www.nature.com/npjdigitalmed

ARTICLEOPEN

Population-scale hand tremor analysis via anonymized mouse
cursor signals

1234567890():,;Ryen W. White1 and Eric Horvitz1
Tremors are a common movement disorder with a spectrum of benign and pathological causes, including neurodegenerative
disease, alcohol withdrawal, and physical overexertion. Studies of tremors in clinical practice are limited in size and scope anddepend on explicit tracking of tremor characteristics by clinicians. Data drawn from small numbers of patients observed in shortduration sessions pose challenges for understanding the nature and distribution of tremors over a large population. Methods arepresented to estimate hand tremors based on anonymized computer mouse cursor movement data collected from millions of users
of a web search engine. To determine the feasibility of using this signal for the estimation of the prevalence of tremors over a large
population, the characteristics of tremor-like movements are computed and compared against user data that can be interpreted as
self-reports, the ﬁndings of published clinical studies, and a target selection study where participants self-report hand tremors andknown causes. The results demonstrate signiﬁcant alignment between estimated tremors and both self-reports and clinicalﬁndings. Those with cursor tremor events are more likely to report tremor-related search interests. Variations in cursor tremor
quantity and cursor tremor frequency with demographics mirror those from clinical studies. Distributions of cursor tremor
frequencies vary as expected for different medical conditions. Overall, the study ﬁnds evidence for the validity of harnessing
anonymized mouse cursor motion as a population-scale tremor sensor for epidemiologic studies. Feasible future applications
include opt-in services for screening and for monitoring the progression of illness.
npj Digital Medicine (2019)2:93; https://doi.org/10.1038/s41746-019-0171-4

INTRODUCTION
Tremors are involuntary, rhythmic oscillations in one or more bodyparts. They are the most common movement disorder found inclinical practice.1 Prevalence varies depending on tremor type.
Essential tremor (ET) is the most common form, with prevalence
rates ranging from 0.4% to 5.6%.2 Hand tremors are suggestive ofmany medical conditions, as well as other factors such as fear or
fatigue.
This study focuses on resting tremors. Resting tremors occur inthe absence of voluntary muscle activity. These tremors occur
when a body part is relaxed and completely supported against
gravity3 and can be exacerbated by increased cognitive load.4
Resting tremors are more easily identiﬁable in mouse cursor
movements than action tremors. The low resolution of the cursor
position sampling makes it difﬁcult to detect action tremors while
the cursor is in motion. Resting tremor is often symptomatic ofspeciﬁc medical conditions, including Parkinson’s disease (PD)5–7
and forms of ET.3,4 Dopaminergic deﬁciency in PD results insymptoms such as tremors and cognitive decline,7 evidence of
which may be apparent in how people interact with computers.8
Prior studies have examined the effect of neurodegenerative
disorders such as PD on computer mouse cursor control9 andcursor movement reaction time10 and the utility of assistive
technologies to help those with motor impairments to better use
computer input devices11 and ﬁlter tremulous movement frommouse cursors before it reaches computer interfaces.12 Others
have used smartphone accelerometers to assess tremor frequencies.13 These studies are often small scale, limiting their
1
Microsoft Research, Redmond, WA, USA
Correspondence: Ryen W. White (ryenw@microsoft.com)

Received: 8 April 2019 Accepted: 30 August 2019

Scripps Research Translational Institutegeneralizability, and require explicit measurement of tremor
characteristics, which is both burdensome and unnatural. In
contrast, log-based monitoring requires no additional action frompatients or clinicians and can be deployed longitudinally at
massive scale.14,15 Prior studies of the logs from millions of searchengine and social media users have highlighted opportunities inusing these data for the early detection of medical conditions,such as pancreatic cancer16 and lung cancer,17 and disease
surveillance in general.18,19
The availability of large-scale, longitudinal computer mouse
cursor movement data presents an opportunity to study hand
tremors in ways that are infeasible in limited-duration laboratory
settings or brief bedside evaluations. Numerous studies of theepidemiology of tremors can be enabled using anonymized
population-scale data, including cohort analyses based on factors
such as geographic location and demographics. With explicit
consent, user-centric screening services housed within searchengines could be employed to inform at-risk individuals and to
perform longitudinal studies to track the status and progression of
known illness.
Mouse cursor motion data have been used in prior studies andapplications, including measuring search engine relevance,14prefetching search results,20 detecting click and identity fraud,21,22
tracking attention in computer users,23,24 computer interface
design,25 measuring website engagement,26 and searcher intent
understanding.27 The timing of keystrokes in search queries has
also been used for population-scale physiological sensing.28 Most
relevant to this study is prior research on applying cursor motion

