Every third work-related accident involving death or permanent disability is a falling from height accident. Preventing or mitigating the impact of such accidents, has been a long-standing challenge and using wearable robotics technology in such applications is an emerging research field. In this paper, we propose a novel fall velocity mitigation and upper-body reorientation mechanism using a wearable Cold Gas Thruster (CGT) unit to decrease the impact and therefore the injuries for low heights falls (≤ 10m). The ejecting pressurized cold gas of CGTs generates a reaction force that is used for attitude control of spacecrafts and satellites, and in wearable manned maneuvering units of astronauts. Here, we demonstrate, through a conceptual design in simulation, the application of CGTs in reorienting falling humans and reducing their impact velocity to a safe level ($\leq 4.8 m/s$). The simulation system accounts for constraints of the device design including the weight of the CGT unit (based on its eventual wearability and usability), the height and weight of the human, the safe impact velocity, and the safe impact orientation, evaluated for different fall heights. The simulation uses a pre-existing skeletal model from OpenSim for the human motion modeling, and implements a feedback control architecture to allow point-mass evaluation as well as multi-body dynamic simulation. Analysed over a set of different initial falling postures, the results show that a wearable backpack CO2-based CGT unit, with CGTs mounted in specific locations, is capable of achieving a fall impact velocity$\leq 4.8 m/s$, and maintain an upper body impact orientation within ± 15°, for falling heights ≤ 10m. The conceptual design establishes the feasibility of using CGTs as wearable fall arresting devices and lays the groundwork for their eventual real-world implementation.

The traditional approach in developing balance support devices for humans follows two strategies: (i) to simulate these systems in simplified conditions, considering for instance, the human model as an inverted pendulum; or (ii) using them in open-loop configuration, relying on feed forward controllers. Both approaches being focused on device performance tend to ignore the collateral effect that the device generated torques have on human motor control and the changes caused in gait patterns, etc. With the aim of extracting and understanding such effects this paper presents a first study in combining the control of a balance support wearable gyroscope device with a full-body neuro-musculoskeletal human model in the loop in a predictive simulation framework. The SCONE predictive simulation software provides an OpenSim (OS) human model with 9 degrees-of-freedom (DoFs) and 18 muscles (9 per leg). The wearable device is modeled in OS and implemented in SCONE as a pyramid cluster of variable speed control moment gyroscopes (VSCMGs), which includes realistic actuator models for the gyroscopes, and allows redundancy to overcome singularities. The performance of the VSCMG device for balance support in 3D was evaluated in two different simulation scenarios: (i) recovering the vertical position of the human model without any human effort contribution, from an initial 10° backward inclination; (ii) supporting a walking human model, overcoming intermittent force perturbations to the torso in 3D. The second case also demonstrates that the VSCMG device and the human effort parameters can be optimized to keep them working synchronously to fulfil their balancing and walking tasks. The successful synchronization of the intervening controllers enabled us to operate the VSCMG device in three modes, analyzing the effects on the metabolic energy expenditure, the gait and balance patterns of the human model. The results show that the knowledge of such mutual device-human adaptations can indeed lead to crucial insights and help improve the designs of the wearable devices themselves.

Encountered-type haptics (ETH) is an emerging research field that enables unencumbered physical haptic interaction in virtual reality (VR). In this paper, we propose Encountered-type haptics (ETH) as an interaction medium for immersive remote teleoperation, facilitating intuitive bare-hand interaction with visuo-haptic feedback. Our system allows a human operator to control a remote robot immersively through the visual rendering of the VR environment, while interacting with a haptic robot at the user site. The ETH feedback rendering and the teleoperation at the remote site are both implemented using a 7 degrees-of-freedom (DoFs) Franka Emika Panda robot under Cartesian-impedance control. The Cartesian goal poses for each robot are determined based on the pose of the operator's proxy hand in the VR environment and the operator's interaction intention, estimated through hand gestures and gaze direction. The impedances of both robots are updated at runtime to provide the operator with bilateral haptic interaction forces. Our system was evaluated through a user study involving a door-opening task under three teleoperation conditions: (1) without haptic feedback, (2) ETH feedback with constant impedance, and (3) ETH feedback with variable impedance. The results highlight the advantages of using ETH, including the ability to regulate forces at both the user and robot sites. ETH conditions demonstrate lower peak forces and reduced force jittering at the remote site. Furthermore, the variable impedance condition within ETH shows improved task execution times and reduced exerted force. This paper demonstrates that ETH is an effective medium for immersive bare-hand bilateral teleoperation.

