A Sensory Feedback Neural Stimulator Prototype for Both Implantable and Wearable Applications.

The restoration of sensory feedback is one of the current challenges in the field of prosthetics. This work, following the analysis of the various types of sensory feedback, aims to present a prototype device that could be used both for implantable applications to perform PNS and for wearable applications, performing TENS, to restore sensory feedback. The two systems are composed of three electronic boards that are presented in detail, as well as the bench tests carried out. To the authors' best knowledge, this work presents the first device that can be used in a dual scenario for restoring sensory feedback. Both the implantable and wearable versions respected the expected values regarding the stimulation parameters. In its implantable version, the proposed system allows simultaneous and independent stimulation of 30 channels. Furthermore, the capacity of the wearable version to elicit somatic sensations was evaluated on healthy participants demonstrating performance comparable with commercial solutions.

A marker-based approach to determine the centers of rotation of finger joints.

BACKGROUND AND OBJECTIVE: The methods proposed in literature to estimate the position of hand joints Centers of Rotation (CoRs) typically require computationally non-trivial optimization routines and exploit a high number of markers to calculate CoRs positions from surface marker trajectories. Moreover, most of the existing works evaluated the accuracy only in simulation. This work proposes a new procedure, based on the Pratt circle fit, to estimate joints CoRs position in 2D through marker-based acquisitions. METHODS: The advantage of the Pratt circle fit lies in its simplicity and computational speed, and in the possibility of exploiting a reduced markerset for calculating CoRs. By applying simplifying assumptions regarding the movement of the fingers (i.e., planar and decoupled flexion-extension movements of each joint occurring in the same flexion plane for all the joints of the finger), it is possible to determine the position of the CoR of each joint in 2D. For this reason, the estimation of the Carpo-MetaCarpal joint of the thumb was not included in this work, as it exhibits a more complex movement associated to the combination of a flexion-extension and adduction-abduction degree of freedom. The errors in estimating CoRs were evaluated by conducting experimental acquisitions on an anthropomorphic robotic hand and comparing the position of the estimated CoR with the real position of the CoR. The repeatability of the method and its capability to estimate anatomically plausible CoRs were evaluated through experimental acquisitions conducted on five healthy volunteers. RESULTS: Errors in estimating finger joints CoRs were in the order of 0.70 mm and 0.18 mm respectively along the finger longitudinal direction (i.e., x coordinate) and thickness (i.e., y coordinate). Standard Deviations of CoRs positions were comparable to the ones obtained in literature (i.e., below 2 mm and 1 mm respectively for the x and y coordinates), thus demonstrating the repeatability of the method. The Anatomical Plausibility Rate of the proposed approach was between 80% and 100%. CONCLUSIONS: The performance of the Pratt-based CoRs estimation procedure proposed in this work was comparable to other existing methods, with the advantage of exploiting a simple fitting algorithm and a reduced markerset with respect to the state-of-the-art techniques.

Nursing-engineering interdisciplinary research: A synthesis of methodological approach to perform healthcare-technology integrated projects.

In the dynamic landscape of contemporary healthcare, the imperative for advancing the frontiers of knowledge and improving patient outcomes necessitates a paradigm shift towards a multidisciplinary approach. This background great enhances a nurse's ability to interface with technology and create technical solutions such as robots, patient care devices, or computer simulation for patient care needs and nursing care delivery. This study aims to describe, through a narrative review of evidence, a methodology to develop and manager Nursing-Engineering interdisciplinary project, clarify the key points and facilitate professionals who are not very familiar with this topic. The methodology employed highlights the importance of this kind of research that allows to achieve highest standards of practice leading to improved patient care, innovative solutions and a global contribution to healthcare excellence.

Exploring the EMG transient: the muscular activation sequences used as novel time-domain features for hand gestures classification.

Introduction: Muscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks. Methods: The signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted. Results: The highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms. Discussion: Muscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands.

Tailoring Upper-Limb Robot-Aided Orthopedic Rehabilitation on Patients' Psychophysiological State.

