An industrial exoskeleton user acceptance framework based on a literature review of empirical studies

Studying the acceptance of exoskeletons in industry has gained increased attention. Exoskeletons (wearable support devices) are envisioned to alleviate heavy work. Examining what factors influence the use of exoskeletons is important, because influencing these factors could positively contribute to the adoption of industrial exoskeletons. The factors identified in this paper have been systematically derived from empirical research with (potential future) end users, most of whom have tried on an exoskeleton. Our framework with factors influencing the acceptance of industrial exoskeletons can be used during the (ideally iterative) design, (re)development and evaluation phase of new or existing exoskeletons. This could improve the quality of exoskeletons since this allows designers to already consider acceptance factors early in the design process instead of finding out what is important late in the design process during (field) testing. In turn, this might accelerate the adoption of exoskeletons. Also, our framework can be used to study the ongoing introduction of exoskeletons at work since it also addresses policy decisions companies interested in implementing exoskeletons should consider.

Deep learning for biosignal control: insights from basic to real-time methods with recommendations

Biosignal control is an interaction modality that allows users to interact with electronic devices by decoding the biological signals emanating from the movements or thoughts of the user. This manner of interaction with devices can enhance the sense of agency for users and enable persons suffering from a paralyzing condition to interact with everyday devices that would otherwise be challenging for them to use. It can also improve control of prosthetic devices and exoskeletons by making the interaction feel more natural and intuitive. However, with the current state of the art, several issues still need to be addressed to reliably decode user intent from biosignals and provide an improved user experience over other interaction modalities. One solution is to leverage advances in Deep Learning (DL) methods to provide more reliable decoding at the expense of added computational complexity. This scoping review introduces the basic concepts of DL and assists readers in deploying DL methods to a real-time control system that should operate under real-world conditions. The scope of this review covers any electronic device, but with an emphasis on robotic devices, as this is the most active area of research in biosignal control. We review the literature pertaining to the implementation and evaluation of control systems that incorporate DL to identify the main gaps and issues in the field, and formulate suggestions on how to mitigate them. Additionally, we formulate guidelines on the best approach to designing, implementing and evaluating research prototypes that use DL in their biosignal control systems.

Processing of Self-Healing Polymers for Soft Robotics

Soft robots are, due to their softness, inherently safe and adapt well to unstructured environments. However, they are prone to various damage types. Self-healing polymers address this vulnerability. Self-healing soft robots can recover completely from macroscopic damage, extending their lifetime. For developing healable soft robots, various formative and additive manufacturing methods have been exploited to shape self-healing polymers into complex structures. Additionally, several novel manufacturing techniques, noted as (re)assembly binding techniques that are specific to self-healing polymers, have been created. Herein, the wide variety of processing techniques of self-healing polymers for robotics available in the literature is reviewed, and limitations and opportunities discussed thoroughly. Based on defined requirements for soft robots, these techniques are critically compared and validated. A strong focus is drawn to the reversible covalent and (physico)chemical cross-links present in the self-healing polymers that do not only endow healability to the resulting soft robotic components, but are also beneficial in many manufacturing techniques. They solve current obstacles in soft robots, including the formation of robust multi-material parts, recyclability, and stress relaxation. This review bridges two promising research fields, and guides the reader toward selecting a suitable processing method based on a self-healing polymer and the intended soft robotics application.

Improved Motion Classification With an Integrated Multimodal Exoskeleton Interface

Human motion intention detection is an essential part of the control of upper-body exoskeletons. While surface electromyography (sEMG)-based systems may be able to provide anticipatory control, they typically require exact placement of the electrodes on the muscle bodies which limits the practical use and donning of the technology. In this study, we propose a novel physical interface for exoskeletons with integrated sEMG- and pressure sensors. The sensors are 3D-printed with flexible, conductive materials and allow multi-modal information to be obtained during operation. A K-Nearest Neighbours classifier is implemented in an off-line manner to detect reaching movements and lifting tasks that represent daily activities of industrial workers. The performance of the classifier is validated through repeated experiments and compared to a unimodal EMG-based classifier. The results indicate that excellent prediction performance can be obtained, even with a minimal amount of sEMG electrodes and without specific placement of the electrode.