Recent literature suggests that self-regulation techniques like biofeedback can be used to enhance the embodiment of artificial limbs. In this study, we developed and preliminarily tested an embodiment training protocol based on a Spatial Augmented Respiratory Cardiofeedback (SARC) implemented through a computer screen - visualizing a 3D model of a prosthetic hand (Hannes) - and a thoracic band for monitoring the Heart Rate Variability (HRV) of the users. The feedback was based on the respiratory-driven modulation of a composite index of the individuals’ cardiac autonomic state after an initial calibration based on slow breathing (at a rate perceived as “comfortable”). Alongside the assessment of the SARC use feasibility, this pilot study evaluates the virtual hand embodiment obtained in two task conditions. In both conditions, the virtual limb gradually appears when the cardiofeedback exercise is performed correctly. Otherwise, the virtual limb parts gradually disappear (“unstable” condition) or they remain visible (cumulative” condition). In the latter case, the virtual hand maintains its “reality-based” stability, supporting the subject’s motivation. Ten volunteers without disabilities were presented both conditions on 10 trials each (2min per trial). Their experience and their proprioceptive drift (estimating their real hand position as close to the artificial one) were assessed as measures of virtual prosthesis embodiment. The questionnaire results preliminarily highlight the feasibility of the SARC. Furthermore, a significantly stronger drift for the virtual prosthesis occurred in the cumulative condition, orienting further investigations.

Shared control and shared autonomy play an important role in assistive technologies, allowing the offloading of the cognitive burden required for control from the user to the intelligent robotic device. In this context, electrophysiological measures of error detection, directly measured from a person's brain activity as Error-related Potentials (ErrPs), can be exploited to provide passive adaptation of an external semi-autonomous system to the human. This concept was implemented in an online robot learning task, where user's evaluation of the robot's actions, in terms of detected ErrP, was exploited to update a reward function in a Reinforcement Learning (RL) framework. Results from both simulated and experimental studies show that the introduction of human evaluation in the robot learning loop allows for: (1) the acceleration of optimal policy learning in a target reaching task, (2) the introduction of a further degree of control in robot learning, namely identification of one among multiple targets, according to the user's will. Overall, presented results support the potential of human-robot co-adaptive and co-operative strategies to develop human-centered assistive technologies.

In laser-based laryngeal surgeries, motorized laser scanners offer greater aiming accuracy and efficiency. In this paper, a new motorized laser micromanipulator is presented, which is based on a spherical orienting device. It is a 2 degrees-of-freedom roll/pitch mechanism which actuates the laser beamsplitter mirror for improved aiming control and automated intraoperative planning. The combination of this device with state-of-the-art reflective laser focusing optics overcomes the drawbacks of an earlier prototype, providing increased operating distance and surgical range. This makes the device more suitable to real surgical scenarios in the operating room (OR). Improved system accuracy and usability is successfully demonstrated through comparative user trials against the traditional manual laser micromanipulator. The new device offers greater than 57% improvement in accuracy demonstrating its safety and usability. Preliminary ex-vivo trials were also performed with expert surgeons with the new mechanism. The surgeons evaluated the system positively and provided valuable and favourable feedback pointing to the suitability of the device for the OR and its potential to enhance the capacity of laser-based transoral microsurgeries.

Ergonomic and human-centered approaches are increasingly important in the design of surgeon-machine interfaces. In the case of microsurgeries, the procedures suffer from susceptibility to variation in surgeon skill and equipment characteristics. This paper presents a novel, computer-assisted surgical interface for laser-based microsurgeries, called the “μRALP Surgical System”. With the system, surgeries can be performed with improved safety and precision using a three-part architecture: (i) a 3D viewer device providing stereoscopic visualization; (ii) a graphics stylus that controls a motorized micromanipulator for laser aiming and activation; and (iii) a configuration interface allowing system setup and modifications in real-time. The system combines the advantages of a computer-assisted platform while respecting the visualization and manipulation requirements of a microsurgical procedure. The features include intraoperative planning for automatic laser incisions and ablations as well as safety regions based on virtual overlays in the surgeon's field-of-view. A comparative evaluation of the proposed system against the traditional system points to the clear superiority of the new interface. The quantitative comparison shows that the proposed interface is safer, more precise, and better controlled. The qualitative comparison demonstrates that the interface is easier to use, easier to learn, and has a minimal training requirement. The technological advances presented here shall lead to enhanced interfaces, increasing the capacity of surgical systems through user-centered design approaches.

