Adaptive Lifting Index (aLI) for Real-Time Instrumental Biomechanical Risk Assessment: Concepts, Mathematics, and First Experimental Results

When performing lifting tasks at work, the Lifting Index (LI) is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive LI (aLI) that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying aLI computation and compare aLI calculations in real-time using wearable sensors and force platforms with the LI estimated with the standard method used by ergonomists and occupational health and safety technicians. To reach this aim, 10 participants performed six lifting tasks under two risk conditions. The results show us that the aLI value rapidly converges towards the reference value in all tasks, suggesting a promising use of adaptive algorithms and instrumental tools for biomechanical risk assessment.

Kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners

Industrial screwing is one of several industry branches' most common manufacturing processes. Good quality and structured data from these operations have increased demand with the popularization of data-driven techniques for manufacturing automation. The dataset presented in this paper comprises screwing experiments with aeronautical nuts performed by an industrial robot Kuka KR-16 in a lab setting. The data comprises force, torque, linear and angular displacements, and velocities in time-series format. The dataset contains three different experiment results: mounted, jammed, and not mounted, which can be used as labels for classification techniques.

Ergonomic human-robot collaboration in industry: A review

In the current industrial context, the importance of assessing and improving workers' health conditions is widely recognised. Both physical and psycho-social factors contribute to jeopardising the underlying comfort and well-being, boosting the occurrence of diseases and injuries, and affecting their quality of life. Human-robot interaction and collaboration frameworks stand out among the possible solutions to prevent and mitigate workplace risk factors. The increasingly advanced control strategies and planning schemes featured by collaborative robots have the potential to foster fruitful and efficient coordination during the execution of hybrid tasks, by meeting their human counterparts' needs and limits. To this end, a thorough and comprehensive evaluation of an individual's ergonomics, i.e. direct effect of workload on the human psycho-physical state, must be taken into account. In this review article, we provide an overview of the existing ergonomics assessment tools as well as the available monitoring technologies to drive and adapt a collaborative robot's behaviour. Preliminary attempts of ergonomic human-robot collaboration frameworks are presented next, discussing state-of-the-art limitations and challenges. Future trends and promising themes are finally highlighted, aiming to promote safety, health, and equality in worldwide workplaces.

A Directional Vibrotactile Feedback Interface for Ergonomic Postural Adjustment

The objective of this paper is to develop and evaluate a directional vibrotactile feedback interface as a guidance tool for postural adjustments during work. In contrast to the existing active and wearable systems such as exoskeletons, we aim to create a lightweight and intuitive interface, capable of guiding its wearers towards more ergonomic and healthy working conditions. To achieve this, a vibrotactile device called ErgoTac is employed to develop three different feedback modalities that are able to provide a directional guidance at the body segments towards a desired pose. In addition, an evaluation is made to find the most suitable, comfortable, and intuitive feedback modality for the user. Therefore, these modalities are first compared experimentally on fifteen subjects wearing eight ErgoTac devices to achieve targeted arm and torso configurations. The most effective directional feedback modality is then evaluated on five subjects in a set of experiments in which an ergonomic optimisation module provides the optimised body posture while performing heavy lifting or forceful exertion tasks. The results yield strong evidence on the usefulness and the intuitiveness of one of the developed modalities in providing guidance towards ergonomic working conditions, by minimising the effect of an external load on body joints. We believe that the integration of such low-cost devices in workplaces can help address the well-known and complex problem of work-related musculoskeletal disorders.