Fabrication of a Soft Robotic Gripper With Integrated Strain Sensing Elements Using Multi-Material Additive Manufacturing

With the purpose of making soft robotic structures with embedded sensors, additive manufacturing techniques like fused deposition modeling (FDM) are popular. Thermoplastic polyurethane (TPU) filaments, with and without conductive fillers, are now commercially available. However, conventional FDM still has some limitations because of the marginal compatibility with soft materials. Material selection criteria for the available material options for FDM have not been established. In this study, an open-source soft robotic gripper design has been used to evaluate the FDM printing of TPU structures with integrated strain sensing elements in order to provide some guidelines for the material selection when an elastomer and a soft piezoresistive sensor are combined. Such soft grippers, with integrated strain sensing elements, were successfully printed using a multi-material FDM 3D printer. Characterization of the integrated piezoresistive sensor function, using dynamic tensile testing, revealed that the sensors exhibited good linearity up to 30% strain, which was sufficient for the deformation range of the selected gripper structure. Grippers produced using four different TPU materials were used to investigate the effect of the Shore hardness of the TPU on the piezoresistive sensor properties. The results indicated that the in situ printed strain sensing elements on the soft gripper were able to detect the deformation of the structure when the tentacles of the gripper were open or closed. The sensor signal could differentiate between the picking of small or big objects and when an obstacle prevented the tentacles from opening. Interestingly, the sensors embedded in the tentacles exhibited good reproducibility and linearity, and the sensitivity of the sensor response changed with the Shore hardness of the gripper. Correlation between TPU Shore hardness, used for the gripper body and sensitivity of the integrated in situ strain sensing elements, showed that material selection affects the sensor signal significantly.

Human Musculoskeletal and Energetic Adaptations to Unilateral Robotic Knee Gait Assistance

OBJECTIVE

This paper aims to analyse the human musculoskeletal and energetic adaptation mechanisms when physically interacting with a unilateral knee orthosis during treadmill walking.

METHODS

Test subjects participated in two walking trials, whereby the orthosis was controlled to deliver five predefined torque profiles of different duration (as % of a gait cycle). The adaptations to assistive torques of different duration were analysed in terms of gait parameters, metabolic effort, and muscle activity.

RESULTS

Orthotic assistances kinematic effects remain local to the assisted leg and joint, unlike the muscles spanning the knee joint, which engage in a balancing-out action to retain stability. Duration of assistive torque significantly affects only the timing of the knee joints peak flexion angle in the stance phase, while the observed joint kinematics and muscle activity demonstrate different recovery times upon changing robotic support (washout effects).

CONCLUSION

Human body adaptations to external robotic knee joint assistance during walking take place on multiple levels and to a different extent in a joint effort to keep the gait stable.

SIGNIFICANCE

This paper provides important insights into the human bodys multiple adaptation mechanisms in the presence of external robotic assistance.

Prevalence and incidence of work-related musculoskeletal disorders in secondary industries of 21st century Europe: a systematic review and meta-analysis

OBJECTIVE

Over the course of the twenty-first century, work-related musculoskeletal disorders are still persisting among blue collar workers. At present, no epidemiological overview exists. Therefore, a systematic review and meta-analysis was performed on the epidemiology of work-related musculoskeletal disorders (WMSD) within Europe's secondary industries.

METHODS

Five databases were screened, yielding 34 studies for the qualitative analysis and 17 for the quantitative analysis. Twelve subgroups of WMSDs were obtained for the meta-analysis by means of predefined inclusion criteria: back (overall), upper back, lower back, neck, shoulder, neck/shoulder, elbow, wrist/hand, leg (overall), hip, knee, and ankle/feet.