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data to detect the possibility of neurological disease8 or anxiety29
in search engine users.
This study continues and signiﬁcantly expands on earlier work,8which focused on discriminating between PD and ET with amachine-learned classiﬁer that employed >50 search-log features,
most of which were non-cursor based and only two features thatwere based on cursor tremor estimates. In ref. 8, little detail isprovided about the methodology used for tremor estimation andno attempt is made to validate tremors detected from search logs,
two shortcomings that are addressed in this article. Validation of
the presence of tremors is essential in establishing the generalizability of the tremor estimation methods beyond a single
application. We focus here on cursor-based tremor estimation. We
present a detailed description of the log-based tremor estimation
methodology and report on four complementary and comprehensive validation analyses, including a 1000-person target
selection study independent from the search logs which showsthat large-scale tremor estimation is feasible. We consider thefeasibility of discriminating between PD and ET based on known
differences in tremor frequency distributions in these diseases.30
The validation of the tremor estimation methods provide evidence
that anonymized cursor motion can be harnessed as a populationscale tremor sensor, with applications including epidemiologic
studies and opt-in services for screening and for monitoringdisease progression in individuals.
We collect cursor logs from an anonymized sample of millionsof searchers on the Microsoft Bing search engine (http://bing.com)
and analyze the data retrospectively. Post hoc validation of cursor
tremor estimates is challenging given only anonymized log data.
As a second channel of information, we analyze correlations
between observed cursor tremors and searchers’ self-reported
interests in tremors as expressed through their search queries.
Clinical knowledge about the inﬂuence of demographics andspeciﬁc medical conditions on cursor tremor prevalence andcharacteristics is also considered. Finally, a target selection study
was performed, where 1000 remote participants selected onscreen targets and self-reported hand tremors and their known
causes. Analyzing the cursor movements from this study provided
additional validation of the cursor-based tremor estimation
methodology.

selection study where participants selected on-screen targets with
their mouse pointer (and all cursor movements were recorded)and self-reported hand tremors and associated medical
conditions.
Queries as self-reporting
Individuals affected by medical symptoms and conditions arelikely to search for information about them on web searchengines.31,32 Contingency matrices were generated for whether
searchers had one or more cursor tremor events (yes/no) andwhether they performed one or more searches for tremors or
related synonyms (e.g., “tremors,” “shaky hands,” etc.) (yes/no)over the observation period. Percentage deviations for each cell(i.e., the difference from expected, deﬁned as (observed –expected)/expected × 100) were computed during Chi-squared
analyses (where observed = frequency count in each cell andexpected = (row total × column total)/grand total): +0% meansthat the combination is observed as expected, while +100%
means it is observed twice as much as expected. Percentage
deviation increases with the number of oscillations used to deﬁne
a cursor tremor event (nosc) from 15% at nosc ≥ 1 to 190% at nosc ≥4 and is signiﬁcant across all tested values of nosc in the range1,33(Chi-squared tests: all χ2(1) ≥ 94.76, all p < 0.001, all φ (effect sizes
as phi correlations) >0.003). The searcher counts for nosc ≥ 2 arereported in Table 1 for all tremor searches (row (a)) and forﬁltering tremor searchers to those who issued queries that show
stronger evidence, which are referred to as “experiential queries”(row (b)). Experiential queries are typically ﬁrst-person statements
providing stronger evidence of tremor incidence (e.g., “my shaky
hands,” “I have tremors”) than episodes of general tremor
searching (excluding, using look-up tables, queries such as thosethat were natural language questions, related to others (e.g.,friends, relatives), or related to domestic animals). Experiential
queries have been leveraged in prior research as evidence ofclinical diagnosis in search-log studies of brain disease,8 cancer,17
and pregnancy.34 These ﬁrst-person statements offer moreevidence that observed tremor searches are connected topersonal experiences with tremors. Later, this is extended toexperiential diagnostic queries: ﬁrst-person statements containing
evidence of clinical diagnosis. Table 1 shows that the percentage
deviation for yes–yes (highlighted) is higher for experiential
tremor searchers (+120%) than for all tremor searchers (+99%).
Overall, the results show that searchers with cursor tremor eventsare more likely to self-report tremor-related interests. Theseﬁndings, especially those for experiential searchers (Table 1, row(b)), offer some conﬁrmatory evidence that cursor-based tremor
estimates capture tremors experienced by searchers.

