGROUND

Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization.

CONTRIBUTION

This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging.

SPINE20 A global advocacy group promoting evidence-based spine care of value

PURPOSE

The Global Burden of Diseases (GBD) Studies have estimated that low back pain is one of the costliest ailments worldwide. Subsequent to GBD publications, leadership of the four largest global spine societies agreed to form SPINE20. This article introduces the concept of SPINE20, the recommendations, and the future of this global advocacy group linked to G20 annual summits.

METHODS

The founders of SPINE20 advocacy group coordinated with G20 Saudi Arabia to conduct the SPINE20 summit in 2020. The summit was intended to promote evidence-based recommendations to use the most reliable information from high-level research. Eight areas of importance to mitigate spine disorders were identified through a voting process of the participating societies. Twelve recommendations were discussed and vetted.

RESULTS

The areas of immediate concern were "Aging spine," "Future of spine care," "Spinal cord injuries," "Children and adolescent spine," "Spine-related disability," "Spine Educational Standards," "Patient safety," and "Burden on economy." Twelve recommendations were created and endorsed by 31/33 spine societies and 2 journals globally during a vetted process through the SPINE20.org website and during the virtual inaugural meeting November 10-11, 2020 held from the G20 platform.

CONCLUSIONS

This is the first time that international spine societies have joined to support actions to mitigate the burden of spine disorders across the globe. SPINE20 seeks to change awareness and treatment of spine pain by supporting local projects that implement value-based practices with healthcare policies that are culturally sensitive based on scientific evidence.

Forward and inverse dynamics modeling of human shoulder-arm musculoskeletal system with scapulothoracic constraint

In this work, we extend the modeling techniques of human shoulder-arm musculoskeletal dynamics by 1) proposing an extended model with 12 joint degrees of freedom and 27 muscles (modeled as 42 musculotendinous actuators) that is capable of most physiologically and anatomically possible movements, 2) proposing a forward dynamics model driven by muscle activation, where the scapulothoracic constraint is formulated as an anatomically consistent force field, and 3) applying the state-of-the-art inverse dynamics solution on this model. We experimentally validate it against electromyograms for 10 activities of daily living. This validated shoulder-arm musculoskeletal model may e.g., serve as a reference plant model in studying human motor control or as part of a human simulator in the future.

Simultaneous contact and aerodynamic force estimation (s-CAFE) for aerial robots

In this article, we consider the problem of multirotor flying robots physically interacting with the environment under influence of wind. The results are the first algorithms for simultaneous online estimation of contact and aerodynamic wrenches acting on the robot based on real-world data, without the need for dedicated sensors. For this purpose, we investigated two model-based techniques for discriminating between aerodynamic and interaction forces. The first technique is based on aerodynamic and contact torque models, and uses the external force to estimate wind speed. Contacts are then detected based on the residual between estimated external torque and expected (modeled) aerodynamic torque. Upon detecting contact, wind speed is assumed to change very slowly. From the estimated interaction wrench, we are also able to determine the contact location. This is embedded into a particle filter framework to further improve contact location estimation. The second algorithm uses the propeller aerodynamic power and angular speed as measured by the speed controllers to obtain an estimate of the airspeed. An aerodynamics model is then used to determine the aerodynamic wrench. Both methods rely on accurate aerodynamics models. Therefore, we evaluate data-driven and physics-based models as well as offline system identification for flying robots. For obtaining ground-truth data, we performed autonomous flights in a 3D wind tunnel. Using this data, aerodynamic model selection, parameter identification, and discrimination between aerodynamic and contact forces could be performed. Finally, the developed methods could serve as useful estimators for interaction control schemes with simultaneous compensation of wind disturbances.

Sub-millimetre accurate human hand kinematics: from surface to skeleton

A highly accurate human hand kinematics model and identification are proposed. The model includes the five digits and the palm arc based on mapping function between surface landmarks and estimated joint centres of rotation. Model identification was experimentally performed using a motion tracking system. The evaluation of the marker position estimation error, which is on sub-millimetre level across all digits, underlines model quality and accuracy. Noticeably, with the development of this model, we were able to improve various modelling assumptions from literature and found a basic linear relationship between surface and skeleton rotational angles.