Exploring technology acceptance of head-mounted device-based augmented reality surgical navigation in orthopaedic surgery

BACKGROUND

This study used the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the acceptance of HMD-based AR surgical navigation.

METHODS

An experiment was conducted in which participants drilled 12 predefined holes using freehand drilling, proprioceptive control, and AR assistance. Technology acceptance was assessed through a survey and non-participant observations.

RESULTS

Participants' intention to use AR correlated (p < 0.05) with social influence (Spearman's rho (rs) = 0.599), perceived performance improvement (rs = 0.592) and attitude towards AR (rs = 0.542).

CONCLUSIONS

While most participants acknowledged the potential of AR, they also highlighted persistent barriers to adoption, such as issues related to user-friendliness, time efficiency and device discomfort. To overcome these challenges, future AR surgical navigation systems should focus on enhancing surgical performance while minimising disruptions to workflows and operating times. Engaging orthopaedic surgeons in the development process can facilitate the creation of tailored solutions and accelerate adoption.

Interdisciplinary evaluation of a robot physically collaborating with workers

Collaborative Robots-CoBots-are emerging as a promising technological aid for workers. To date, most CoBots merely share their workspace or collaborate without contact, with their human partners. We claim that robots would be much more beneficial if they physically collaborated with the worker, on high payload tasks. To move high payloads, while remaining safe, the robot should use two or more lightweight arms. In this work, we address the following question: to what extent can robots help workers in physical human-robot collaboration tasks? To find an answer, we have gathered an interdisciplinary group, spanning from an industrial end user to cognitive ergonomists, and including biomechanicians and roboticists. We drew inspiration from an industrial process realized repetitively by workers of the SME HANKAMP (Netherlands). Eleven participants replicated the process, without and with the help of a robot. During the task, we monitored the participants' biomechanical activity. After the task, the participants completed a survey with usability and acceptability measures; seven workers of the SME completed the same survey. The results of our research are the following. First, by applying-for the first time in collaborative robotics-Potvin's method, we show that the robot substantially reduces the participants' muscular effort. Second: we design and present an unprecedented method for measuring the robot reliability and reproducibility in collaborative scenarios. Third: by correlating the worker's effort with the power measured by the robot, we show that the two agents act in energetic synergy. Fourth: the participant's increasing level of experience with robots shifts his/her focus from the robot's overall functionality towards finer expectations. Last but not least: workers and participants are willing to work with the robot and think it is useful.

The Dynamic Adoption Journey: A Typology for Users and Non-Users of Occupational Exoskeletons

Various barriers prevent the adoption of occupational exoskeletons. It is therefore important to understand why some people are willing to use occupational exoskeletons, while others are not. To identify why people use or do not use exoskeletons, we created a typology describing different types of users and non-users. These types were created based on existing literature on internet adoption and social robots. Next, literature and empirical data were used to identify reasons why some people use exoskeletons and others do not use them (yet). The typology includes users with pain and users without work-related musculoskeletal disorders, but also non-users: resisters, rejecters, discontinuers, excluded or expelled non-users. It can be used by companies interested in implementing exoskeletons to identify possible early adopters. For exoskeleton designers, it can be used as a tool to identify non-users and focus on design strategies to enable non-users to become users (such as making exoskeletons that would fit people with a wide range of body shapes). Future research can use these types to identify users and non-users in field trials or pilot projects.

Does a Soft Actuated Back Exosuit Influence Multimodal Physiological Measurements and User Perception During an Industry Inspired Task?

