Heuristic Usability Evaluation of Wearable Mental State Monitoring Sensors for Healthcare Environments

Leape, Charlotte P.; Fong, Allan; Ratwani, Raj M.

doi:10.1177/1541931213601134

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Being able to link those signals back to specific emotions, among a possible myriad of other movements and states, may simply have been too complex. Although some basis for the wearable device was provided by the company [87,88], these papers did not constitute an actual validation of the algorithms of the device. Unfortunately, we were unable to obtain the algorithms and, as such, the scientific contribution of our study is more utilitarian than mechanistic.…”

Section: Potential Device Effectivenessmentioning

confidence: 99%

Feasibility of using a biofeedback device in mindfulness training - a pilot randomized controlled trial

Lin

Prickett

Woltering

2021

Pilot Feasibility Stud

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Background Stress can negatively impact an individual’s health and well-being and high levels of stress are noted to exist among college students today. While traditional treatment methods are plagued with stigma and transfer problems, newly developed wearable biofeedback devices may offer unexplored possibilities. Although these products are becoming commonplace and inexpensive, scientific evidence of the effectiveness of these products is scarce and their feasibility within research contexts are relatively unexplored. Conversely, companies are not required, and possibly reluctant, to release information on the efficacy of these products against their claims. Thus, in the present pilot, we assess the feasibility of using a real-time respiratory-based biofeedback device in preparation for a larger study. Our main aims were to assess device-adherence and collaboration with the company that develops and sells the device. Method Data were collected from 39 college students who self-identified as experiencing chronic stress at a Southwestern university in the USA. Students were randomized into either a mindfulness-only control group without a biofeedback device (n = 21), or an experimental group with biofeedback device (n = 18). Both groups received mindfulness meditation training. Pre-test and post-test procedures were conducted 2 weeks apart. Further, both participant compliance and company compliance were assessed and collaboration with the company was evaluated. Results Participant device-adherence as well as the company’s collaboration necessary for a full-scale study was determined to be low. This may also have affected our results which showed a strong main effect for time for all outcome variables, suggesting all groups showed improvement in their levels of stress after the intervention period. No group by time effects were identified, however, indicating no added benefit of the biofeedback device. Conclusions Our findings suggest feasibility of future studies requires full collaboration and detailed and agreed upon data sharing procedures with the biofeedback company. The particular device under investigation added no value to the intervention outcomes and it was not feasible to continue a larger-scale study. Further, as the technology sector is innovating faster than it can validate products, we urge for open science collaborations between public and private sectors to properly develop evidence-based regulations that can withstand technological innovation while maintaining product quality, safety, and effectiveness. Trial registration NCT02837016. Registered 19 July 2016.

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Section: Potential Device Effectivenessmentioning

confidence: 99%

Feasibility of using a biofeedback device in mindfulness training - a pilot randomized controlled trial

Lin

Prickett

Woltering

2021

Pilot Feasibility Stud

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“…These studies found that, while mobile sensors are not as accurate as a wired and full-scale EEG device, they can be used in noncritical applications. Some recent studies into the benefits of mobile EEG sensors on different domains have demonstrated an acceptable reliability (Leape, Fong, & Ratwani, 2016;Lushin & others, 2016;Wu, Wei, & Tudor, 2017;F. Zhang et al, 2017).…”

Section: Towards the Use Of Mobile Eeg Sensors For Real World Applicamentioning

confidence: 99%

Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors

Nakisa

Rastgoo

Tjondronegoro

et al. 2018

Expert Systems with Applications

194

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There is currently no standard or widely accepted subset of features to effectively classify different emotions based on electroencephalogram (EEG) signals. While combining all possible EEG features may improve the classification performance, it can lead to high dimensionality and worse performance due to redundancy and inefficiency. To solve the high-dimensionality problem, this paper proposes a new framework to automatically search for the optimal subset of EEG features using evolutionary computation (EC) algorithms. The proposed framework has been extensively evaluated using two public datasets (MAHNOB, DEAP) and a new dataset acquired with a mobile EEG sensor. The results confirm that EC algorithms can effectively support feature selection to identify the best EEG features and the best channels to maximize performance over a four-quadrant emotion classification problem. These findings are significant for informing future development of EEG-based emotion classification because low-cost mobile EEG sensors with fewer electrodes are becoming popular for many new applications.

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“…Muse has been validated as a device for conducting Event-Related Potential (ERP) research [24]. This device has been compared with other wearable sensors resulting in high performance in the fields of ease of integration and applied usability [25]. In addition, many other studies have used Muse for several purposes, including brain wave activity detection during training [26], enjoyment evaluation [27], accelerometer measurement of head movement during surgery [28], and concentration and stress measurement during surgery [29].…”

Section: Introductionmentioning

confidence: 99%

MuseStudio: Brain Activity Data Management Library for Low-Cost EEG Devices

2021

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Collecting data allows researchers to store and analyze important information about activities, events, and situations. Gathering this information can also help us make decisions, control processes, and analyze what happens and when it happens. In fact, a scientific investigation is the way scientists use the scientific method to collect the data and evidence that they plan to analyze. Neuroscience and other related activities are set to collect their own big datasets, but to exploit their full potential, we need ways to standardize, integrate, and synthesize diverse types of data. Although the use of low-cost ElectroEncephaloGraphy (EEG) devices has increased, such as those whose price is below 300 USD, their role in neuroscience research activities has not been well supported; there are weaknesses in collecting the data and information. The primary objective of this paper was to describe a tool for data management and visualization, called MuseStudio, for low-cost devices; specifically, our tool is related to the Muse brain-sensing headband, a personal meditation assistant with additional possibilities. MuseStudio was developed in Python following the best practices in data analysis and is fully compatible with the Brain Imaging Data Structure (BIDS), which specifies how brain data must be managed. Our open-source tool can import and export data from Muse devices and allows viewing real-time brain data, and the BIDS exporting capabilities can be successfully validated following the available guidelines. Moreover, these and other functional and nonfunctional features were validated by involving five experts as validators through the DESMET method, and a latency analysis was also performed and discussed. The results of these validation activities were successful at collecting and managing electroencephalogram data.

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Heuristic Usability Evaluation of Wearable Mental State Monitoring Sensors for Healthcare Environments

Cited by 6 publications

References 6 publications

Feasibility of using a biofeedback device in mindfulness training - a pilot randomized controlled trial

Feasibility of using a biofeedback device in mindfulness training - a pilot randomized controlled trial

Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors

MuseStudio: Brain Activity Data Management Library for Low-Cost EEG Devices

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