RESULTS
Four different analyses were performed to assess the validity of
cursor-based tremor estimation: (a) connection with searchers’interests as indicated in their search queries; (b) differences incursor tremor prevalence and frequencies based on demographics; (c) shifts noted in the distribution of cursor tremor
frequencies when conditioned on observing evidence in searchers’ queries of having received a clinical diagnosis, and (d) a target
Table 1.

Contingency tables with counts of searchers and percentage deviations from expected counts (shown in parentheses) broken down by thepresence/absence of tremor-related searches and the presence/absence of cursor tremor events (all ﬁltered to events with nosc ≥ 2)

Searcher group

1+cursor tremor events

1+tremor searchYes(a) “Tremor search” means any tremor-related search(b) “Tremor search” means any experiential tremor-related search

No

Yes

565 (+98.9%)

2,739,486 (−0.01%)No

292 (−49.0%)

5,525,048 (+0.005%)

Yes
No

99 (+119.6%)
37 (−59.3%)

2,739,952 (−0.002%)
5,525,303 (+0.001%)

Results are reported for searchers with (a) any tremor-related searches (Chi-squared test: χ2(1) = 415.5, p < 0.0001, effect size as phi correlation (φ) = 0.007) andfor (b) any experiential tremor-related searches (Chi-squared test: χ2(1) = 96.4, p < 0.0001, φ = 0.003). Searcher counts for within-searcher co-occurrence oftremor search and cursor tremor (yes–yes cells in rows (a) and (b)) are signiﬁcantly above expected in both cases and higher when the positive examples oftremor searches are experiential cases (b) rather than any tremor-related search (a)npj Digital Medicine (2019) 93

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Fig. 1 Average cursor tremor count and average cursor tremor frequency, by age and gender. a Mean average number of cursor tremor
events per age group and gender, and b mean average cursor tremor frequency per age group and gender. Error bars denote standard errorof the mean. To compute the macro-average used in b, the average cursor tremor frequency per searcher is computed and then averaged
again across all searchers in each age group category. This ensures that no single searcher unduly inﬂuences overall statistics