Back support soft exosuits are promising solutions to reduce risk of musculoskeletal injuries at workplaces resulting from physically demanding and repetitive lifting tasks. Design of novel active exosuits address the impact on the muscle activity and metabolic costs but do not consider other critical aspects such as comfort and user perception during the intended tasks. Thus, in this study, we describe a novel soft active exosuit in line with its impact on physiological and subjective measures during lifting. We tested four healthy participants who performed repetitive lifting tasks with and without this exosuit. The exosuit provided assistance proportional to the lumbar flexion angle measured using an inertial measurement unit. We measured the participant's multimodal physiological measures including surface electromyography, metabolic cost, heart rate, and skin temperature. We also measured subjective scores on user exertion, task load, and device acceptability. All participants perceived a reduction in task load when using the exosuit. Three participants showed reduction of muscle activity for the erector spinae muscles. The metabolic costs and heart rate reserve reduced for two participants, with similar trends for skin temperature. For future development of workplace exosuits, we recommend incorporating assessments of both physiological and subjective measures, considering the user-dependent response to the exosuit.

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.

The effect of augmented reality on the accuracy and learning curve of external ventricular drain placement

OBJECTIVE

The traditional freehand technique for external ventricular drain (EVD) placement is most frequently used, but remains the primary risk factor for inaccurate drain placement. As this procedure could benefit from image guidance, the authors set forth to demonstrate the impact of augmented-reality (AR) assistance on the accuracy and learning curve of EVD placement compared with the freehand technique.

METHODS

Sixteen medical students performed a total of 128 EVD placements on a custom-made phantom head, both before and after receiving a standardized training session. They were guided by either the freehand technique or by AR, which provided an anatomical overlay and tailored guidance for EVD placement through inside-out infrared tracking. The outcome was quantified by the metric accuracy of EVD placement as well as by its clinical quality.

RESULTS

The mean target error was significantly impacted by either AR (p = 0.003) or training (p = 0.02) in a direct comparison with the untrained freehand performance. Both untrained (11.9 ± 4.5 mm) and trained (12.2 ± 4.7 mm) AR performances were significantly better than the untrained freehand performance (19.9 ± 4.2 mm), which improved after training (13.5 ± 4.7 mm). The quality of EVD placement as assessed by the modified Kakarla scale (mKS) was significantly impacted by AR guidance (p = 0.005) but not by training (p = 0.07). Both untrained and trained AR performances (59.4% mKS grade 1 for both) were significantly better than the untrained freehand performance (25.0% mKS grade 1). Spatial aptitude testing revealed a correlation between perceptual ability and untrained AR-guided performance (r = 0.63).

CONCLUSIONS

Compared with the freehand technique, AR guidance for EVD placement yielded a higher outcome accuracy and quality for procedure novices. With AR, untrained individuals performed as well as trained individuals, which indicates that AR guidance not only improved performance but also positively impacted the learning curve. Future efforts will focus on the translation and evaluation of AR for EVD placement in the clinical setting.

Augmented Reality-Assisted Neurosurgical Drain Placement (ARANED): Technical Note

BACKGROUND

Many surgical procedures, such as placement of intracranial drains, are currently being performed blindly, relying on anatomical landmarks. As a result, accuracy results still have room for improvement. Neuronavigation could address this issue, but its application in an urgent setting is often impractical. Augmented reality (AR) provided through a head-worn device has the potential to tackle this problem, but its implementation should meet physicians' needs.

METHODS

The Surgical Augmented Reality Assistance (SARA) project aims to develop an AR solution that is suitable for preoperative planning, intraoperative visualisation and navigational support in an everyday clinical setting, using a Microsoft HoloLens.

RESULTS

Proprietary hardware and software adaptations and dedicated navigation algorithms are applied to the Microsoft HoloLens to optimise it specifically for neurosurgical navigation. This includes a pipeline with an additional set of advanced, semi-automated algorithms responsible for image processing, hologram-to-patient registration and intraoperative tracking using infrared depth-sensing. A smooth and efficient workflow while maintaining high accuracy is prioritised. The AR solution provides a fully integrated and completely mobile navigation setup. Initial preclinical and clinical validation tests applying the solution to intracranial drain placement are described.

CONCLUSION

AR has the potential to vastly increase accuracy of everyday procedures that are frequently performed without image guidance, but could still benefit from navigational support, such as intracranial drain placements. Technical development should go hand in hand with preclinical and clinical validation in order to demonstrate improvements in accuracy and clinical outcomes.

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.