A Supernumerary Soft Robotic Limb for Reducing Hand-Arm Vibration Syndromes Risks

The most common causes of the risk of work-related musculoskeletal disorders (WMSD) have been identified as joint overloading, bad postures, and vibrations. In the last two decades, various solutions ranging from human-robot collaborative systems to robotic exoskeletons have been proposed to mitigate them. More recently, a new approach has been proposed with a high potential in this direction: the supernumerary robotic limbs SRLs are additional robotic body parts (e.g., fingers, legs, and arms) that can be worn by the workers, augmenting their natural ability and reducing the risks of injuries. These systems are generally proposed in the literature for their potentiality of augmenting the user's ability, but here we would like to explore this kind of technology as a new generation of (personal) protective equipment. A supernumerary robotic upper limb, for example, allows for indirectly interacting with hazardous objects like chemical products or vibrating tools. In particular, in this work, we present a supernumerary robotic limbs system to reduce the vibration transmitted along the arms and minimize the load on the upper limb joints. For this purpose, an off-the-shelf wearable gravity compensation system is integrated with a soft robotic hand and a custom damping wrist, designed starting from theoretical considerations on a mass-spring-damper model. The real efficacy of the system was experimentally tested within a simulated industrial work environment, where seven subjects performed a drilling task on two different materials. Experimental analysis was conducted according to the ISO-5349. Results showed a reduction from 40 to 60% of vibration transmission with respect to the traditional hand drilling using the presented SRL system without compromising the time performance.

The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics

Due to the epochal changes introduced by “Industry 4.0”, it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human–robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative “direct instrumental evaluations” and “rating of standard methods”, allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers’ awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities.

A Novel Soft Robotic Supernumerary Hand for Severely Affected Stroke Patients

Upper limb functions are severely affected in 23% of the chronic stroke patients, compromising their life quality. To re-enable hand use, providing a degree of functionality and motivating against learned non-use, we propose a robotic supernumerary limb, the SoftHand X (SHX), consisting of a robotic hand, a gravity support system, and different sensors to detect the patient's intent for controlling the robotic hand. In this paper, this novel compensational approach is introduced and experimentally evaluated in stroke patients, assessing its efficacy, usability and safety. Ten patients were asked to perform tasks of a modified Action Research Arm Test with the SHX, by using three input methods. The mARAT scores rated the potentiality of the system. Usability was evaluated with the System Usability Scale, while spasticity before and after use was measured by the modified Ashworth Scale (mAS). Nine patients, not able to perform any tasks without external support, completed the whole experimental procedure using the proposed system with a median score greater than 12/30. Among the three input methods tested, the usability of one was rated as "good" while the other two were rated as "ok". Seven patients exhibited a reduction of the mAS. All nine patients stated that they would use the system frequently. Results obtained suggest that the SHX has the potential to partially compensate severely impaired hand function in stroke patients.

Unifying bilateral teleoperation and tele-impedance for enhanced user experience

Usability is one of the most important aspects of teleoperation. Ideally, the operator’s experience should be one of complete command over the remote environment, but also be as close as possible to what they would have if physically present at the remote end, i.e., transparency in terms of both action and perception. These two aspects may coincide in favorable conditions, where classic approaches such as the four-channel architecture ensures transparency of the control framework. In the presence of substantial delays between the user and the slave, however, the stability–performance trade-off inherent to bilateral teleoperation deteriorates not only transparency, but also command. An alternative, unilateral approach is given by tele-impedance, which controls the slave–environment interaction by measuring and remotely replicating the user’s limb endpoint position and impedance. Not including force feedback to the operator, tele-impedance is absolutely robust to delays, whereas it completely lacks transparency. This article introduces a novel control framework that integrates a new, fully transparent, two-channel bilateral architecture with the tele-impedance paradigm. The result is a unified solution that mitigates problems of classical approaches, and provides the user with additional tools to modulate the slave robot’s physical interaction behavior, resulting in a better operator experience in spite of time inconsistencies. The validity and effectiveness of the proposed solution is demonstrated in terms of performance in the interaction tasks, of user fatigue and overall experience.