Searcher demographics
The appearance of similar trends in mouse cursor tremor
estimates would add further supportive evidence on the validityof cursor motion as a tremor sensor. In total, 2,740,051 searchers(14.3% of all 19M searchers in the dataset) had at least one cursor
tremor event with nosc ≥ 2 (7,288,536 cursor tremor events intotal). The mean average (M) number of cursor tremor events
per searcher was 2.62 (standard deviation (SD) = 4.99, median =1). The average cursor tremor frequency per searcher was
6.81 hertz (Hz) (SD = 4.04 Hz, median = 6 Hz) and the macroaverage cursor tremor frequency (averaged within the observed
activity of each searcher and then again over all searchers) was
6.86 Hz (SD = 3.46 Hz, median = 6 Hz).
Age and gender classiﬁcations generated by proprietarystatistical classiﬁers from the Bing search engine were available
for approximately 300k of the 2.7M searchers in the cursor tremor
dataset with nosc ≥ 2 (11.1%). The age and gender classiﬁers weretrained by another team at Microsoft on longitudinal search
queries and search-result clicks from a separate set of web
searchers for whom self-reported demographics were available.
The accuracy of both classiﬁers is approximately 80%. For each of
the 300k searchers, the age classiﬁer provided an age estimate
comprising one of the ﬁve groups: <18 years (0.03% of cursor
tremor searchers), 18–24 years (1.71%), 25–34 years (1.20%), 35–49
years (6.09%), and ≥50 years (90.96%). Gender classiﬁcations weredivided between male (50.51% of the 300k searchers) and female(49.49%). Figure 1 summarizes cursor tremor quantity and cursor
tremor frequencies by estimated age and gender. Note thatstatistically signiﬁcant differences in the average number ofqueries submitted or the amount of time spent on result pages
were not observed between the different age groups andbetween the different genders.
Two-way analyses of variance were conducted on the inﬂuenceof age and gender on cursor tremor counts and cursor tremor
frequency. All effects were statistically signiﬁcant at the α =0.05 signiﬁcance level apart from gender. The main effects for age
were signiﬁcant for cursor tremor count (F(4,298636) = 234.26, p <0.0005) and cursor tremor frequency (F(4,298636) = 45.46, p =0.002), with small effect sizes (count: η2 = 0.004, frequency: η2 =0.0007). Tukey post hoc tests indicate signiﬁcant differences incursor tremor counts between ≥50 years (M = 4.11, SD = 8.49) andall other groups apart from <18 years (M = 2.35, SD = 2.24), but
only signiﬁcant differences in cursor tremor frequency between≥50 years (M = 6.88 Hz, SD = 3.32 Hz) and <18 years (M = 7.38 Hz,
Scripps Research Translational InstituteSD = 3.65 Hz) and 18–24 (M = 7.09 Hz, SD = 3.57 Hz). Although
women are more likely to present with tremors in early PD,35 few
studies have offered evidence of signiﬁcant gender effects on
tremor characteristics.36 The main effects for gender were notsigniﬁcant for cursor tremor count (F(4,298636) = 0.72, p = 0.396)
and cursor tremor frequency (F(4,298636)= 0.29, p = 0.590). Theinteraction effects were signiﬁcant for cursor tremor count (F(4,298636) = 9.46, p < 0.0005) and cursor tremor frequency (F(4,298636) = 4.07, p = 0.003).
Evidence of clinical diagnosis
Clinical studies have shown that tremor frequencies vary per
medical condition.37 Another way to validate the cursor-based
tremor estimates is to examine whether the expected changes intremor frequency for different medical conditions are also evident
in cursor-based tremors. PD and ET were selected as the target
conditions since resting tremors are observed in both conditions,3,6 the conditions are prevalent and affect millions of people
worldwide, and they have distinct tremor frequency distributions.
As with the earlier analysis of searchers’ self-reported tremor
interests, ﬁrst-person experiential diagnostic queries (containing
evidence suggesting that the user was diagnosed with a medical
condition) such as “just been diagnosed with parkinsons” or“treating my essential tremor” issued on the Bing search engine
were used as diagnostic evidence. The patterns used to identify
experiential diagnostic queries are listed in Supplementary Table
1. Cases exhibiting evidence that diagnostic queries were invalid,e.g., questions, related to others (e.g., friends, relatives), related todomestic animals, and so on, were excluded. Only cursor tremor
searchers who issued one or more valid experiential diagnostic
queries for PD or ET were included in this part of the analysis.
As may be expected, experiential diagnostic queries occur
infrequently in search log data. There were few searchers in the
17-day experiment from mid-September until early October who
issued experiential diagnostic queries for PD or ET. To ﬁnd moreexperiential diagnostic searchers in the cursor tremors dataset foreach condition, the observation period to identify experiential
diagnostic PD or experiential diagnostic ET cursor tremor
searchers was extended back 6 months before the start of theexperiment to March 2017. The extended time frame was onlyused to ﬁnd additional experiential diagnostic searchers; allanalysis of cursor tremor events was still only performed on datafrom the 17-day observation period. Any experiential diagnostic
search from a cursor tremor searcher appearing in that extended
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R.W. White and E. Horvitz

