Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis

Osteoarthritis of the knee, a widespread cause of knee disability, is commonly treated in orthopedics due to its rising prevalence. Lower extremity misalignment, pivotal in knee injury etiology and management, necessitates comprehensive mechanical alignment evaluation via frequently-requested weight-bearing long leg radiographs (LLR). Despite LLR's routine use, current analysis techniques are error-prone and time-consuming. To address this, we conducted a multicentric study to develop and validate a deep learning (DL) model for fully automated leg alignment assessment on anterior-posterior LLR, targeting enhanced reliability and efficiency. The DL model, developed using 594 patients' LLR and a 60%/10%/30% data split for training, validation, and testing, executed alignment analyses via a multi-step process, employing a detection network and nine specialized networks. It was designed to assess all vital anatomical and mechanical parameters for standard clinical leg deformity analysis and preoperative planning. Accuracy, reliability, and assessment duration were compared with three specialized orthopedic surgeons across two distinct institutional datasets (136 and 143 radiographs). The algorithm exhibited equivalent performance to the surgeons in terms of alignment accuracy (DL: 0.21 ± 0.18°to 1.06 ± 1.3°vs. OS: 0.21 ± 0.16°to 1.72 ± 1.96°), interrater reliability (ICC DL: 0.90 ± 0.05 to 1.0 ± 0.0 vs. ICC OS: 0.90 ± 0.03 to 1.0 ± 0.0), and clinically acceptable accuracy (DL: 53.9%-100% vs OS 30.8%-100%). Further, automated analysis significantly reduced analysis time compared to manual annotation (DL: 22 ± 0.6 s vs. OS; 101.7 ± 7 s, p ≤ 0.01). By demonstrating that our algorithm not only matches the precision of expert surgeons but also significantly outpaces them in both speed and consistency of measurements, our research underscores a pivotal advancement in harnessing AI to enhance clinical efficiency and decision-making in orthopaedics.

Autonomous swab robot for naso- and oropharyngeal COVID-19 screening

The COVID-19 outbreak has triggered a global health and economic crisis, necessitating widespread testing to control viral spread amidst rising cases and fatalities. The recommended testing method, a combined naso- and oropharyngeal swab, poses risks and demands limited protective gear. In response to the COVID-19 pandemic, we developed and tested the first autonomous swab robot station for Naso- and Oropharyngeal Coronavirus Screening (SR-NOCS). A force-sensitive robot running under a Cartesian impedance controller is employed to drive the swab to the sampling area. This groundbreaking device underwent two clinical studies-one conducted during the initial pandemic lockdown in Europe (early 2021) and the other, more recently, in a public place after the pandemic had subsided earlier in the year 2023. In total, 52 patients suspected of COVID-19 infection were included in these clinical studies. The results revealed a complete positive correlation between autonomous and manual sampling. The test subjects exhibited a high acceptance rate, all expressing a willingness to undergo future tests with SR-NOCS. Based on our findings, such systems could enhance testing capabilities, potentially conducting up to 300 tests per robot per day with consistent precision. The tests can be carried out with minimal supervision, reducing infection risks and effectively safeguarding patients and healthcare workers.

Toward telemedical diagnostics-clinical evaluation of a robotic examination system for emergency patients

Introduction

The SARS-CoV-2 pandemic has affected global public healthcare for several years. Numerous medical professionals have been infected since the outbreak in 2019, resulting in a shortage of healthcare providers. Since traditional personal protective wear was insufficient to eliminate the virus transmission reliably, new strategies to avoid cross-infection were imperative while enabling high-quality medical care. In the project ProteCT, we investigated the potential of robotic-assisted examination in providing medical examination via a telemedical approach.

Material and Methods

We constructed a fully functional examination cabin equipped with cameras, microphones, screens and robotic arms to evaluate usability and perception. Therefore, we conducted a preliminary study with 10 healthy volunteers and 10 physicians to gain first insights and optimize the setup. In a second step, we performed telemedical examinations of actual patients from the local emergency department to compare the robotic approach with the classical method of measuring vital signs, auscultation, palpation and percussion.

Results

The preliminary study identified basic requirements, such as the need for force-feedback and telemedical training for physicians. In the main study, acceptance was high and most patients indicated they would use a telemedical system again. Our setup enabled the physician to make the same diagnoses as by classic examination in the emergency department in most cases.