Physical therapy keeps exploiting more and more the capabilities of the robot of adapting the treatments to patients' needs. This paper aims at presenting a psychophysiological-aware control strategy for upper limb robot-aided orthopedic rehabilitation. The main features are the capability of i) generating point-to-point trajectories inside an adaptable workspace, ii) providing assistance in guiding the patients' limbs in accomplishing the assigned task allowing them to freely move with a certain degree of spatial and temporal autonomy and iii) tuning the control parameters according to the patients' kinematics performance and psychophysiological state. The implemented control strategy is validated in a real clinical setting on eight orthopedic patients undergoing twenty daily robot-aided rehabilitation sessions. The psychophysiological-aware control strategy evidenced a positive impact on the enrolled participants since they are effectively conducted in a calmer condition with respect to the patients who did not receive the psychophysiological adaptation. Moreover, clinical performance indicators suggest that the proposed tailored control strategy improves motor functions.

An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts.

The heart rate (HR) is a widely used clinical variable that provides important information on a physical user's state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user's wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user's HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG.

Wearable High Voltage Compliant Current Stimulator for Restoring Sensory Feedback.

Transcutaneous Electrical Nerve Stimulation (TENS) is a promising technique for eliciting referred tactile sensations in patients with limb amputation. Although several studies show the validity of this technique, its application in daily life and away from laboratories is limited by the need for more portable instrumentation that guarantees the necessary voltage and current requirements for proper sensory stimulation. This study proposes a low-cost, wearable high-voltage compliant current stimulator with four independent channels based on Components-Off-The-Shelf (COTS). This microcontroller-based system implements a voltage-current converter controllable through a digital-to-analog converter that delivers up to 25 mA to load up to 3.6 kΩ. The high-voltage compliance enables the system to adapt to variations in electrode-skin impedance, allowing it to stimulate loads over 10 kΩ with currents of 5 mA. The system was realized on a four-layer PCB (115.9 mm × 61 mm, 52 g). The functionality of the device was tested on resistive loads and on an equivalent skin-like RC circuit. Moreover, the possibility of implementing an amplitude modulation was demonstrated.

Affective state estimation based on Russell's model and physiological measurements.

Affective states are psycho-physiological constructs connecting mental and physiological processes. They can be represented in terms of arousal and valence according to the Russel's model and can be extracted from physiological changes in human body. However, a well-established optimal feature set and a classification method effective in terms of accuracy and estimation time are not present in the literature. This paper aims at defining a reliable and efficient approach for real-time affective state estimation. To obtain this, the optimal physiological feature set and the most effective machine learning algorithm, to cope with binary as well as multi-class classification problems, were identified. ReliefF feature selection algorithm was implemented to define a reduced optimal feature set. Supervised learning algorithms, such as K-Nearest Neighbors (KNN), cubic and gaussian Support Vector Machine, and Linear Discriminant Analysis, were implemented to compare their effectiveness in affective state estimation. The developed approach was tested on physiological signals acquired on 20 healthy volunteers during the administration of images, belonging to the International Affective Picture System, conceived for inducing different affective states. ReliefF algorithm reduced the number of physiological features from 23 to 13. The performances of machine learning algorithms were compared and the experimental results showed that both accuracy and estimation time benefited from the optimal feature set use. Furthermore, the KNN algorithm resulted to be the most suitable for affective state estimation. The results of the assessment of arousal and valence states on 20 participants indicate that KNN classifier, adopted with the 13 identified optimal features, is the most effective approach for real-time affective state estimation.

Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs).

BACKGROUND: The aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator. METHODS: Ten impaired participants (5 males and 5 females, mean age 52 ± 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3 s and a reliable control in case it remained below 5 s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability. RESULTS: All participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3 s in case of the exoskeleton and bellow 5s in case of the external manipulator). CONCLUSIONS: Although the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.

Treatment of the Paretic Hand with a Robotic Glove Combined with Physiotherapy in a Patient Suffering from Traumatic Tetraparesis: A Case Report.

