Smartphone Keyboard Research Articles

Exploring the impact of word prediction assistive features on smartphone keyboards for blind users

Assistive technologies have been developed to enhance blind users’ typing performance, focusing on speed, accuracy, and effort reduction. One such technology is word prediction software, designed to minimize keystrokes required for text input. This study investigates the impact of word prediction on typing performance among blind users using an on-screen QWERTY keyboard. We conducted a comparative study involving eleven blind participants, evaluating both standard QWERTY input and word prediction-assisted typing. Our findings reveal that while word prediction slightly improves typing speed, it does not enhance typing accuracy and increases both physical and temporal workload compared to the default keyboard. We conclude with recommendations for improving word prediction systems, including more efficient editing methods and the integration of voice pitch variations to aid error recognition.

Passively sensing smartphone use in teens with rates of use by sex and across operating systems

Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (mage = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21–0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators (https://nda.nih.gov/abcd/). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use.

Smartphone keyboard dynamics predict affect in suicidal ideation

While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (β = −0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data.

Rethinking Smart Keyboard Layout to Aid Strong Password Creation

In an era marked by increasing digitization and the omnipresence of smartphones, the importance of robustpassword security cannot be overstated. With the ever-growing threat of cyberattacks, there is a pressing needfor user-friendly tools that facilitate the creation of strong and unique passwords. Traditional alphanumerickeyboard layouts (physical or virtual) have remained largely unchanged for decades, relying on the sameQWERTY layout initially designed for typewriters. However, these layouts may not be optimal for generatingstrong passwords. This paper focuses on tailoring virtual keyboard layouts on smartphones specifically forstrong password creation. For this, we have performed extensive user surveys to see if the presence ofdedicated rows for digits and special characters (essential in any strong password) allows users to createstronger passwords compared to regular smartphone keyboard layout. Apart from that, we also investigatedthe optimal assignment of characters, digits, and special characters and their groupings in a single soft key.The findings from the detailed user experiment suggested optimal settings for a smartphone virtual keyboard(for Android) like- diagonal length for good typing speed (approximately between 8.38 and 9.41 cm), andkey density (0.88 to 1.21 keys/cm2) which produces the least error without sacrificing the strength ofpasswords created using those layouts. We hope the outcome of this paper will help designers to aid virtualkeyboard layouts for smartphones that can motivate and create strong passwords without sacrificing usability.

Improvement of Spelling Correction Accuracy in Indonesian Language through the Application of Hamming Distance Method

Spelling correction is a critical feature in software to reduce typing errors, commonly found in document processing software and smartphone keyboards. This research aims to evaluate the accuracy of the Hamming Distance method in correcting words in the Indonesian language, both standard and non-standard forms. The research data is derived from a previous study and comprises 60 standard and non-standard Indonesian words. Typos are generated by considering the layout of letters on the QWERTY keyboard. Typing error data is divided into two groups, namely words with 1 and 2 character differences. The first test is conducted on standard words, achieving an accuracy rate of 98.33% for 1 and 2 character differences. Subsequent testing on non-standard words shows an accuracy rate of 100% for 1 character difference and 96.67% for 2 character differences. The results of this research highlight the potential of the Hamming Distance method in improving the quality of spelling correction in the Indonesian language.

Influence of cognitive and motor tasks using smartphone during gait: EMG and gait performance analysis – Dual-task study

Previous studies reported changes in spatiotemporal gait parameters during dual-task performance while walking using a smartphone compared to walking without a smartphone. However, studies that assess muscle activity while walking and simultaneously performing smartphone tasks are scarce. So, this study aimed to assess the effects of motor and cognitive tasks using a smartphone while simultaneously performing gait on muscle activity and gait spatiotemporal parameters in healthy young adults. Thirty young adults (22.83 ± 3.92 years) performed five tasks: walking without a smartphone (single-task, ST); typing on a smartphone keyboard in a sitting position (secondary motor single-task); performing a cognitive task on a smartphone in a sitting position (cognitive single-task); walking while typing on a smartphone keyboard (motor dual-task, mot-DT) and walking while performing a cognitive task on a smartphone (cognitive dual-task, cog-DT). Gait speed, stride length, stride width and cycle time were collected using an optical motion capture system coupled with two force plates. Muscle activity was recorded using surface electromyographic signals from bilateral biceps femoris, rectus femoris, tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis, gluteus maximus and lumbar erector spinae. Results showed a decrease in stride length and gait speed from the single-task to cog-DT and mot-DT (p < 0.05). On the other hand, muscle activity increased in most muscles analyzed from single- to dual-task conditions (p < 0.05). In conclusion, performing a cognitive or motor task using a smartphone while walking promote a decline in spatiotemporal gait parameters performance and change muscle activity pattern compared to normal walking.

Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health.

We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs. not). The mood disorder group showed lower cognitive performance despite mild symptoms (depression/mania). There were also diurnal pattern differences with respect to cognitive performance: individuals with higher cognitive performance typed faster and were less sensitive to time of day. They also exhibited more well-defined diurnal patterns in smartphone keyboard usage: they engaged with the keyboard more during the day and tapered their usage more at night compared to those with lower cognitive performance, suggesting a healthier usage of their phone.

Smartphone Keyboard Interaction Monitoring as an Unobtrusive Method to Approximate Rest-Activity Patterns: Experience Sampling Study Investigating Interindividual and Metric-Specific Variations

Sleep is an important determinant of individuals' health and behavior during the wake phase. Novel research methods for field assessments are required to enable the monitoring of sleep over a prolonged period and across a large number of people. The ubiquity of smartphones offers new avenues for detecting rest-activity patterns in everyday life in a noninvasive an inexpensive manner and on a large scale. Recent studies provided evidence for the potential of smartphone interaction monitoring as a novel tracking method to approximate rest-activity patterns based on the timing of smartphone activity and inactivity throughout the 24-hour day. These findings require further replication and more detailed insights into interindividual variations in the associations and deviations with commonly used metrics for monitoring rest-activity patterns in everyday life. This study aimed to replicate and expand on earlier findings regarding the associations and deviations between smartphone keyboard-derived and self-reported estimates of the timing of the onset of the rest and active periods and the duration of the rest period. Moreover, we aimed to quantify interindividual variations in the associations and time differences between the 2 assessment modalities and to investigate to what extent general sleep quality, chronotype, and trait self-control moderate these associations and deviations. Students were recruited to participate in a 7-day experience sampling study with parallel smartphone keyboard interaction monitoring. Multilevel modeling was used to analyze the data. In total, 157 students participated in the study, with an overall response rate of 88.9% for the diaries. The results revealed moderate to strong relationships between the keyboard-derived and self-reported estimates, with stronger associations for the timing-related estimates (β ranging from .61 to .78) than for the duration-related estimates (β=.51 and β=.52). The relational strength between the time-related estimates was lower, but did not substantially differ for the duration-related estimates, among students experiencing more disturbances in their general sleep quality. Time differences between the keyboard-derived and self-reported estimates were, on average, small (<0.5 hours); however, large discrepancies were also registered for quite some nights. The time differences between the 2 assessment modalities were larger for both timing-related and rest duration-related estimates among students who reported more disturbances in their general sleep quality. Chronotype and trait self-control did not significantly moderate the associations and deviations between the 2 assessment modalities. We replicated the positive potential of smartphone keyboard interaction monitoring for estimating rest-activity patterns among populations of regular smartphone users. Chronotype and trait self-control did not significantly influence the metrics' accuracy, whereas general sleep quality did: the behavioral proxies obtained from smartphone interactions appeared to be less powerful among students who experienced lower general sleep quality. The generalization and underlying process of these findings require further investigation.

Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis

Multiple Sclerosis (MS) is a progressive demyelinating disease of the central nervous system characterised by a wide range of motor and non-motor symptoms. The level of disability of people with MS (pwMS) is based on a wide range of clinical measures, though their frequency of evaluation and inaccuracies coming from objective and self-reported evaluations limits these assessments. Alternatively, remote health monitoring through devices can offer a cost-efficient solution to gather more reliable, objective measures continuously. Measuring smartphone keyboard interactions is a promising tool since typing and, thus, keystroke dynamics are likely influenced by symptoms that pwMS can experience. Therefore, this paper aims to investigate whether keyboard interactions gathered on a person’s smartphone can provide insight into the clinical status of pwMS leveraging machine learning techniques. In total, 24 Healthy Controls (HC) and 102 pwMS were followed for one year. Next to continuous data generated via smartphone interactions, clinical outcome measures were collected and used as targets to train four independent multivariate binary classification pipelines in discerning pwMS versus HC and estimating the level of disease severity, manual dexterity and cognitive capabilities. The final models yielded an AUC-ROC in the hold-out set above 0.7, with the highest performance obtained in estimating the level of fine motor skills (AUC-ROC=0.753). These findings show that keyboard interactions combined with machine learning techniques can be used as an unobtrusive monitoring tool to estimate various levels of clinical disability in pwMS from daily activities and with a high frequency of sampling without increasing patient burden.