Design and Validation of a Modular One-To-Many Actuator for a Soft Wearable Exosuit

The size, weight, and power consumption of soft wearable robots rapidly scale with their number of active degrees of freedom. While various underactuation strategies have been proposed, most of them impose hard constrains on the kinetics and kinematics of the device. Here we propose a paradigm to independently control multiple degrees of freedom using a set of modular components, all tapping power from a single motor. Each module consists of three electromagnetic clutches, controlled to convert a constant unidirectional motion in an arbitrary output trajectory. We detail the design and functioning principle of each module and propose an approach to control the velocity and position of its output. The device is characterized in free space and under loading conditions. Finally, we test the performance of the proposed actuation scheme to drive a soft exosuit for the elbow joint, comparing it with the performance obtained using a traditional DC motor and an unpowered-exosuit condition. The exosuit powered by our novel scheme reduces the biological torque required to move by an average of 46.2%, compared to the unpowered condition, but negatively affects movement smoothness. When compared to a DC motor, using the our paradigm slightly deteriorates performance. Despite the technical limitations of the current design, the method proposed in this paper is a promising way to design more portable wearable robots.

A Bio-inspired Grasp Stiffness Control for Robotic Hands

This work presents a bio-inspired grasp stiffness control for robotic hands based on the concepts of Common Mode Stiffness (CMS) and Configuration Dependent Stiffness (CDS). Using an ellipsoid representation of the desired grasp stiffness, the algorithm focuses on achieving its geometrical features. Based on preliminary knowledge of the fingers workspace, the method starts by exploring the possible hand poses that maintain the grasp contacts on the object. This outputs a first selection of feasible grasp configurations providing the base for the CDS control. Then, an optimization is performed to find the minimum joint stiffness (CMS control) that would stabilize these grasps. This joint stiffness can be increased afterwards depending on the task requirements. The algorithm finally chooses among all the found stable configurations the one that results in a better approximation of the desired grasp stiffness geometry (CDS). The proposed method results in a reduction of the control complexity, needing to independently regulate the joint positions, but requiring only one input to produce the desired joint stiffness. Moreover, the usage of the fingers pose to attain the desired grasp stiffness results in a more energy-efficient configuration than only relying on the joint stiffness (i.e., joint torques) modifications. The control strategy is evaluated using the fully actuated Allegro Hand while grasping a wide variety of objects. Different desired grasp stiffness profiles are selected to exemplify several stiffness geometries.

Soft brain-machine interfaces for assistive robotics: A novel control approach

Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients. The interface is composed of an eye-tracking system, for an intuitive and reliable control of a robotic arm system's trajectories, and a Brain-Computer Interface (BCI) unit, for the control of the robot Cartesian stiffness, which determines the interaction forces between the robot and environment. The latter control is achieved by estimating in real-time a unidimensional index from user's electroencephalographic (EEG) signals, which provides the probability of a neutral or active state. This estimated state is then translated into a stiffness value for the robotic arm, allowing a reliable modulation of the robot's impedance. A preliminary evaluation of this hybrid interface concept provided evidence on the effective execution of tasks with dynamic uncertainties, demonstrating the great potential of this control method in BMI applications for self-service and clinical care.

Reduced-complexity representation of the human arm active endpoint stiffness for supervisory control of remote manipulation

In this paper, a reduced-complexity model of the human arm endpoint stiffness is introduced and experimentally evaluated for the teleimpedance control of a compliant robotic arm. The modeling of the human arm endpoint stiffness behavior is inspired by human motor control principles on the predominant use of the arm configuration in directional adjustments of the endpoint stiffness profile, and the synergistic effect of muscular activations, which contributes to a coordinated modification of the task stiffness in all Cartesian directions. Calibration and identification of the model parameters are carried out experimentally, using perturbation-based arm endpoint stiffness measurements in different arm configurations and cocontraction levels of the chosen muscles. Consequently, the real-time model is used for the remote control of a compliant robotic arm while executing a drilling task, a representative example of tool use in environments with constraints and dynamic uncertainties. The results of this study illustrate that the proposed model enables the master to execute the remote task by modulation of the directions of the major axes of the endpoint stiffness ellipsoid and its volume using natural arm configurations and the cocontraction of the involved muscles, respectively.