4Fig. 2 Pointwise mutual information (PMI) score by cursor tremor
frequency, by medical condition. PMI score represents the change inprobability of observing a speciﬁc cursor tremor frequency given
evidence of condition diagnosis over the background distribution,
computed across all cursor tremor events independent of condition.
The PMI score distributions per cursor tremor frequency are shownfor experiential diagnostic Parkinson’s disease searchers (dashedline) or experiential diagnostic essential tremor searchers (solid line)

time was counted as evidence of clinical diagnosis. In total, 157
experiential diagnostic PD cursor tremor searchers and 109
experiential diagnostic ET cursor tremor searchers were discovered in the logs, contributing 754 and 519 cursor tremor events at
nosc ≥ 2, respectively. The cursor tremor frequency distributions forsearchers in these two groups were computed and comparedagainst the cursor tremor frequency distribution across all
searchers.
Pointwise mutual information (PMI)6,38 is a measure of
association from information theory and statistics. PMI was used
to quantify the change in probability of observing a speciﬁc cursor
tremor frequency given evidence of condition diagnosis over the
background across all cursor tremor events, independent of thecondition (i.e., log[p(tremor frequency|evidence of condition)/p(tremor frequency)]). The PMI score is computed at eachfrequency in the range [1 Hz, 15 Hz]. Figure 2 shows the PMIscore distributions for PD and ET. For each condition, the regions
where the cursor tremor frequency values are higher than
expected (PMI > 0) correspond to tremor frequency ranges
identiﬁed during clinical studies: Parkinsonian tremors(3–7 Hz)30,39,40 and ETs (4–12 Hz).41
For ET, the region of PMI > 0 (4–10 Hz) has a lower upper
threshold than the 4–12 Hz reported in clinical studies.41 Explanations for this include a right skew in the age distribution of
searchers in the cursor tremor dataset: >90% of cursor tremor
searchers are aged ≥50 years and tremor frequencies can decrease
with age,42 and the nature of the task: search is cognitively
demanding43 and tremor frequencies can be lower for more
focused tasks.44
Target selection
In total, there were 1000 participants in the target selection study.
In all, 6.7% self-reported experiencing hand tremors and 2.6% selfreported ET as the known cause of their hand tremors (alignedwith prior studies2,45). Participants who self-identiﬁed with nohand tremor are regarded as controls. The gender of thosereporting tremors was distributed as 55.2% male (n = 35), 41.8%
female (n = 28), and 6.0% (n = 4) did not report their gender.
There were six age groups: 18–24 years (10.5%, n = 7), 25–34 years(41.8%, n = 28), 35–44 years (9.0%, n = 6), 45–54 years (6.0%, n =4), 55–64 years (20.9%, n = 14), and ≥65 years (9.0%, n = 6). Other
medical conditions were less prevalent in the self-report data (e.g.,
npj Digital Medicine (2019) 93