RESULTS

The most prevalent WMSDs were located at the back (overall), shoulder/neck, neck, shoulder, lower back and wrist WMSDs with mean 12-month prevalence values of 60, 54, 51, 50, 47, and 42%, respectively. The food industry was in the majority of subgroups the most prominent researched sector and was frequently associated with high prevalence values of WMSDs. Incidence ratios of upper limb WMSDs ranged between 0.04 and 0.26. Incidence ratios could not be calculated for other anatomical regions due to the lack of sufficient articles.

CONCLUSION

WMSDs are still highly present among blue collar workers. Relatively high prevalence values and low incidence ratios indicate a limited onset of WMSDs with however long-term complaints.

Invariant Set Distributed Explicit Reference Governors for Provably Safe On-Board Control of Nano-Quadrotor Swarms

This article provides a theory for provably safe and computationally efficient distributed constrained control, and describes an application to a swarm of nano-quadrotors with limited on-board hardware and subject to multiple state and input constraints. We provide a formal extension of the explicit reference governor framework to address the case of distributed systems. The efficacy, robustness, and scalability of the proposed theory is demonstrated by an extensive experimental validation campaign and a comparative simulation study on single and multiple nano-quadrotors. The control strategy is implemented in real-time on-board palm-sized unmanned erial vehicles, and achieves safe swarm coordination without relying on any offline trajectory computations.

How using brain-machine interfaces influences the human sense of agency

Brain-machine interfaces (BMI) allows individuals to control an external device by controlling their own brain activity, without requiring bodily or muscle movements. Performing voluntary movements is associated with the experience of agency ("sense of agency") over those movements and their outcomes. When people voluntarily control a BMI, they should likewise experience a sense of agency. However, using a BMI to act presents several differences compared to normal movements. In particular, BMIs lack sensorimotor feedback, afford lower controllability and are associated with increased cognitive fatigue. Here, we explored how these different factors influence the sense of agency across two studies in which participants learned to control a robotic hand through motor imagery decoded online through electroencephalography. We observed that the lack of sensorimotor information when using a BMI did not appear to influence the sense of agency. We further observed that experiencing lower control over the BMI reduced the sense of agency. Finally, we observed that the better participants controlled the BMI, the greater was the appropriation of the robotic hand, as measured by body-ownership and agency scores. Results are discussed based on existing theories on the sense of agency in light of the importance of BMI technology for patients using prosthetic limbs.

An Autonomous Cognitive Empathy Model Responsive to Users’ Facial Emotion Expressions

Successful social robot services depend on how robots can interact with users. The effective service can be obtained through smooth, engaged, and humanoid interactions in which robots react properly to a user’s affective state. This article proposes a novel Automatic Cognitive Empathy Model, ACEM, for humanoid robots to achieve longer and more engaged human-robot interactions (HRI) by considering humans’ emotions and replying to them appropriately. The proposed model continuously detects the affective states of a user based on facial expressions and generates desired, either parallel or reactive, empathic behaviors that are already adapted to the user’s personality. Users’ affective states are detected using a stacked autoencoder network that is trained and tested on the RAVDESS dataset.

The overall proposed empathic model is verified throughout an experiment, where different emotions are triggered in participants and then empathic behaviors are applied based on proposed hypothesis. The results confirm the effectiveness of the proposed model in terms of related social and friendship concepts that participants perceived during interaction with the robot.

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.

The DREAM Dataset: Supporting a data-driven study of autism spectrum disorder and robot enhanced therapy

We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children's behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general.

