Measuring mental workload in assistive wearable devices: a review (original) (raw)
Related papers
2017
Wearable devices have gained high popularity in the last years, especially for health monitoring. Some devices aim at identifying mental states, but scientific studies on the potential of wearable devices for identifying mental states are rather sparse. Heart rate parameters proved to be valuable indicators for increasing mental workload and growing levels of physical activity. The question arises, if wearable devices can be used to identify high mental workload in different physiological activity conditions. Thirty-two participants (18 female) participated in an experiment with a 2 (mental workload) x 4 (physiological activity) factorial within-subject design. Participants sat, stood, stepped or cycled while they fulfilled either no secondary task (5 minutes) or a counting backwards task (5 minutes). Heart Rate was measured via a wrist-worn mobile device and a stationary device. Results showed that measurements of the two devices did not correlate consistently. Heart Rate and Inter-Beat Intervals, measured via the stationary device differed significantly with varying levels of physical activity and mental workload. Data from the wearable device showed only the physical activity effect. Findings indicate that wearable devices are not fully capable of identifying mental workload. Still, wearable devices have potential for identifying and fostering reduction of high physical load in everyday usage.
Sensors
The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states c...
Evaluation of Subjective and EEG-Based Measures of Mental Workload
Communications in Computer and Information Science, 2013
Assessment of mental workload is an important aspect of many human factors and HCI applications. Not surprisingly, a number of workload measures have been proposed. This study examined the sensitivity, convergent and concurrent validity of several subjective self-report and EEG workload measures. Most measures displayed adequate sensitivity to task difficulty manipulations, but relatively modest convergent and concurrent validity. Overall, we believe these result serve to aid human factors practitioners in selecting measures of workload for varied applications.
State of science: mental workload in ergonomics
Ergonomics, 2015
Mental workload (MWL) is one of the most widely used concepts in ergonomics and human factors and represents a topic of increasing importance. Since modern technology in many working environments imposes ever more cognitive demands upon operators while physical demands diminish, understanding how MWL impinges on performance is increasingly critical. Yet, MWL is also one of the most nebulous concepts, with numerous definitions and dimensions associated with it. Moreover, MWL research has had a tendency to focus on complex, often safety-critical systems (e.g. transport, process control). Here we provide a general overview of the current state of affairs regarding the understanding, measurement and application of MWL in the design of complex systems over the last three decades. We conclude by discussing contemporary challenges for applied research, such as the interaction between cognitive workload and physical workload, and the quantification of workload 'redlines' which speci...
Measuring mental workload using physiological measures: A systematic review
Applied Ergonomics
Technological advances have led to physiological measurement being increasingly used to measure, and predict operator states. Mental workload (MWL) in particular has been characterised using a variety of physiological sensor data. This state of the art review contributes a synthesis of the literature. We present a systematic review of 58 peer reviewed journal articles which present original data using primarily peripheral nervous system (PNS) measures to include electrocardiographic, blood pressure, respiratory, ocular and dermal sensors. In addition, electroencephalographic measures have been included if they are presented with a PNS measure. The literature reviewed covers a wide range of applied and experimental studies across various domains, with aviation being highly represented in the sample of applied literature reviewed. We present a summary of the six measures and provide a high level evidence base including how to deploy each measure, and characteristics that can affect or preclude a measure from use in a study. Measures can be used to discriminate differences in MWL caused by task type, task load, and in some cases task difficulty. In addition there is varying ranges of sensitivity to sudden or gradual changes in taskload across the six measures. We conclude that there is no single measure that clearly discriminates mental workload but there is a growing empirical basis with which to inform both science and practice.
Improving Upper-limb Prosthesis Usability: Cognitive Workload Measures Quantify Task Difficulty
medRxiv (Cold Spring Harbor Laboratory), 2022
Providing user-focused, objective, and quantified metrics for prosthesis usability may help reduce the high (up to 50%) abandonment rates and accelerate the clinical adoption and cost reimbursement for new and improved prosthetic systems. We comparatively evaluated several physiological, behavioral, and subjective cognitive workload measures applied to upper-limb neuroprosthesis use. Users controlled a virtual prosthetic arm via surface electromyography (sEMG) and completed a virtual target control task at easy and hard levels of difficulty (with large and small targets, respectively). As indices of cognitive workload, we took behavioral (Detection Response Task; DRT) and. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
Applied Sciences
Robotic exoskeleton technologies are applied in the medical field to help patients with impaired mobility to recover their motor functions. Relevant literature shows that usability and cognitive workload may influence the patients’ likelihood to benefit from the use of rehabilitative exoskeletons. Following the PRISMA method, the present study aimed to systematically review the assessment methods of usability and cognitive workload in the use of exoskeletal devices for motor rehabilitation. The literature search was conducted in the Scopus and Web of Science bibliographical databases, using 16 keywords that were combined into one search query. A final sample of 23 articles was included in the review, from which 18 distinct assessment methods were identified. Of them, 15 aimed to assess usability, whereas 3 aimed to assess cognitive workload in the use of rehabilitative exoskeletons. Some of the identified methods (e.g., SUS, QUEST, SWAT, and NASA-TLX) showed good psychometric proper...