stroke [0.7% of participants], multiple sclerosis [0.5%], and PD[0.2%]). In the raw cursor data (no downsampling), the average
number of cursor events per task was 1601 (SD = 1109), theaverage distance traveled by the mouse cursor per task was
19,735 pixels (px) (SD = 9364 px), and the average time per task
was 113 s (SD = 57 s).
Cursor tremor events were observed for 73.1% of participants(n = 49 of 67) who self-reported hand tremors versus 3.4% of
controls (n = 32 of 933) (Z = 20.23, p < 0.001). Cursor tremor
frequencies for the 73 cursor tremor events observed from the 26
participants self-reporting ET (M = 9.12Hz; SD = 5.31Hz) signiﬁcantly differed from frequencies of all other cursor tremor eventsin the dataset (M = 5.93Hz; SD = 4.49Hz; t(275) = 3.70, p < 0.01).
The frequency range with PMI score > 0 was 6–12 Hz; this is
toward the upper end of the range of tremor frequencies
observed in clinical studies on ET,41 perhaps reﬂecting theyounger demographic in the participant population of the target
selection study.46
DISCUSSION
The ﬁndings of this feasibility study demonstrate alignment
between cursor tremor events and both self-reported interest intremors and ﬁndings of clinical studies, providing evidence insupport of using cursor movements as a population-scale sensor
of cursor tremor events. Clinical studies have shown that tremor
incidence increases with age47 and that tremor frequencies
decrease with age.46,48 The variations in cursor tremor quantity
and cursor tremor frequency with searcher demographics
observed in this study are concordant with ﬁndings in thosestudies, providing additional supportive evidence for mouse
cursor motion as a means of tremor estimation. The alignment
between cursor tremor distributions from searchers issuingexperiential diagnostic queries and tremor distributions fromclinical studies30,39–41 further supports the use of mouse cursor
motion for tremor estimation. The consistency offers additional
conﬁrmatory evidence that observed cursor tremor events capture
real tremors and are less likely to be attributable to other possible
causes, such as myoclonus.49 While the results are promising,
additional validation is required. The sensitivity and speciﬁcity ofdetecting tremor via the use of computer mice needs to be
determined with patients and controls. Such studies will help
answer important questions such as the level of severity of cursor
tremors required to separate them from other extraneous
movements.
The data analyzed were from a short (17-day) experiment for asample of search trafﬁc on a speciﬁc search engine (Bing). Firstperson experiential queries were used to ﬂag likely tremor
sufferers and experiential diagnostic queries were used to identify
those likely to have been diagnosed with medical conditions.
Experiential queries have been used in previous studies to identify
searchers exhibiting evidence of diagnosis,8,17,34 but more work isneeded to validate this approach with patients, especially giventhat misdiagnosis of both PD and ET are common, as is confusion
over general Parkinsonism.50 The relationship between searchtasks and characteristics of tremor-like movements needs to beunderstood, including the presence and inﬂuences of anxiety,including anxiety induced in the process of search and retrieval(e.g., searching on alarming medical conditions32). The target
selection study offered an additional source of validation
independent of the search logs and where participants selfreported known causes of hand tremors in line with establishedprevalence rates.45 More cursor movement data are needed over alonger time frame and multiple application settings to obtain
more robust insights, including how cursor tremor frequencieschange for searchers over time.51
Future work includes exploring the application of thesemethods to additional tremor types and other conditions with
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5
motor symptoms, as well as studying environmental sources ofvibration (e.g., computer usage on airplanes or on public
transportation). There are many types of tremor, including
rhythmic and non-rhythmic and task or situation-related tremors,and almost all with overlapping frequency ranges. Different
tremors need to be recorded in different ways, e.g., resting
tremors will disappear with intentional mouse movements. Highfrequency tremors, such as exaggerated physiological tremor(caused by fatigue, metabolic diseases, alcohol withdrawal, andothers), may not be recordable by a computer mouse due tofriction and low sampling rates. Action tremors may be detectablefrom cursor trails given more granular tracking of computer
mouse cursor position to estimate tremor behaviors while thecursor is in motion. Factors that could affect the reliability of the
cursor tremor estimation also require further investigation.
Computer mice move in proportion to the velocity and/or
acceleration of mouse movements.52 Mouse motion therefore
does not provide a linear transduction of tremor amplitude, which
could be biased by higher tremor frequencies. The current study
focuses on small cursor movements (≤15 px), which may be lessaffected by these biases, and ﬁlters cursor tremor events to thosewith frequencies from 1 to 15 Hz to reduce errors attributable tosampling noise. A thorough assessment is required of possible
bias in tremor amplitude related to the use of cursor data.
Standardizing cursor tremor estimation methods across different
system settings (e.g., screen resolutions, display dimensions, input
device types, mouse sensitivities) may improve tremor estimates.
This study could not control for these settings in the retrospective
log analyses, but the target selection study did control for display
dimensions and input device type.
The methods described in this article are meant to complement,
not replace, traditional studies of tremor by offering a new lens on
tremors across thousands or millions of individuals. Logs need to
be used carefully: logged search activity can be affected by many
aspects of the search experience including query autocompletion53 and result page content.54 The nature of the search query(e.g., informational or navigational) can also impact people’s
cursor movements on result pages.15 Log-based monitoring is