Modular one-to-many clutchable actuator for a soft elbow exosuit

Exoskeletons have been developed for a wide range of applications, from the military to the medical field, with the aim of augmenting human performance or compensating for neuromuscular deficiencies. However, to empower the high number of degrees of freedom of the human body, they often employ a high number of motors, increasing the size, weight and power consumption of the system. We hereby present an actuation strategy to empower our elbow exosuit that adopts a single motor to drive multiple, independently actuated, degrees of freedom. This paradigm, known as One-to-many, is achieved using a modular design where each module comprises a clutchable mechanism that allows to convert a single directional motion from the prime mover to a selectable bidirectional output. Moreover, the mechanism has a locking feature that enables the wearer of the exoskeleton to hold a static load with a minimal power consumption. We present a simple controller for the clutchable unit based on a finite-state machine model, and evaluate its performance for varying input velocities. The system is shown to have a bandwidth of 1.5 Hz, sufficient for daily elbow movements, whilst retaining a compact design.

A Human-Robot Co-Manipulation Approach Based on Human Sensorimotor Information

This paper aims to improve the interaction and coordination between the human and the robot in cooperative execution of complex, powerful, and dynamic tasks. We propose a novel approach that integrates online information about the human motor function and manipulability properties into the hybrid controller of the assistive robot. Through this human-in-the-loop framework, the robot can adapt to the human motor behavior and provide the appropriate assistive response in different phases of the cooperative task. We experimentally evaluate the proposed approach in two human-robot co-manipulation tasks that require specific complementary behavior from the two agents. Results suggest that the proposed technique, which relies on a minimum degree of task-level pre-programming, can achieve an enhanced physical human-robot interaction performance and deliver appropriate level of assistance to the human operator.

Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)-Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

Exploring teleimpedance and tactile feedback for intuitive control of the Pisa/IIT SoftHand

This paper proposes a teleimpedance controller with tactile feedback for more intuitive control of the Pisa/IIT SoftHand. With the aim to realize a robust, efficient and low-cost hand prosthesis design, the SoftHand is developed based on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. Due to the built-in flexibility of the hand joints, as the SoftHand grasps, it follows a synergistic path while allowing grasping of objects of various shapes using only a single motor. The DC motor of the hand incorporates a novel teleimpedance control in which the user's postural and stiffness synergy references are tracked in real-time. In addition, for intuitive control of the hand, two tactile interfaces are developed. The first interface (mechanotactile) exploits a disturbance observer which estimates the interaction forces in contact with the grasped object. Estimated interaction forces are then converted and applied to the upper arm of the user via a custom made pressure cuff. The second interface employs vibrotactile feedback based on surface irregularities and acceleration signals and is used to provide the user with information about the surface properties of the object as well as detection of object slippage while grasping. Grasp robustness and intuitiveness of hand control were evaluated in two sets of experiments. Results suggest that incorporating the aforementioned haptic feedback strategies, together with user-driven compliance of the hand, facilitate execution of safe and stable grasps, while suggesting that a low-cost, robust hand employing hardware-based synergies might be a good alternative to traditional myoelectric prostheses.

A synergy-driven approach to a myoelectric hand

In this paper, we present the Pisa/IIT SoftHand with myoelectric control as a synergy-driven approach for a prosthetic hand. Commercially available myoelectric hands are more expensive, heavier, and less robust than their body-powered counterparts; however, they can offer greater freedom of motion and a more aesthetically pleasing appearance. The Pisa/IIT SoftHand is built on the motor control principle of synergies through which the immense complexity of the hand is simplified into distinct motor patterns. As the SoftHand grasps, it follows a synergistic path with built-in flexibility to allow grasping of a wide variety of objects with a single motor. Here we test, as a proof-of-concept, 4 myoelectric controllers: a standard controller in which the EMG signal is used only as a position reference, an impedance controller that determines both position and stiffness references from the EMG input, a standard controller with vibrotactile force feedback, and finally a combined vibrotactile-impedance (VI) controller. Four healthy subjects tested the control algorithms by grasping various objects. All controllers were sufficient for basic grasping, however the impedance and vibrotactile controllers reduced the physical and cognitive load on the user, while the combined VI mode was the easiest to use of the four. While these results need to be validated with amputees, they suggest a low-cost, robust hand employing hardware-based synergies is a viable alternative to traditional myoelectric prostheses.

