A tool for measuring mental workload during prosthesis use: The Prosthesis Task Load Index (PROS-TLX)

Parr, Johnny V. V.; Galpin, Adam; Uiga, Liis; Marshall, Benjamin; Wright, David J.; Franklin, Zoë C.; Wood, Greg

doi:10.1371/journal.pone.0285382

Cited by 7 publications

(5 citation statements)

References 29 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, reminding participants of their first-session scores immediately prior to their second session survey completion, might address expectation-related variability in anchor scores [74]. After this current study was conducted, a new Prosthesis Task Load Index (PROS-TLX) was developed and validated [75], and should be considered in future comparative prosthesis control research.…”

Section: Findings From User Experience Metricsmentioning

confidence: 99%

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

Williams,

Shehata,

Cheng

et al. 2023

Preprint

View full text Add to dashboard Cite

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop user-specific prosthesis controllers that can predict a person’s intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users’ intentions; the timing of specific wrist and hand control functions; and users’ opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details—spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the “limb position effect”. We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

show abstract

Section: Findings From User Experience Metricsmentioning

confidence: 99%

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

Williams,

Shehata,

Cheng

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Our earlier research, which employed the same surveys, offered other important considerations—that users’ perceptions of poor or diminished control between sessions might change over time, and that reminders of earlier session control perceptions would have helped to establish first-session anchor scores [42]. This work concurs with these considerations and recommends that the Prosthesis Task Load Index (PROS-TLX) [43] be considered in future comparative prosthesis control research.…”

Section: Discussionmentioning

confidence: 71%

Myoelectric Prosthesis Control using Recurrent Convolutional Neural Network Regression Mitigates the Limb Position Effect

Williams,

Shehata,

Cheng

et al. 2024

Preprint

View full text Add to dashboard Cite

Although state-of-the-art myoelectric prostheses offer persons with upper limb amputation extensive movement capabilities, users have not been afforded a reliable means to control common movements required in daily living. Many proposed prosthesis controllers use pattern recognition, a method that learns and recognizes patterns of electromyographic (EMG) signals produced by the user's residual limb muscles to predict and execute device movements. Such control becomes unreliable in high limb positions---a problem known as the limb position effect. Pattern recognition often uses a classification algorithm; simple to implement, but limits user-initiated control to only one device movement at a time, at a single speed. To combat position-related control deficiencies and classification controller constraints, we developed and tested two recurrent convolutional neural network (RCNN) pattern recognition-based solutions: (1) an RCNN classification controller that uses EMG plus positional inertial measurement unit (IMU) signals to offer one-speed, sequential movement control; and (2) an RCNN regression controller that uses the same data capture technique to offer simultaneous control of multiple movements and device movement velocity. We assessed both RCNN controllers by comparing them to a commonly used linear discriminant analysis classification controller (LDA-Baseline). Participants without upper limb impairment were recruited to perform multipositional tasks while wearing a simulated prosthesis. Both RCNN classification and regression controllers showed improved functional task performance over LDA-Baseline, in 11 and 38 out of 115 metrics, respectively. This work contributes an RCNN regression-based controller that provides accurate, simultaneous, and proportional movements to EMG-based technologies including prostheses, exoskeletons, and even muscle-activated video games.

show abstract

“…Furthermore, after testing for the first time each control (excluding the familiarisation phases), qualitative feedback from participants about their experiences with each control strategy was assessed through two surveys, the System Usability Scale (SUS) [30], [31] and the first part of the Prosthesis Task Load indeX (rawProsTLX) [32], to evaluate usability and cognitive load, respectively. These surveys allow for a more comprehensive understanding of how the users perceive the control system, the mental effort required to use it, and their overall satisfaction with its usability.…”

Section: Resultsmentioning

confidence: 99%

Bioinspired head-to-shoulder reference frame transformation for movement-based arm prosthesis control

Lento,

Leconte,

Bardisbanian

et al. 2024

Preprint

View full text Add to dashboard Cite

Current myoelectric controls of multiple-joint arm prostheses are not satisfactory, and alternatives based on natural arm coordination are being increasingly explored. We recently showed that adding movement goals to shoulder information enabled Artificial Neural Networks (ANNs), trained on natural arm movements, to predict distal joints so well that transhumeral amputees could reach as with their valid arm in Virtual Reality (VR). Yet, this control relies on the position and orientation of the object to reach expressed in a reference frame attached to the shoulder, whereas it might only be available in a head-centered reference frame through gaze-guided computer vision. Here, we designed two methods to perform the required transformation from incomplete, orientation-only data from head and shoulder, possibly available in real life with Inertial Measurement Units (IMUs). The first involved an ANN trained offline to do this transformation, while the second was based on a bioinspired space map with online adaptation. Experimental results on twelve intact-limbs participants controlling a prosthesis avatar in VR demonstrated persistent errors with the first method, which could be quickly absorbed with the second method. The effectiveness of this second bioinspired and adaptive method was also tested on six participants with transhumeral limb loss in VR, and a physical proof of concept was implemented on a teleoperated robotic platform with computer vision. Those advances represent necessary steps toward the deployment of movement-based control in real-life scenarios.

show abstract

A tool for measuring mental workload during prosthesis use: The Prosthesis Task Load Index (PROS-TLX)

Cited by 7 publications

References 29 publications

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

Myoelectric Prosthesis Control using Recurrent Convolutional Neural Network Regression Mitigates the Limb Position Effect

Bioinspired head-to-shoulder reference frame transformation for movement-based arm prosthesis control

Contact Info

Product

Resources

About