Discussion

The potential acceptance of a telemedical system such as ProteCT is high. Robotic telemedical approaches could complement future healthcare beyond the Corona pandemic to reach rural areas or even war zones. Moreover, the daily clinical use of robotic telemedicine could improve patients' safety, the quality of perioperative management and the workflow in any medical facility.

Conclusion

The development of telemedical and telerobotic systems is a multidisciplinary and complex challenge. However, acceptance of the proposed system was high among patients and physicians, indicating the potential use of similar systems for future healthcare.

Monitoring Active Patient Participation During Robotic Rehabilitation: Comparison Between a Robot-Based Metric and an EMG-Based Metric

While rehabilitation robots present a much-needed solution to improving early mobilization therapy in demanding clinical settings, they also present new challenges and opportunities in patient monitoring. Aside from the fundamental challenge of quantifying a patient's voluntary contribution during robot-led therapy motion, many sensors cannot be used in clinical settings due to time and space limitations. In this paper, we present and compare two metrics for monitoring a patient's active participation in the motion. The two metrics, each derived from first principles, have the same biomechanical interpretability, i.e., active work by the patient during the robotic mobilization therapy, but are calculated in two different spaces (Cartesian vs. muscle space). Furthermore, the sensors used to quantify these two metrics are fully independent from each other and the associated measurements are unrelated. Specifically, the robot-based work metric utilizes robot-integrated force sensors, while the EMG-based work metric requires electrophysiological sensors. We then apply the two metrics to therapy performed using a clinically certified, commercially available robotic system and compare them against the specific instructions given to the healthy subjects as well as against each other. Both metric outputs qualitatively match the expected behavior of the healthy subjects. Additionally, strong correlations (median [Formula: see text]) are shown between the two metrics, not only for healthy subjects (n = 12) but also for patients (n = 2), providing solid evidence for their validity and translatability. Importantly, the robot-based work metric does not rely on any sensors outside of those integrated into the robot, thus making it ideal for application in clinical settings.

Model-Based Shared Control of a Hybrid FES-Exoskeleton: An Application in Participant-Specific Robotic Rehabilitation

Hybrid exoskeleton, comprising an exoskeleton interfaced with functional electrical stimulation (FES) technique, is conceptualized to complement the weakness of each other in automated neuro-rehabilitation of sensory-motor deficits. The externally actuating exoskeleton cannot directly influence neurophysiology of the patients, while FES is difficult to use in functional or goal-oriented tasks. The latter challenge is largely inherited from the fact that the dynamics of the muscular response to FES is complex, and it is highly user- and state-dependent. Due to the retardation of the muscular contraction response to the FES profile, furthermore, a commonly used model-free control scheme, such as PID control, suffers performance. The challenge in FES control is exacerbated especially in the presence of the actuation redundancy between the volitional activity of the user, powered exoskeleton, and FES-induced muscle contractions. This study therefore presents trajectory tracking performance of the hybrid exoskeleton in a novel model-based hybrid exoskeleton scheme which entices user-specific FES model-predictive control.

Individualized Training of Back Muscles Using Iterative Learning Control of a Compliant Balance Board

Here we present the GyroTrainer, a bespoke mechatronic balance board system designed to trigger activation of the back muscles while the user engages in a balance-challenging game. The GyroTrainer uses admittance control coupled with an iterative learning approach so as to tailor the admittance control parameters, i.e. difficulty level, according to the user's skill. Our experimental evaluation demonstrated that an individualized admittance control stiffness could be identified for each user, which corresponds with a desired level of difficulty and increased back muscle activity. A first game implementation demonstrates the feasibility of utilizing the GyroTrainer system and the individually identified admittance control stiffness for gamification of back muscle training.

Assessing Human-Human Kinematics for the Implementation of Robot-Assisted Physical Therapy in Humanoids: A Pilot Study

The development of humanoids with bimanual manipulator arms may facilitate assistive robots to perform physical therapy with older adults living at home. As we assume the human-human interaction to be the gold standard of physical therapy, we propose a kinematics analysis to derive guidelines for implementing physical therapy assisted by humanoids. Therefore, a pilot study was carried out involving three physical therapists and two participants acting as exemplary patients. The study analyzes the therapists' movement strategy, including the position and orientation of the therapists' bodies in relation to the participants and the placement of the therapists' hands on the upper limb segment of the participants, as well as the inter- and intravariability during the performance of a ROM (range of motion) assessment. The results demonstrate that while physical therapists exhibit variation in their interaction strategies, they still achieve a consistently low level of variability in their manipulation space.