Effects of Motor Task Difficulty on Postural Control Complexity during Dual Tasks in Young Adults: A Nonlinear Approach.

Few studies have evaluated the effect of a secondary motor task on the standing posture based on nonlinear analysis. However, it is helpful to extract information related to the complexity, stability, and adaptability to the environment of the human postural system. This study aimed to analyze the effect of two motor tasks with different difficulty levels in motor performance complexity on the static standing posture in healthy young adults. Thirty-five healthy participants (23.08 ± 3.92 years) performed a postural single task (ST: keep a quiet standing posture) and two motor dual tasks (DT). i.e., mot-DT(A)-perform the ST while performing simultaneously an easy motor task (taking a smartphone out of a bag, bringing it to the ear, and putting it back in the bag)-and mot-DT(T)-perform the ST while performing a concurrent difficult motor task (typing on the smartphone keyboard). The approximate entropy (ApEn), Lyapunov exponent (LyE), correlation dimension (CoDim), and fractal dimension (detrending fluctuation analysis, DFA) for the mediolateral (ML) and anterior-posterior (AP) center-of-pressure (CoP) displacement were measured with a force plate while performing the tasks. A significant difference was found between the two motor dual tasks in ApEn, DFA, and CoDim-AP (p &lt; 0.05). For the ML CoP direction, all nonlinear variables in the study were significantly different (p &lt; 0.05) between ST and mot-DT(T), showing impairment in postural control during mot-DT(T) compared to ST. Differences were found across ST and mot-DT(A) in ApEn-AP and DFA (p &lt; 0.05). The mot-DT(T) was associated with less effectiveness in postural control, a lower number of degrees of freedom, less complexity and adaptability of the dynamic system than the postural single task and the mot-DT(A).

Bimodal Keystroke Dynamics-Based Authentication for Mobile Application Using Anagram

Currently, most of the smartphones recognize uses based on static biometrics, such as face and fingerprint. However, those traits were vulnerable against spoofing attack. For overcoming this problem, dynamic biometrics like the keystroke and gaze are introduced since it is more resistant against spoofing attack. This research focuses on keystroke dynamics for strengthening the user recognition system against spoofing attacks. For recognizing a user, the user keystrokes feature used in the login process is compared with keystroke features stored in the keystroke features database. For evaluating the accuracy of the proposed system, words generated based on the Indonesian anagram are used. Furthermore, for conducting the experiment, 34 participants were asked to type a set of words using the smartphone keyboard. Then, each user’s keystroke is recorded. The keystroke dynamic feature consists of latency and digraph which are extracted from the record. According to the experiment result, the error of the proposed method is decreased by 23.075% of EER with FAR and FRR are decreased by 16.381% and 10.41% respectively, compared with Kim’s method. It means that the proposed method is successful increase the biometrics performance by reducing the error rates

Predicting clinically relevant changes in bipolar disorder outside the clinic walls based on pervasive technology interactions via smartphone typing dynamics

Modeling smartphone keyboard dynamics as the foundation of an early warning system (EWS) for mood instability holds potential to expand the reach of healthcare beyond the traditional clinic wall’s, which may lead to better ongoing care for chronic mental illnesses such as bipolar disorder. Here, we investigate the feasibility of such a system using a real-world open-science dataset. In particular, we are interested in whether passive technology interaction patterns in real-world datasets reflect findings from more controlled research trials, and the implications for clinical care. Data from 328 people who downloaded an open-science app was analyzed using a variety of machine learning methods, including different modeling methods (random forests, gradient boosting, neural networks), different types of class rebalancing, and pre-processing techniques. The aim was to predict fluctuations in PHQ scores in the weeks before the fluctuation occurred. Various feature selection methods were also employed to identify the top features driving the predictive patterns (out of total 54 starting features). Results showed predictive accuracy around ∼90%, similar to controlled research trials, while revealing a number of interesting features (e.g. PTSD and mood instability) that suggest future research avenues. The findings from our analysis appear to indicate that real-world interaction data from smartphones can be utilized as an EWS monitoring tool for mood disorders like bipolar. We also discuss the broader applicability of ecological momentary assessment (EMA) approaches to connected systems combining different forms of pervasive technology interaction (smartphones, wearables, social robots) to track everyday health status.

