Keystroke dynamics Digital biomarkers of stress and alertness

2021 ◽  
Keyword(s):  
Keystroke Dynamics ◽  
Digital Biomarkers

scholarly journals Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study

2020 ◽  
Vol 27 (7) ◽  
pp. 1007-1018 ◽  
Author(s):  
Claudia Vesel ◽  
Homa Rashidisabet ◽  
John Zulueta ◽  
Jonathan P Stange ◽  
Jennifer Duffecy ◽  
...  
Keyword(s):  
Real World ◽  
Time Of Day ◽  
Patient Health Questionnaire ◽  
Health Questionnaire ◽  
Keystroke Dynamics ◽  
Observation Study ◽  
Diurnal Patterns ◽  
Lower Accuracy ◽  
Patient Health ◽  
Digital Biomarkers

Abstract Objective Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. Materials and Methods BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. Results We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P < .001), shorter session duration (P < .001), and lower accuracy (P < .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. Conclusions Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.

scholarly journals Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 835
Author(s):  
Ioannis Tsimperidis ◽  
Cagatay Yucel ◽  
Vasilios Katos
Keyword(s):  
High Accuracy ◽  
Keystroke Dynamics ◽  
Time Intervals ◽  
Age And Gender ◽  
Common Features ◽  
User Classification ◽  
Gender And Age ◽  
And Gender ◽  
Keystroke Dynamic

Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers.

Digital Biomarkers of Cognitive Frailty: The Value of Detailed Gait Assessment Beyond Gait Speed

Gerontology ◽  
2021 ◽  
pp. 1-10
Author(s):  
He Zhou ◽  
Catherine Park ◽  
Mohammad Shahbazi ◽  
Michele K. York ◽  
Mark E. Kunik ◽  
...  
Keyword(s):  
Older Adults ◽  
Steady State ◽  
Dual Task ◽  
Gait Speed ◽  
Stride Length ◽  
Cognitive Frailty ◽  
Single Task ◽  
Double Support ◽  
Gait Parameters ◽  
Digital Biomarkers

<b><i>Background:</i></b> Cognitive frailty (CF), defined as the simultaneous presence of cognitive impairment and physical frailty, is a clinical symptom in early-stage dementia with promise in assessing the risk of dementia. The purpose of this study was to use wearables to determine the most sensitive digital gait biomarkers to identify CF. <b><i>Methods:</i></b> Of 121 older adults (age = 78.9 ± 8.2 years, body mass index = 26.6 ± 5.5 kg/m<sup>2</sup>) who were evaluated with a comprehensive neurological exam and the Fried frailty criteria, 41 participants (34%) were identified with CF and 80 participants (66%) were identified without CF. Gait performance of participants was assessed under single task (walking without cognitive distraction) and dual task (walking while counting backward from a random number) using a validated wearable platform. Participants walked at habitual speed over a distance of 10 m. A validated algorithm was used to determine steady-state walking. Gait parameters of interest include steady-state gait speed, stride length, gait cycle time, double support, and gait unsteadiness. In addition, speed and stride length were normalized by height. <b><i>Results:</i></b> Our results suggest that compared to the group without CF, the CF group had deteriorated gait performances in both single-task and dual-task walking (Cohen’s effect size <i>d</i> = 0.42–0.97, <i>p</i> &#x3c; 0.050). The largest effect size was observed in normalized dual-task gait speed (<i>d</i> = 0.97, <i>p</i> &#x3c; 0.001). The use of dual-task gait speed improved the area under the curve (AUC) to distinguish CF cases to 0.76 from 0.73 observed for the single-task gait speed. Adding both single-task and dual-task gait speeds did not noticeably change AUC. However, when additional gait parameters such as gait unsteadiness, stride length, and double support were included in the model, AUC was improved to 0.87. <b><i>Conclusions:</i></b> This study suggests that gait performances measured by wearable sensors are potential digital biomarkers of CF among older adults. Dual-task gait and other detailed gait metrics provide value for identifying CF above gait speed alone. Future studies need to examine the potential benefits of gait performances for early diagnosis of CF and/or tracking its severity over time.

scholarly journals Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Brinnae Bent ◽  
Peter J. Cho ◽  
Maria Henriquez ◽  
April Wittmann ◽  
Connie Thacker ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Data Driven ◽  
Self Management ◽  
Noninvasive Method ◽  
Medical Interventions ◽  
Interstitial Glucose ◽  
Using Data ◽  
Glucose Prediction ◽  
Digital Biomarkers

AbstractPrediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.

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