Estimating Joint Kinematics and Muscles Forces During Robotic Rehabilitation to Detect and Counteract Reduced Ankle Mobility

The paper presents a solution to detect active ankle joint movement while a patient undergoes therapy with a robotic lower limb rehabilitation device that neither restricts nor actively supports ankle dorsi- or plantarflexion. The presented method requires the addition of only two accelerometer sensors to the system as well as a musculoskeletal model of the lower limb. Using forward kinematics and inverse dynamics, it enables knee and ankle joint kinematic tracking in the sagittal plane and muscle force estimation. This is an extension of a previous work in which only hip joint tracking was possible and, thus, muscle force estimation was limited. The correlation results of the current validation study with 12 healthy subjects show high correlation (R=0.88±0.09) between the kinematics estimated with the proposed method and those calculated from a gold standard motion capture setup for all three joints (hip, knee, and ankle). The correlation results of the estimated m. tibialis anterior muscle force against electromyography measurements (R = 0.62±0.27) are promising and a first application to a patient data set shows potential for future clinical application.

Validation of a Robotic Testbench for Evaluating Biomechanical Effects of Implant Rotation in Total Knee Arthroplasty on a Cadaveric Specimen

In this study, we developed and validated a robotic testbench to investigate the biomechanical compatibility of three total knee arthroplasty (TKA) configurations under different loading conditions, including varus-valgus and internal-external loading across defined flexion angles. The testbench captured force-torque data, position, and quaternion information of the knee joint. A cadaver study was conducted, encompassing a native knee joint assessment and successive TKA testing, featuring femoral component rotations at -5°, 0°, and +5° relative to the transepicondylar axis of the femur. The native knee showed enhanced stability in varus-valgus loading, with the +5° external rotation TKA displaying the smallest deviation, indicating biomechanical compatibility. The robotic testbench consistently demonstrated high precision across all loading conditions. The findings demonstrated that the TKA configuration with a +5° external rotation displayed the minimal mean deviation under internal-external loading, indicating superior joint stability. These results contribute meaningful understanding regarding the influence of different TKA configurations on knee joint biomechanics, potentially influencing surgical planning and implant positioning. We are making the collected dataset available for further biomechanical model development and plan to explore the 6 Degrees of Freedom (DOF) robotic platform for additional biomechanical analysis. This study highlights the versatility and usefulness of the robotic testbench as an instrumental tool for expanding our understanding of knee joint biomechanics.

Interproximal tooth cleaning operated by a tactile robot. An in vitro analysis

AIM

New technologies such as tactile robots and artificial intelligence are about to find their way into clinical practice in dentistry and may contribute to the improvement of oral health care in the future. In this study we hypothesized that a collaborative, tactile robot programmed by a dental student removes interproximal artificial plaque as effectively as a human operator in an in vitro pilot trial.

MATERIAL AND METHODS

Model teeth were fully covered with artificial plaque and set into phantom jaws. First, a robot was programmed by a dental student to perform interproximal cleaning with an interproximal brush. Second, teeth were covered with artificial plaque again and the dental student performed the interproximal cleaning manually. Both experiments were repeated five times. Residual plaque was measured with binary pictures. Surface coverage was reported and comparison of methods was performed with significance defined at a= 0.05.

RESULTS

No statistically significant difference was found in the cleaning result between the robot and the human operator.

CONCLUSION

The results of this in vitro pilot study indicate that a tactile robot with integrated artificial intelligence programmed by a dental student can perform interproximal cleaning as effectively as the dental student. Practical lmplications: In the future, the use of robot assistants to support oral hygiene, e.g., in patients with reduced motor skills or impaired vision may be further investigated.