Building a fitness tracker for the brain: A journey from lab to consumers

In this paper, we discuss our journey as academic entrepreneurs, which began with the market need for objective markers for the diagnosis and assessment of mental health disorders. For most of history, mental health disorders have been chiefly defined by the subjective experiences of people with these disorders -- we know people have a depressive disorder because they feel depressed or that they have a psychotic disorder because they experience hallucinations. The lack of objective markers to track changes in mental health symptoms creates barriers to treatment. To address this, the vision of KeyWise AI is the further development and commercialization of an innovative digital phenotyping technology that turns smartphones into fitness trackers for the brain. Commercializing the generalizable knowledge derived from the ongoing citizen science BiAffect study, our brain fitness-tracking technology supports real-time passive and unobtrusive modeling and monitoring of emotional, behavioral and cognitive processes. The core technology centers around a custom-built smartphone keyboard that replaces the default keyboard, thus allowing for unobtrusive collection of anonymized keyboard dynamics and motor kinetics metadata. The second half of this paper provides general guidelines to help future academic entrepreneurs on their startup journey.

Associations between smartphone keystroke dynamics and cognition in MS.

Examine the associations between smartphone keystroke dynamics and cognitive functioning among persons with multiple sclerosis (MS). Sixteen persons with MS with no self-reported upper extremity or typing difficulties and 10 healthy controls (HCs) completed six weeks of remote monitoring of their keystroke dynamics (i.e., how they typed on their smartphone keyboards). They also completed a comprehensive neuropsychological assessment and symptom ratings about fatigue, depression, and anxiety at baseline. A total of 1,335,787 keystrokes were collected, which were part of 30,968 typing sessions. The MS group typed slower (P < .001) and more variably (P = .032) than the HC group. Faster typing speed was associated with better performance on measures of processing speed (P = .016), attention (P = .022), and executive functioning (cognitive flexibility: P = .029; behavioral inhibition: P = .002; verbal fluency: P = .039), as well as less severe impact from fatigue (P < .001) and less severe anxiety symptoms (P = .007). Those with better cognitive functioning and less severe symptoms showed a stronger correlation between the use of backspace and autocorrection events (P < .001). Typing speed may be sensitive to cognitive functions subserved by the frontal-subcortical brain circuits. Individuals with better cognitive functioning and less severe symptoms may be better at monitoring their typing errors. Keystroke dynamics have the potential to be used as an unobtrusive remote monitoring method for real-life cognitive functioning among persons with MS, which may improve the detection of relapses, evaluate treatment efficacy, and track disability progression.

Naturalistic smartphone keyboard typing reflects processing speed and executive function.

ObjectiveThe increase in smartphone usage has enabled the possibility of more accessible ways to conduct neuropsychological evaluations. The objective of this study was to determine the feasibility of using smartphone typing dynamics with mood scores to supplement cognitive assessment through trail making tests.MethodsUsing a custom‐built keyboard, naturalistic keypress dynamics were unobtrusively recorded in individuals with bipolar disorder (n = 11) and nonbipolar controls (n = 8) on an Android smartphone. Keypresses were matched to digital trail making tests part B (dTMT‐B) administered daily in two periods and weekly mood assessments. Following comparison of dTMT‐Bs to the pencil‐and‐paper equivalent, longitudinal mixed‐effects models were used to analyze daily dTMT‐B performance as a function of typing and mood.ResultsComparison of the first dTMT‐B to paper TMT‐B showed adequate reliability (intraclass correlations = 0.74). In our model, we observed that participants who typed slower took longer to complete dTMT‐B (b = 0.189, p < .001). This trend was also seen in individual fluctuations in typing speed and dTMT‐B performance (b = 0.032, p = .004). Moreover, participants who were more depressed completed the dTMT‐B slower than less depressed participants (b = 0.189, p < .001). A practice effect was observed for the dTMT‐Bs.ConclusionTyping speed in combination with depression scores has the potential to infer aspects of cognition (visual attention, processing speed, and task switching) in people's natural environment to complement formal in‐person neuropsychological assessments that commonly include the trail making test.