This paper presents a low-complexity, novel approach to wireless sensor network (WSN) assisted autonomous mobile robot (AMR) navigation. The goal is to have an AMR navigate to a target location using only the information inherent to WSNs, i.e., topology of the WSN and received signal strength (RSS) information, while executing an efficient navigation path. Here, the AMR has neither the location information for the WSN, nor any sophisticated ranging equipment for prior mapping. Two schemes are proposed utilizing particle filtering based bearing estimation with RSS values obtained from directional antennas. Real-world experiments demonstrate the effectiveness of the proposed schemes. In the basic node-to-node navigation scheme, the bearing-only particle filtering reduces trajectory length by 11.7% (indoors) and 15% (outdoors), when compared to using raw bearing measurements. The advanced scheme further reduces the trajectory length by 22.8% (indoors) and 19.8% (outdoors), as compared to the basic scheme. The mechanisms exploit the low-cost, low-complexity advantages of the WSNs to provide an effective method for map-less and ranging-less navigation.

State-of-the-art laser phonomicrosurgery (LP) used for the treatment of laryngeal abnormalities involves complex otolaryngological surgical techniques. It relies heavily on surgeon dexterity, requiring significant psychomotor skills. Equipment scale and size, laser operative distance, and the anatomically small nature of the vocal folds all combine to compound the surgical challenges. An objective measurement is therefore necessary to understand the impact of equipment design, its usability, surgeon skill, and learning, on performing LP effectively. This paper introduces imaging based feature extraction as a method to establish metrics to assess surgical performance in LP. Experimental analysis demonstrates the utility of these metrics in measuring surgical task execution vis-à-vis the task objectives. The metrics also provide for a combined rating scale giving a robust quantitative classification of the levels of surgical performance.

Robot-assisted surgical procedures, such as Laser Phonomicrosurgery (LP), suffer from susceptibility to variation in surgeon skill and equipment characteristics. Ergonomic and human-centered approaches acquire increased importance in the design of surgeon-machine interfaces. This paper proposes a protocol for comparative evaluation of surgeon-machine interfaces based on two criteria: (i) the subjective evaluation of their usability using questionnaires, and (ii) the objective evaluation of their performance using an imaging-based feature extraction method. Two interfaces in LP, the traditional (“AcuBlade”) interface and the novel (“Virtual Scalpel”) interface, were evaluated to demonstrate the effectiveness of the proposed scheme. A series of experimental trials were conducted using the interfaces in surgery-like tasks in a controlled environment. The subjective evaluation pointed to the superiority of the Virtual Scalpel interface (score: 83.06) in terms of confidence and ease of use, and learnability, over the AcuBlade interface (score: 65.56). The objective evaluation showed the Virtual Scalpel interface having an overall score (55.96) significantly superior to the AcuBlade (51.37). It is thus shown that the multidimensional evaluation approach allowed to clearly distinguish between levels of perceived usability and effective performance of surgeon-machine interfaces from a user-centered perspective.

This paper extends the novel research for event localization and target-directed navigation using a deployed wireless sensor network (WSN) [4]. The goal is to have an autonomous mobile robot (AMR) navigate to a target-location by: (i) producing an artificial magnitude distribution within the WSN-covered region, and (ii) having the AMR use the pseudo-gradient from the interpolated distribution in its neighborhood, as it moves towards the target location. Implicit surfaces are used to interpolate the artificial distribution. This scheme only uses the topology of the WSN and received signal strength (RSS) to estimate an efficient navigation path for the AMR. Here, the AMR does not require global coordinates for the region, as it relies on local, neighborhood information alone to navigate. The performance of the scheme is analyzed with hardware experiments and in simulation, using a variety of node-densities and with increasing levels of noise to ensure robustness.

Robot manipulator designs are increasingly focused on low cost approaches, especially those envisioned for use in unstructured environments such as households, office spaces and hazardous environments. The cost of angular sensors varies based on the precision offered. For tasks in these environments, millimeter order manipulation errors are unlikely to cause drastic reduction in performance. In this paper, estimates the joint angles of a manipulator using low cost triaxial accelerometers by taking the difference between consecutive acceleration vectors. The accelerometer-based angle is compensated with a uniaxial gyroscope using a complementary filter to give robust measurements. Three compensation strategies are compared: complementary filter, time varying complementary filter, and extended Kalman filter. This sensor setup can also accurately track the joint angle even when the joint axis is parallel to gravity and the accelerometer data does not provide useful information. In order to analyze this strategy, accelerometers and gyroscopes were mounted on one arm of a PR2 robot. The arm was manually moved smoothly through different trajectories in its workspace while the joint angle readings from the on-board optical encoders were compared against the joint angle estimates from the accelerometers and gyroscopes. The low cost angle estimation strategy has a mean error 1.3° over the three joints estimated, resulting in mean end effector position errors of 6.1 mm or less. This system provides an effective angular measurement as an alternative to high precision encoders in low cost manipulators and as redundant measurements for safety in other manipulators.