From Human Hand to Grasp Surface Detection, Tracking & Analysis

Hands are paramount for dexterous interactions that humans exhibit in daily life. Understanding the intricacies of human hand-object interactions is therefore necessary. Unfortunately, the limitations of state-of-the-art technologies make capturing the full hand-object complexity unfeasible, giving rise to the need for new technological means to achieve this aim. In this work, we propose an end-to-end framework in which individualized hand models are derived and used to capture quantitative personalized hand-object interaction information, precisely, hand shape, kinematics, and contact surfaces. The results of this study serve as a proof of concept that such a framework can significantly deepen personalized hand-object interaction analyses, providing, in perspective, insights for medical diagnoses and rehabilitation, among others.Clinical relevance- Our work showcases the need to incorporate bespoke human hand models in individualized hand function assessment technologies, as hand-object interaction information is subject-dependent.

Robust Independent Component Analysis based EMG decomposition - a comparison study

High density surface Electromyography (HD-sEMG) provides a high fidelity measurement of the myoelectric activity that can be leveraged by EMG decomposition methods to estimate the motor neuron discharges. Independent Component Analysis (ICA) methods are used as basis for many EMG decomposition algorithms, for the estimation of motor unit action potential signals. Accurate source separation is a non-trivial task in EMG decomposition. While FastICA is widely used for this purpose, other methods with attractive characteristics, such as RobustICA, remain relatively unexplored. The purpose of the current work is to compare three different ICA-based EMG decomposition methods (FastICA, RobustICA and RobustICALCH) in terms of decomposition accuracy and computation time. The evaluation was performed on simulated data using a decomposition algorithm inspired by previous studies. Our results demonstrate that RobustICA outperforms the other methods in terms of number of correctly identified motor units, high decomposition accuracy, and low computation time, across different muscle contraction levels.

Real-Time-Capable Muscle Force Estimation for Monitoring Robotic Rehabilitation Therapy in the Intensive Care Unit

In this paper, a method is proposed to enable real-time monitoring of muscle forces during robotic rehabilitation therapy in the ICU. This method is solely based on sensor information provided by the rehabilitation robot. In current clinical practice, monitoring primarily takes place in the later stages of rehabilitation, but it would also be highly beneficial during early stages. Musculoskeletal models have large, mostly unrealized potential to support and improve patient monitoring. The method presented in this paper is based on a state-of-the-art muscle-tendon path model, which is applied to the use case of the robotic rehabilitation device VEMOTION. The muscle force estimation is validated against surface electromyography measurements of lower limb muscles from 12 healthy volunteers The results show an overall correlation of R = 0.70 0.25 for the single-joint muscle m. iliopsoas, which has a ±major contribution to hip flexion. Given this correlation, the proposed model could be used for real-time monitoring of active patient participation.

Error-related Potentials in a Virtual Pick-and-Place Experiment: Toward Real-world Shared-control

In Human-Robot Collaboration setting a robot may be controlled by a user directly or through a Brain-Computer Interface that detects user intention, and it may act as an autonomous agent. As such interaction increases in complexity, conflicts become inevitable. Goal conflicts can arise from different sources, for instance, interface mistakes - related to misinterpretation of human's intention - or errors of the autonomous system to address task and human's expectations. Such conflicts evoke different spontaneous responses in the human's brain, which could be used to regulate intrinsic task parameters and to improve system response to errors - leading to improved transparency, performance, and safety. To study the possibility of detecting interface and agent errors, we designed a virtual pick and place task with sequential human and robot responsibility and recorded the electroencephalography (EEG) activity of six participants. In the virtual environment, the robot received a command from the participants through a computer keyboard or it moved as autonomous agent. In both cases, artificial errors were defined to occur in 20% - 25% of the trials. We found differences in the responses to interface and agent errors. From the EEG data, correct trials, interface errors, and agent errors were truly predicted for 51.62% ± 9.99% (chance level 38.21%) of the pick movements and 46.84%±6.62% (chance level 36.99%) for the place movements in a pseudo-asynchronous fashion. Our study suggests that in a human-robot collaboration setting one may improve the future performance of a system with intention detection and autonomous modes. Specific examples could be Neural Interfaces that replace and restore motor functions.

Dentronics: Tooth cleaning with a tactile, collaborative robot. An in vitro proof of concept

AIM

The aim was to compare the performance of a collaborative tactile robot programmed by a dental professional (DP) with the performance of a DP in removal of surrogate plaque In vitro.