BACKGROUND: cervical spinal cord injury leads to loss of upper limb functionality, which causes a decrease in autonomy to perform activities of daily living. The use of robotic technologies in rehabilitation could contribute to improving upper limb functionality and treatment quality. This case report aims to describe the potential of robotic hand treatment with Gloreha Sinfonia, in combination with conventional rehabilitation, in a tetraparetic patient. MATERIAL: fifteen rehabilitative sessions were performed. Evaluations were conducted pre-treatment (T0), post-treatment (T1), and at two-months follow-up (T2) based on: the upper-limb range of motion and force assessment, the FMA-UE, the 9-Hole Peg Test (9HPT), and the DASH questionnaire. A virtual reality game-based rating system was used to evaluate the force control and modulation ability. RESULTS: the patient reported greater ability to use hands with less compensation at T1 and T2 assessments. Improvements in clinical scales were reported in both hands at T1, however, at T2 only did the dominant hand show further improvement. Improved grip strength control and modulation ability were reported for T1. However a worsening was found in both hands at T2, significant only for the non-dominant hand. The maximum force exerted increased from T0 to T2 in both hands. CONCLUSION: hand treatment combining physical therapy and Gloreha Sinfonia seems to have benefits in functionality and dexterity in tetraparetic patient in the short term. Further studies are needed to confirm these findings, to verify long-term results, and to identify the most appropriate modalities of robotic rehabilitation.

A semi-autonomous robot control based on bone layer transition detection for a safe pedicle tapping.

PURPOSE: Automatic robotic platforms for robot-aided spinal surgery are mostly employed for drilling the pedicle screw path and do not adapt the tool rotational speed depending on the variation of the bone density. This feature is highly desirable in control strategies for robot-aided pedicle tapping, which may result in a poor quality thread if the surgical tool speed is not adequately tuned depending on the bone density to be threaded. Therefore, the objective of this paper is to propose a novel semi-autonomous control for robot-aided pedicle tapping that is able to (i) identify the bone layer transition, (ii) adapt the tool velocity depending on the detected bone layer density and (iii) stop the tool tip before propulsion of the bone boundaries. METHODS: The proposed semi-autonomous control for pedicle tapping consists of: (i) a hybrid position/force control loop that allows the surgeon to move the surgical tool along a pre-planned axis and (ii) a velocity control loop that allows him/her to finely tune the tool rotational speed by modulating the tool-bone interaction force along the same axis. The velocity control loop integrates also a bone layer transition detection algorithm that dynamically limits the tool velocity depending on the bone layer density. The approach was tested on the Kuka LWR4+ provided with an actuated surgical tapper which was used to tap a wood specimen simulating the bone layer density characteristics and bovine bones. RESULTS: A normalized maximum time delay in the bone layer transition detection of 0.25 was achieved by the experiments. A success rate of [Formula: see text] was achieved for all the tested tool velocities. The proposed control achieved a maximum steady-state error of 0.4 rpm. CONCLUSION: The study demonstrated high capability of the proposed approach to i) promptly detect transition among the specimen layers and ii) adapt the tool velocities depending on the detected layers.

Hierarchical strategy for sEMG classification of the hand/wrist gestures and forces of transradial amputees.

Introduction: The myoelectric control strategy, based on surface electromyographic signals, has long been used for controlling a prosthetic system with multiple degrees of freedom. Several methods classify gestures and force levels but the simultaneous real-time control of hand/wrist gestures and force levels did not yet reach a satisfactory level of effectiveness. Methods: In this work, the hierarchical classification approach, already validated on 31 healthy subjects, was adapted for the real-time control of a multi-DoFs prosthetic system on 15 trans-radial amputees. The effectiveness of the hierarchical classification approach was assessed by evaluating both offline and real-time performance using three algorithms: Logistic Regression (LR), Non-linear Logistic Regression (NLR), and Linear Discriminant Analysis (LDA). Results: The results of this study showed the offline performance of amputees was promising and comparable to healthy subjects, with mean F1 scores of over 90% for the "Hand/wrist gestures classifier" and 95% for the force classifiers, implemented with the three algorithms with features extraction (FE). Another significant finding of this study was the feasibility of using the hierarchical classification strategy for real-time applications, due to its ability to provide a response time of 100 ms while maintaining an average online accuracy of above 90%. Discussion: A possible solution for real-time control of both hand/wrist gestures and force levels is the combined use of the LR algorithm with FE for the "Hand/wrist gestures classifier", and the NLR with FE for the Spherical and Tip force classifiers.

Manipulation Tasks in Hazardous Environments Using a Teleoperated Robot: A Case Study at CERN.