Tele-impedance: Teleoperation with impedance regulation using a body–machine interface

This work presents the concept of tele-impedance as a method for remotely controlling a robotic arm in interaction with uncertain environments. As an alternative to bilateral force-reflecting teleoperation control, in tele-impedance a compound reference command is sent to the slave robot including both the desired motion trajectory and impedance profile, which are then realized by the remote controller without explicit feedback to the operator. We derive the reference command from a novel body–machine interface (BMI) applied to the master operator’s arm, using only non-intrusive position and electromyography (EMG) measurements, and excluding any feedback from the remote site except for looking at the task. The proposed BMI exploits a novel algorithm to decouple the estimates of force and stiffness of the human arm while performing the task. The endpoint (wrist) position of the human arm is monitored by an optical tracking system and used for the closed-loop position control of the robot’s end-effector. The concept is demonstrated in two experiments, namely a peg-in-the-hole and a ball-catching task, which illustrate complementary aspects of the method. The performance of tele-impedance control is assessed by comparing the results obtained with the slave arm under either constantly low or high stiffness.

A neuro-sliding-mode control with adaptive modeling of uncertainty for control of movement in paralyzed limbs using functional electrical stimulation

During the past several years, several strategies have been proposed for control of joint movement in paraplegic subjects using functional electrical stimulation (FES), but developing a control strategy that provides satisfactory tracking performance, to be robust against time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, muscle fatigue, and external disturbances, and to be easy to apply without any re-identification of plant dynamics during different experiment sessions is still an open problem. In this paper, we propose a novel control methodology that is based on synergistic combination of neural networks with sliding-mode control (SMC) for controlling FES. The main advantage of SMC derives from the property of robustness to system uncertainties and external disturbances. However, the main drawback of the standard sliding modes is mostly related to the so-called chattering caused by the high-frequency control switching. To eliminate the chattering, we couple two neural networks with online learning without any offline training into the SMC. A recurrent neural network is used to model the uncertainties and provide an auxiliary equivalent control to keep the uncertainties to low values, and consequently, to use an SMC with lower switching gain. The second neural network consists of a single neuron and is used as an auxiliary controller. The control law will be switched from the SMC to neural control, when the state trajectory of system enters in some boundary layer around the sliding surface. Extensive simulations and experiments on healthy and paraplegic subjects are provided to demonstrate the robustness, stability, and tracking accuracy of the proposed neuroadaptive SMC. The results show that the neuro-SMC provides accurate tracking control with fast convergence for different reference trajectories and could generate control signals to compensate the muscle fatigue and reject the external disturbance.

Neuro-sliding mode control with modular models for control of knee-joint angle using quadriceps electrical stimulation

In this paper, we propose a control methodology which is based on synergistic combination of a single-neuron controller with sliding mode control (SMC) for control of knee-joint position in paraplegic subjects with quadriceps stimulation. The control law will be switched from the sliding mode control to neural control, when the state trajectory of system enters in some boundary layer around the sliding surface. The main drawback of the standard sliding modes is mostly related to the so-called chattering caused by the high-frequency control switching. The value of switching gain depends on the bounds of system uncertainties. The system with large uncertainties needs to use a higher switching gain. This will, however, result in the high-frequency control switching and chattering across the sliding surface. To avoid such a condition, it is necessary to decrease the system uncertainty. To decrease the uncertainty, an accurate model of the system is required. For this purpose, we present a modular approach to modeling the knee-joint dynamics. Extensive experiments on healthy and paraplegic subjects are provided to demonstrate the robustness, stability and tracking accuracy of the neuro-SMC. The experimental results show that the neuro-SMC provides excellent tracking control for different reference trajectories and could generate control signals to compensate the muscle fatigue.