MATERIALS AND METHODS

Six teeth of typodonts in articulated jaws were covered with surrogate plaque and cleaned by a DP with help of a manual toothbrush (DP/manual) and an electric toothbrush (DP/electric). The experiment was repeated with the help of a collaborative seven-axis tactile robot programmed by a DP handling a manual toothbrush (robot/manual) and an electric toothbrush (robot/electric). All experiment were repeated five times resulting in a total of N= 30 teeth in each group. Cleaning results were reported as the percentage of surface area with residual plaque.

RESULTS

The cleaning results of the robot and the DP showed no significant differences. However, electric toothbrushing was significantly less effective compared to manual toothbrushing (p<0.05).

CONCLUSION

This In vitro study indicates that current robot technology may perform removal of surrogate plaque as efficient as a DP. In future this may be helpful to release nursing staff from this time-demanding and possibly contagious task or support humans with reduced motor skills or impaired vision in performing daily oral hygiene.

Acceptance of Remote Assistive Robots with and without Human-in-the-Loop for Healthcare Applications

Assistive social robots aim to facilitate outpatient-care including required safety critical measures. Accepting a robot to perform such measures, e.g., operate in close physical interaction for medical examinations, requires human trust towards the robot. Human-in-the-loop (HIL) applications where the robot is teleoperated by a human expert can help the person to accept even risky tasks performed by a robot. Therefore, the assistive humanoid GARMI was designed to enable HIL applications with varying autonomy. In this study, we use GARMI to understand which tasks in the framework of care may be accepted depending on human socio-demographics and user beliefs as well as the level of robot autonomy. Firstly, we seek to understand the general acceptance of GARMI using the Almere questionnaire. Secondly, we ask adults to rate their willingness to use several functionalities of GARMI. Lastly, we investigate the effect of the introduction method of GARMI on user acceptance. We assemble all relevant factors on acceptance to provide direction in the user-centered design process of assistive robots. The results of 166 participants show that alongside others, trust towards the robot and utilitarian variables such as perceived usefulness are the most influencing factors on the acceptance of GARMI and should be considered for the design of robotic semi-autonomous outpatient-services.

Tactile Robotic Telemedicine for Safe Remote Diagnostics in Times of Corona: System Design, Feasibility and Usability Study

The current crisis surrounding the COVID-19 pandemic demonstrates the amount of responsibility and the workload on our healthcare system and, above all, on the medical staff around the world. In this work, we propose a promising approach to overcome this problem using robot-assisted telediagnostics, which allows medical experts to examine patients from distance. The designed telediagnostic system consists of two robotic arms. Each robot is located at the doctor and patient sites. Such a system enables the doctor to have a direct conversation via telepresence and to examine patients through robot-assisted inspection (guided tactile and audiovisual contact). The proposed bilateral teleoperation system is redundant in terms of teleoperation control algorithms and visual feedback. Specifically, we implemented two main control modes: joint-based and displacement-based teleoperation. The joint-based mode was implemented due to its high transparency and ease of mapping between Leader and Follower whereas the displacement-based is highly flexible in terms of relative pose mapping and null-space control. Tracking tests between Leader and Follower were conducted on our system using both wired and wireless connections. Moreover, our system was tested by seven medical doctors in two experiments. User studies demonstrated the system's usability and it was successfully validated by the medical experts.

Integration of robotic in the reprocessing and transfer of endoscopes

Background and study aims  Optimal hygiene is crucial for patients undergoing flexible endoscopy. Reprocessing is currently influenced by manual procedures performed by endoscopy staff. To overcome this limitation, we designed and evaluated the integration of robotic application for an automated endoscope processing pathway. Methods  We used an endoscope reprocessing pass through machine with drying cabinet and a Franka Emika Panda robot. The robot was programmed to interact with its environment in a compliant way, guaranteeing desired contact force thresholds and therefore ensuring safety of both robot and medical equipment. Results  In an initial phase we tested the robots' ability to handle a modified tray holding an endoscope as well as certain challenges (correct positioning, connection of tubing, undesired collisions). We added another Panda robot arm resulting in a device featuring two independent manipulators and tested the accuracy of each individual step. We evaluated 50 consecutive processing and transfer procedures, simulating the average daily throughput of an endoscopic unit. The endoscopes were removed in adapted tray using a specially designed lifting device and placed in an endoscope storage and venting cabinet. The mean time for the handling of the scope was 104.2 ± 1.2 seconds and an accuracy of 100 % (0 failures in 50 attempts) was achieved. Conclusions  To the best of our knowledge, this is the first description and evaluation of an automated compliant robotic assistance in the processing of endoscopes. Further development could help to overcome shortcomings of the man handled endoscope processing and could lead to reproducible, standardized and certified endoscope processing.