Walking with a powered ankle-foot orthosis: the effects of actuation timing and stiffness level on healthy users

BACKGROUND

In the last decades, several powered ankle-foot orthoses have been developed to assist the ankle joint of their users during walking. Recent studies have shown that the effects of the assistance provided by powered ankle-foot orthoses depend on the assistive profile. In compliant actuators, the stiffness level influences the actuator's performance. However, the effects of this parameter on the users has not been yet evaluated. The goal of this study is to assess the effects of the assistance provided by a variable stiffness ankle actuator on healthy young users. More specifically, the effect of different onset times of the push-off torque and different actuator's stiffness levels has been investigated.

METHODS

Eight healthy subjects walked with a unilateral powered ankle-foot orthosis in several assisted walking trials. The powered orthosis was actuated in the sagittal plane by a variable stiffness actuator. During the assisted walking trials, three different onset times of the push-off assistance and three different actuator's stiffness levels were used. The metabolic cost of walking, lower limb muscles activation, joint kinematics, and gait parameters measured during different assisted walking trials were compared to the ones measured during normal walking and walking with the powered orthosis not providing assistance.

RESULTS

This study found trends for more compliant settings of the ankle actuator resulting in bigger reductions of the metabolic cost of walking and soleus muscle activation in the stance phase during assisted walking as compared to the unassisted walking trial. In addition to this, the study found that, among the tested onset times, the earlier ones showed a trend for bigger reductions of the activation of the soleus muscle during stance, while the later ones led to a bigger reduction in the metabolic cost of walking in the assisted walking trials as compared to the unassisted condition.

CONCLUSIONS

This study presents a first attempt to show that, together with the assistive torque profile, also the stiffness level of a compliant ankle actuator can influence the assistive performance of a powered ankle-foot orthosis.

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.

Social Processes: What Determines Industrial Workers' Intention to Use Exoskeletons?

OBJECTIVE

The aim of this study is to test the unified theory of acceptance and use of technology (UTAUT) model for explaining the intention to use exoskeletons among industrial workers.

BACKGROUND

Exoskeletons could help reduce physical workload and risk for injuries among industrial workers. Therefore, it is crucial to understand which factors play a role in workers' intention to use such exoskeletons.

METHOD

Industrial workers (N = 124) completed a survey on their attitudes regarding the use of exoskeletons at their workplace. Using partial least squares (PLS) path modeling, the UTAUT model and a revised version of the UTAUT model were fitted to these data.

RESULTS

The adapted UTAUT model of Dwivedi et al. (2017) was able to explain up to 75.6% of the variance in intention to use exoskeletons, suggesting a reasonable model fit.

CONCLUSION

The model fit suggests that effort expectancy (how easy it seems to use an exoskeleton) plays an important role in predicting the intention to use exoskeletons. Social influence (whether others think workers should use exoskeletons) and performance expectancy (how useful exoskeletons seem to be for work) play a smaller role in predicting the intention to use.

APPLICATIONS

This research informs companies about the optimal implementation of exoskeletons by improving the determinants of acceptance among their workers.

Additive Manufacturing for Self-Healing Soft Robots

The field of self-healing soft robots was initiated a few years ago. A healing ability can be integrated in soft robots by manufacturing their soft membranes out of synthetic self-healing polymers, more specifically elastomeric Diels-Alder (DA) networks. As such they can recover completely from macroscopic damage, including scratches, cuts, and ruptures. Before this research, these robots were manufactured using a technique named "shaping-through-folding-and-self-healing." This technique requires extensive manual labor, is relatively slow, and does not allow for complex shapes. In this article, an additive manufacturing methodology, fused filament fabrication, is developed for the thermoreversible DA polymers, and the approach is validated on a soft robotic gripper. The reversibility of their network permits manufacturing these flexible self-healing polymers through reactive printing into the complex shapes required in soft robotics. The degree of freedom in the design of soft robotics that this new manufacturing technique offers is illustrated through the construction of adaptive DHAS gripper fingers, based on the design by FESTO. Being constructed out of self-healing soft flexible polymer, the fingers can recover entirely from large cuts, tears, and punctures. This is highlighted through various damage-heal cycles.