Remote robotic systems are employed in the CERN accelerator complex to perform different tasks, such as the safe handling of cables and their connectors. Without dedicated control, these kinds of actions are difficult and require the operators' intervention, which is subjected to dangerous external agents. In this paper, two novel modules of the CERNTAURO framework are presented to provide a safe and usable solution for managing optical fibres and their connectors. The first module is used to detect touch and slippage, while the second one is used to regulate the grasping force and contrast slippage. The force reference was obtained with a combination of object recognition and a look-up table method. The proposed strategy was validated with tests in the CERN laboratory, and the preliminary experimental results demonstrated statistically significant increases in time-based efficiency and in the overall relative efficiency of the tasks.

A Classification Method for Workers' Physical Risk.

In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers' risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker's complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy 88.7±7.3% for the model trained with max(HR), std(RR) and std(HR)).

Development and Validation of a System for the Assessment and Recovery of Grip Force Control.

The ability to finely control hand grip forces can be compromised by neuromuscular or musculoskeletal disorders. Therefore, it is recommended to include the training and assessment of grip force control in rehabilitation therapy. The benefits of robot-mediated therapy have been widely reported in the literature, and its combination with virtual reality and biofeedback can improve rehabilitation outcomes. However, the existing systems for hand rehabilitation do not allow both monitoring/training forces exerted by single fingers and providing biofeedback. This paper describes the development of a system for the assessment and recovery of grip force control. An exoskeleton for hand rehabilitation was instrumented to sense grip forces at the fingertips, and two operation modalities are proposed: (i) an active-assisted training to assist the user in reaching target force values and (ii) virtual reality games, in the form of tracking tasks, to train and assess the user's grip force control. For the active-assisted modality, the control of the exoskeleton motors allowed generating additional grip force at the fingertips, confirming the feasibility of this modality. The developed virtual reality games were positively accepted by the volunteers and allowed evaluating the performance of healthy and pathological users.

Evaluation of the effects of focused ultrasound stimulation on the central nervous system through a multiscale simulation approach.

The lack of sensory feedback represents one of the main drawbacks of commercial upper limb prosthesis. Transcranial Focused Ultrasound Stimulation (tFUS) seems to be a valid non-invasive technique for restoring sensory feedback allowing to deliver acoustic energy to cortical sensory areas with high spatial resolution and depth penetration. This paper aims at studying in simulation the use of tFUS on cortical sensory areas to evaluate its effects in terms of latency ad firing rate of the cells response, for understanding if these parameters influence the safety and the efficacy of the stimulation. In this paper, in order to study the propagation of the ultrasound wave from the transducer to the cortical cells, a multiscale approach was implemented by building a macroscopic model, which estimates the pressure profile in a simplified 2D human head geometry, and coupling it with the SONIC microscale model, that describes the electrical behaviour of a cortical neuron. The influence of the stimulation parameters and of the skull thickness on the latency and the firing rate are evaluated and the obtained behaviour is linked to the sensory response obtained on human subjects. Results have shown that slight changes in the transducer position should not affect the efficacy of the stimulation; however, high skull thickness leads to lower cells activation. These results will be useful for evaluating safety and effectiveness of tFUS for sensory feedback in closed-loop prosthetic systems.

A user-friendly automatic toolbox for hand kinematic analysis, clinical assessment and postural synergies extraction.