Testing robot-based assist-as-needed therapy for improving active participation of a patient during early neurorehabilitation: a case study

In this study, a patient in the Intensive Care-Unit received robot-based mobilization therapy with an assist-as-needed (AAN) function over the course of three weeks. Therapists were able to adapt the hip range of motion $\beta$, the bed verticalization angle $\alpha$ and the leg load force F_Load for each therapy, based on the current condition of the patient. To evaluate the patient active participation, surface electromyography (sEMG) of the M. rectus femoris (RF) and M. biceps femoris (BF) were measured and analyzed. It was observed that the patient active participation, measured through sEMG, increased along with increased hip range of motion $\beta$, bed verticalization angle $\alpha$ and leg load force F_Load set by the therapists. The patient muscle activation pattern followed the pattern of healthy controls, in part. To the authors' best knowledge, this study is the first of its kind to be performed with an ICU patient.

Development of an Exoskeleton Platform of the Finger for Objective Patient Monitoring in Rehabilitation

This paper presents the application of an adaptive exoskeleton for finger rehabilitation. The system consists of a force-controlled exoskeleton of the finger and wireless coupling to a mobile application for the rehabilitation of complex regional pain syndrome (CRPS) patients. The exoskeleton has sensors for motion detection and force control as well as a wireless communication module. The proposed mobile application allows to interactively control the exoskeleton, store collected patient-specific data, and motivate the patient for therapy by means of gamification. The exoskeleton was applied to three CRPS patients over a period of six weeks. We present the design of the exoskeleton, the mobile application with its game content, and the results of the performed preliminary patient study. The exoskeleton system showed good applicability; recorded data can be used for objective therapy evaluation.

Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich

The application of robotic and intelligent technologies in healthcare is dramatically increasing. The next generation of lightweight and tactile robots have provided a great opportunity to be used for a wide range of applications from medical examination, diagnosis, therapeutic procedures to rehabilitation and assistive robotics. They can potentially outperform current medical procedures by exploiting the com- plementary strengths of humans and computer-based technologies. In this study, the importance of human- robot interaction is discussed and technological re- quirements and challenges in making human-centered robot platforms for medical applications is addressed.

CyberLimb: a novel robotic prosthesis concept with shared and intuitive control

Background

Existing assistive technologies attempt to mimic biological functions through advanced mechatronic designs. In some occasions, the information processing demands for such systems require substantial information bandwidth and convoluted control strategies, which make it difficult for the end-user to operate. Instead, a practical and intuitive semi-automated system focused on accomplishing daily tasks may be more suitable for end-user adoption.

Methods

We developed an intelligent prosthesis for the Cybathlon Global Edition 2020. The device was designed in collaboration with the prosthesis user (pilot), addressing her needs for the competition and aiming for functionality. Our design consists of a soft robotic-based two finger gripper controlled by a force-sensing resistor (FSR) headband interface, automatic arm angle dependent wrist flexion and extension, and manual forearm supination and pronation for a shared control system. The gripper is incorporated with FSR sensors to relay haptic information to the pilot based on the output of a neural network model that estimates geometries and objects material.

Results

As a student team of the Munich Institute of Robotics and Machine Intelligence, we achieved 12th place overall in the Cybathlon competition in which we competed against state-of-the-art prosthetic devices. Our pilot successfully accomplished two challenging tasks in the competition. During training sessions, the pilot was able to accomplish the remaining competition tasks except for one. Based on observation and feedback from training sessions, we adapted our developments to fit the user’s preferences. Usability ratings indicated that the pilot perceived the prosthesis to not be fully ergonomic due to the size and weight of the system, but argued that the prosthesis was intuitive to control to perform the tasks from the Cybathlon competition.

Conclusions

The system provides an intuitive interface to conduct common daily tasks from the arm discipline of the Cybathlon competition. Based on the feedback from our pilot, future improvements include the prosthesis’ reduction in size and weight in order to enhance its mobility. Close collaboration with our pilot has allowed us to continue with the prosthesis development. Ultimately, we developed a simple-to-use solution, exemplifying a new paradigm for prosthesis design, to help assist arm amputees with daily activities.

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.