especially useful in studies where broad regional analyses areparamount over attention to individual detail, such as monitoringlarge-scale trends and epidemics, or for other applications such as
population-scale physiological sensing,28 but they are unlikely tobe sufﬁciently discriminative for diagnostic inferences on
their own.
As mentioned earlier, log-based tremor estimates can be
combined with other signals to detect evidence of neurodegenerative disease.8 Beyond contributing as features in automateddisease detection, there are other possible applications of cursor
tracking. These include studying cursor-based tremors in cohorts
generated based on location, demographics, or other data (e.g.,
topical interests as inferred from query logs), an analysis that ischallenging to do at scale using current methods. Longitudinalstudies of cursor-based tremors could enable the provision of
opt-in services for screening for neurological disorders and forthe tracking of disease progression in people with conﬁrmed
neurodegenerative diseases. Such studies could also assist
physicians by providing longitudinal patterns or trends intremors using data collected unobtrusively from people’s everyday activities, a signal that is currently unavailable for clinicaluse. Technologies such as Personal KinetiGraph (http://
globalkineticscorporation.com/the-pkg-system)
orKinesia(http://glneurotech.com/kinesia) have a similar objective but
impose additional burden by requiring that patients use devices
such as smart watches that provide continuous monitoring andassessment of tremors. Search log analyses can complement
these methods on an individual basis and can uniquely provide
population-scale tremor insights across millions of searchengine users.
Scripps Research Translational InstituteIn applications, it is of paramount importance to ensure user
privacy and to provide users with means to opt-in and easily optout of any services related to tremor estimation. Looking ahead onthese and similar applications and analyses, search providers must
keep their users informed and in control. Searchers must consent
to long-term tracking, and search providers should clearlycommunicate both what data are being recorded and how they
are being used. Follow-up studies are required with known tremor
patients with different medical conditions and clinicians to further
validate tremor estimation via mouse cursor movements andevaluate the diagnostic and prognostic utility of this promising
new class of motor signal.
METHODS
Large-scale search log study
Anonymized data were collected from >19M users of the Bing searchengine in the United States (a random sample of the larger user
population). Data were collected from consenting users (per the terms of
the end user license agreement) as part of a separate, unrelated
experiment to measure user engagement with the search engine. Data
collection did not inﬂuence the presentation of search results or otheraspects of the user experience. The experiment ran for 17 days, from
September 19, 2017 to October 5, 2017 inclusive and covered all queries
from searchers included in the study. It used existing methods for mouse
cursor tracking at scale on the Bing search results page.14,15 Mouse cursor
position was logged every 10 px of computer mouse motion. Time-based
sampling and interpolation of cursor positions were not used. Ten pixels
corresponds to approximately 2.5 mm in on-screen distance at thestandard resolution of 96 dots per inch. Only mouse cursor movements
were tracked in this study; on-screen touch or gestural movements were
not monitored. The information in the logs is insufﬁcient to differentiate
between cases where searchers were manipulating a physical computer
mouse controlled with a hand or using a trackpad controlled by a single
ﬁnger.
All analysis was conducted ofﬂine on existing data. The data wererecorded in such a way that searchers cannot be identiﬁed, directly or
through identiﬁers linked to them. Each searcher is represented by aunique identiﬁer (one-way hash) stored in a web browser cookie thatpersisted over multiple search sessions. An external review of the study
protocol by Western Institutional Review Board (WIRB) approved thesearch log study, with the determination that the research met therequirements for IRB exemption under 45 CFR 46.101(b)(4).
Sequences of consecutive cursor location data points are used toestimate hand tremor events. Although the hand is at rest, tremors may
still cause observable variations in the mouse cursor position. The simple
method developed to estimate hand tremors from large-scale cursor datadeﬁned a cursor tremor event as an observed tremor-like movement with(a) at least one oscillation (i.e., a short cursor movement in one cardinal
direction [north, south, east, or west] followed by an immediate movementin the opposite direction) (e.g., move the cursor north [toward the top of
the screen] and then move the cursor south [toward the bottom of thescreen]), and (b) cursor movements of ≤15 px in both directions. Althoughmost cursor movements were recorded every 10 px, the observed
distances were occasionally slightly larger than that due to factors
including network latencies, mouse precision, computational delays, etc.
The tremor estimation threshold was set to 15 px to ensure that thesecursor events were still included in the analysis. Applying this restriction
helps to clearly distinguish cursor-based tremors from intentional actions,such as clicking on a hyperlink or right–left movements associated withreading.24 It is unlikely that computer users would voluntarily produce
such small oscillations in cursor position. Cursor movement patterns that
did not meet these criteria were ignored. Given the coarseness of the
cursor data, more sophisticated tremor detectors such as those utilizing
zero crossing (e.g., ref. 55) could not be used.
In practice, cursor movements will rarely occur precisely along thehorizontal and vertical axes between east–west and north–south,
respectively. To improve the robustness of the tremor estimation andallow minor deviations in cursor direction, 90-degree regions were
established around each of the four cardinal directions: 45° before thecardinal direction and 45° after the cardinal direction. For example, “north”
was deﬁned as the region from north west (315°N) to north east (45°N).
The minimum number of oscillations (nosc) required for a sequence of
npj Digital Medicine (2019) 93