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

Preserving Privacy with Federated Learning in Route Choice Behavior Modeling

While big data helps improve decision-making and model developments, it often runs into privacy concerns. An example would be retrieving drivers’ origin and destination information from smartphone navigation apps for developing a route choice behavior model. To conserve privacy, yet to take advantage of big data in navigation applications, the authors propose to apply a federated learning approach, which has shown promising application in predicting smartphone keyboard’s next word without sending text to the server. Additional benefits of using federated learning is to save on data communications, by sending model parameters instead of entire raw data, and to distribute the computational burden to each smartphone instead of to the main server. The results from real-world route navigation usage data from about 30,000 drivers over one year showed that the proposed federated learning approach was able to achieve very similar accuracy to the traditional centralized global model and yet assures privacy.

248 Behavioural Biometrics: Using Smartphone Keyboard Activity as a Proxy for Rest-Activity Patterns

Abstract Introduction Rest-activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long-term monitoring of rest-activity patterns is typically performed with diaries or actigraphy. Here, we propose a fully unobtrusive method to obtain rest-activity patterns using smartphone keyboard activity. This study investigated whether keyboard activities from habitual smartphone use are reliable estimates of rest and activity timing compared to daily self-reports within healthy participants. Methods First-year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours, and filled out the Consensus Sleep Diary for one week. The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers. Results Results revealed high correlations between these markers and user-reported onset and offset of resting period (r ranged 0.74 - 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R2 ranged 0.60 - 0.66). This indicates that smartphone keyboard activity can be used to estimate rest-activity patterns. In addition, effects of chronotype and type of day were investigated. Conclusion Implementing this monitoring method in longitudinal studies would allow for long-term monitoring of (disturbances to) rest-activity patterns, without user burden or additional costly devices. It could be particularly useful in studies amongst clinical populations with sleep-related problems, or in populations for whom disturbances in rest-activity patterns are secondary complaints, such as neurological disorders. Support (if any):

Kollector: Detecting Fraudulent Activities on Mobile Devices Using Deep Learning

With the rapid growth in smartphone usage, preventing leakage of personal information and privacy has become a challenging task. One major consequence of such leakage is impersonation. This type of illegal usage is nearly impossible to prevent as existing preventive mechanisms (e.g., passcode and fingerprinting), are not capable of continuously monitoring usage and determining whether the user is authorized. Once unauthorized users can defeat the initial protection mechanisms, they would have full access to the devices including using stored passwords to access high-value websites. We present Kollector, a new framework to detect impersonation based on a multi-view bagging deep learning approach to capture sequential tapping information on the smart-phone's keyboard. We construct a sequential-tapping biometrics model to continuously authenticate the user while typing. We empirically evaluated our system using real-world phone usage sessions from 26 users over eight weeks. We then compared our model against commonly used shallow machine techniques and find that our system performs better than other approaches and can achieve an 8.42 percent equal error rate, a 94.24 percent accuracy and a 94.41 percent H-mean using only the accelerometer and only five keyboard taps. We also experiment with using only three keyboard taps and find that the system still yields high accuracy while giving additional opportunities to make more decisions that can result in more accurate final decisions.

Behavioural biometrics: Using smartphone keyboard activity as a proxy for rest-activity patterns.

SummaryRest–activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long‐term monitoring of rest–activity patterns is typically performed with diaries or actigraphy. Here, we propose an unobtrusive method to obtain rest–activity patterns using smartphone keyboard activity. The present study investigated whether this proposed method reliably estimates rest and activity timing compared to daily self‐reports within healthy participants. First‐year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours and completed the Consensus Sleep Diary for 1 week. The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers. Results revealed high correlations between these markers and user‐reported onset and offset of resting period (r ranged from 0.74 to 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R 2 ranged from 0.60 to 0.66). This indicates that smartphone keyboard activity can be used to estimate rest–activity patterns. In addition, effects of chronotype and type of day were investigated. Implementing this method in longitudinal studies would allow for long‐term monitoring of (disturbances to) rest–activity patterns, without user burden or additional costly devices. It could be particularly interesting to replicate these findings in studies amongst clinical populations with sleep‐related problems, or in populations for whom disturbances in rest–activity patterns are secondary complaints, such as neurological disorders.