The clinical assessment of the human hand is typically conducted through questionnaires or tests that include objective (e.g., time) and subjective (e.g., grasp quality) outcome measures. However, there are other important indicators that should be considered to quantify grasp and movement quality in addition to the time needed by a subject to execute a task, and this is essential for human and artificial hands that attempt to replicate the human hand properties. The correct estimation of hand kinematics is fundamental for computing these indicators with high fidelity, and a technical background is typically required to perform this analysis. In addition, to understand human motor control strategies as well as to replicate them on artificial devices, postural synergies were widely explored in recent years. Synergies should be analyzed not only to investigate possible modifications due to musculoskeletal and/or neuromuscular disorders, but also to test biomimetic hands. The aim of this work is to present an open source toolbox to perform all-in-one kinematic analysis and clinical assessment of the hand, as well as to perform postural synergies extraction. In the example provided in this work, the tool takes as input the position of 28 retroreflective markers with a diameter of 6 mm, positioned on specific anatomical landmarks of the hand and recorded with an optoelectronic motion capture system, and automatically performs 1) hand kinematic analysis (i.e., computation of 23 joint angles); 2) clinical assessment, by computing indicators that allow quantifying movement efficiency (Peak Grip Aperture), smoothness (Normalized Dimensionless Jerk Grasp Aperture) and speed (Peak Velocity of Grasp Aperture), planning capabilities (Time to Peak Grip Aperture), spatial posture (Wrist and Finger Joint Angles) and grasp stability (Posture of Hand Finger Joints), and 3) postural synergies extraction and analysis through the Pareto, Scree and Loadings plots. Two examples are described to demonstrate the applicability of the toolbox: the first one aiming at performing a clinical assessment of a volunteer and the second one aiming at extracting and analyzing the volunteer's postural synergies. The tool allows calculating joint angles with high accuracy (reconstruction errors below 4 mm and 3.2 mm for the fingers and wrist respectively) and automatically performing clinical assessment and postural synergies extraction. Results can be visually inspected, and data can be saved for any desired post processing analysis. Custom-made protocols to extract joint angles, based on different markersets, could be also integrated in the toolbox. The tool can be easily exploitable in clinical contexts, as it does not require any particular technical knowledge to be used, as confirmed by the usability evaluation conducted (perceived usability = 94.2 ± 5.4). In addition, it can be integrated with the SynGrasp toolbox to perform grasp analysis of underactuated virtual hands based on postural synergies.

Multiscale approach for tFUS neurocomputational modelling.

Among the non-invasive methods employed for brain stimulation, trans cranial Focused Ultrasound Stimulation (tFUS) is the technique with the best penetration into the tissues and spatial resolution. The development of computational models of US propagation in brain tissue can be useful for estimating the behaviour of neural cells subjected to mechanical stimulus due to US. This paper aims at studying the neural cell response of a cortical Regular Spiking point neuron model, for different values of stimulus Duty Cycle (DC). The main goal is to use a multiscale approach to couple the results obtained from a macroscale simulation on wave propagation in tissue, with neuron model described by Hodgkin-Huxley equations to study latency and firing rate of the RS model. The obtained results showed that latency and firing rate have slight variations along the propagation direction of the US beam, in the focal region under the skull model, for different stimulus DC.

Interventions to prevent and reduce work-related musculoskeletal injuries and pain among healthcare professionals. A comprehensive systematic review of the literature.

INTRODUCTION: Work-related musculoskeletal disorders (WMSDs) are among the main causes of injury and pain in healthcare professionals. Previous reviews provided a fragmented view of the interventions available for WMSDs. This review aims to provide a comprehensive description of interventions for preventing and reducing work-related musculoskeletal injuries and/or pain among healthcare professionals, and to assess the methodological quality of studies. METHODS: A systematic literature review was performed, based on the Effective Public Health Practice Project process. A comprehensive search was conducted on six peer-reviewed databases and manually. The methodological quality of the studies included was rated as weak, moderate, or strong. The studies were organized based on the 2019 classification of the interventions by Oakman and colleagues. RESULTS: Twenty-seven articles were included reporting individual (n = 4), task-specific (n = 4), work organization and job design (n = 2), work environment (n = 1), and multifactorial (n = 16) interventions. Overall quality rating was strong for 6 studies, moderate for 16, and weak for 5. Individual interventions such as neuromuscular and physical exercise were effective in reducing pain. Task-specific and work organization interventions could prevent certain injuries. Significant reduction of both injuries and pain resulted from multifactorial interventions, which were reported by the majority of strong (n = 5) and moderate (n = 10) quality articles. CONCLUSIONS: This review provides healthcare professionals with evidence-based information to plan interventions targeted towards reducing WMSDs. In particular, more efforts are needed to implement and extend effective multifactorial interventions. Moreover, studies about each professional healthcare target group are needed. PRACTICAL APPLICATION: Our results can guide policy-makers, healthcare managers and professionals to choose the best strategies to prevent and reduce WMSDs and to shape continuous education programs. This study prompts clinicians to develop inter-professional collaborations and to practice physical activities in order to reduce WMSDs.