R.W. White and E. Horvitz

6
cursor movements to be classiﬁed as a cursor tremor event can be
adjusted per precision and recall requirements (higher values of nosc
improve precision but harm recall). The total oscillation time in seconds isdetermined using the timestamps for each log entry and used inconjunction with oscillation counts to estimate tremor frequencies inhertz (Hz). There can be multiple cursor tremor events per query
impression, deﬁned as an instance of the submission of a query, thegeneration and presentation of a results page by the search engine, andthe interaction with that page by the searcher. In total, 38,148,131 cursor
tremor events with at least one oscillation (nosc ≥ 1) and frequencies
ranging from 1 to 15 Hz inclusive were extracted from 5,726,038 searchers
across all values of nosc in the range.1,33 Cursor tremor with frequencies<1 Hz or >15 Hz are likely caused by sampling noise and are excluded fromthe dataset.

AUTHOR CONTRIBUTIONSBoth authors designed the study and co-authored the manuscript. In addition, R.W.W.
mined the search logs, implemented and deployed the target selection study
interface, and performed all statistical analyses.

ADDITIONAL INFORMATION
Supplementary information accompanies the paper on the npj Digital Medicine
website (https://doi.org/10.1038/s41746-019-0171-4).
Competing interests: Both the authors are employed by Microsoft and own stock inthe company.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claimsin published maps and institutional afﬁliations.

Target selection study
To help further validate the use of cursor movements as a source of tremor
signal, a crowdsourced study was designed that uses a target-selection
methodology from previous tremor studies.9 The study also collected selfreport data about hand tremors from participants. Participants completed
a consent form at the start of the study. They were recruited from a microtasking vendor (http://clickworker.com) in the English-speaking UnitedStates market. Participants were asked to select (click) with their mouse
cursor 25 targets (squares) positioned randomly in a 1024 × 768 px
bounding box on the screen (all participants used a device with at least
these display dimensions). Participation was limited to one task per
participant and participants were paid 50¢ per task. The x- and ycoordinates of mouse movements and every mouse click (on target and off
target) were recorded using JavaScript. Target width was randomly
selected from {8 px, 16 px, 32 px}. To reduce learning effects, a random
delay of 1–3 s inclusive was applied after each click on a target before thenext target was displayed. After clicking a target, the center point of thenext target was computed at a random angle and a random distance from
{192 px, 384 px, 768 px}. If the next target position was outside thebounding box (even partially), the location was recomputed until the fulltarget was visible. Participants were required to use a standard opticalcomputer mouse rather than stylus pens or touchscreens. JavaScript wasused to enforce the use of a computer mouse. However, it was unable todistinguish between mouse, trackball, and touchpad usage; for that, thestudy relied on participants’ adherence to task guidelines, where it was
clearly stated that only optical mice should be used.
To understand the validity of the cursor tremor estimation method fromthe large-scale study, cursor data were downsampled to retain mouse
cursor positions every 10 px of cursor movement and cursor tremor eventswere deﬁned as oscillations of ≤15 px horizontally or vertically with afrequency of 1–15 Hz inclusive; the same deﬁnitions as earlier. Downsampling reduced the number of mouse cursor events by around 50%.
Immediately after the target selection task, participants completed asurvey to collect demographics (age, gender), computer experience (years),
the presence of hand tremors (yes/no), and the medical condition(s) thatcause these tremors (if known). All questions were optional. The study
protocol was reviewed and approved by the IRB at Microsoft Research.
Methods for both studies were performed in accordance with relevant
guidelines and regulations.

Reporting summary
Further information on research design is available in the Nature Research
Reporting Summary linked to this article.

DATA AVAILABILITY
Data that support the ﬁndings of this study are available from Microsoft, but
restrictions apply to the availability of these data. Data are, however, available fromthe authors upon reasonable request and with permission of Microsoft.

CODE AVAILABILITY
Custom code was written to process the search log data. This code was written in a
Microsoft proprietary programming language (SCOPE) and is not usable outside of
the company.

npj Digital Medicine (2019) 93

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npj Digital Medicine (2019) 93