Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Giancardo, Luca; Sánchez‐Ferro, Álvaro; Butterworth, I.; Mendoza, Carlos S.; Hooker, Jacob M.

doi:10.1038/srep09678

Cited by 39 publications

(37 citation statements)

References 35 publications

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“…The accuracy of keystroke timing data was 3.4 ± 2.0 mS (across 15 random participants with various Windows computers and keyboard types), measured according to the software delay of processing Windows keyboard events. This is consistent with the timing accuracy in another study [28] (being 3.2 ± 0.8 mS), using a similar measurement but on a known Windows computer configuration. Typing characteristics of participants As already mentioned, the methodology required each participant to type continuous text segments of at least 50 keystrokes for each hand, equating to 100 keystrokes total, or approximately 15 words.…”

Section: Keystroke Recordingsupporting

confidence: 90%

The detection of hand tremor through the characteristics of finger movement while typing

Adams

2018

Preprint

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Parkinson's Disease (PD) is a neurodegenerative movement disease affecting over 6 million people worldwide. Current diagnosis is based on clinical and observational criteria only, resulting in a high misdiagnosis rate. Approximately 75% of people with PD have hand tremor, which can precede clinical diagnosis by up to 6 years. Previous studies have shown that early PD can be accurately detected from keystroke features while typing, and this study investigated whether tremor can be detected as well. Typing data from 76 subjects, with and without PD, including 27 with PD and 15 with tremor, was analysed and showed that hand tremor in PD can be detected from keystroke features. This novel technique has not been used before and was able to achieve an overall sensitivity of 67% and a specificity of 80% and was also able to differentiate PD tremor from essential tremor. This means that the diagnosis of early PD through typing can achieve the clinical requirement of at least two cardinal features being present (bradykinesia and tremor). Less than half a page of typing is needed, the technique does not require any specialised equipment, and can take place in the patient's home as they type normally on a computer.

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Section: Keystroke Recordingsupporting

confidence: 90%

The detection of hand tremor through the characteristics of finger movement while typing

Adams

2018

Preprint

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“…We used a machine‐learning model (nQRNN) that receives as input typing features derived from the hold time, that is, the time required to press and release each key on a participant's laptop, regardless of the text typed. The typing features are encoded as “Key Hold Time Distribution” matrices joined with the encoding previously described . nQRNN outputs the probability of each patient of being a responder or nonresponder and were employed to generate the plots in Figures and .…”

Section: Methodsmentioning

confidence: 99%

Remote Monitoring of Treatment Response in Parkinson's Disease: The Habit of Typing on a Computer

et al. 2019

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Objective The recent advances in technology are opening a new opportunity to remotely evaluate motor features in people with Parkinson's disease (PD). We hypothesized that typing on an electronic device, a habitual behavior facilitated by the nigrostriatal dopaminergic pathway, could allow for objectively and nonobtrusively monitoring parkinsonian features and response to medication in an at‐home setting. Methods We enrolled 31 participants recently diagnosed with PD who were due to start dopaminergic treatment and 30 age‐matched controls. We remotely monitored their typing pattern during a 6‐month (24 weeks) follow‐up period before and while dopaminergic medications were being titrated. The typing data were used to develop a novel algorithm based on recursive neural networks and detect participants’ responses to medication. The latter were defined by the Unified Parkinson's Disease Rating Scale‐III (UPDRS‐III) minimal clinically important difference. Furthermore, we tested the accuracy of the algorithm to predict the final response to medication as early as 21 weeks prior to the final 6‐month clinical outcome. Results The score on the novel algorithm based on recursive neural networks had an overall moderate kappa agreement and fair area under the receiver operating characteristic (ROC) curve with the time‐coincident UPDRS‐III minimal clinically important difference. The participants classified as responders at the final visit (based on the UPDRS‐III minimal clinically important difference) had higher scores on the novel algorithm based on recursive neural networks when compared with the participants with stable UPDRS‐III, from the third week of the study onward. Conclusions This preliminary study suggests that remotely gathered unsupervised typing data allows for the accurate detection and prediction of drug response in PD. © 2019 International Parkinson and Movement Disorder Society

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“…An extraordinary level of personal information can already be obtained from people's data trails. Researchers at the Massachusetts Institute of Technology in Cambridge, for example, discovered in 2015 that fine-grained analysis of people's motor behaviour, revealed through their keyboard typing patterns on personal devices, could enable earlier diagnosis of Parkinson's disease 7 . A 2017 study suggests that measures of mobility patterns, such as those obtained from people carrying smartphones during their normal daily activities, can be used to diagnose early signs of cognitive impairment resulting from Alzheimer's disease 8 .…”

Section: Four Concernsmentioning

confidence: 99%

Four ethical priorities for neurotechnologies and AI

Yuste

Goering

Arcas

et al. 2017

Nature

339

217

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A man with a spinal-cord injury (right) prepares for a virtual cycle race in which competitors steer avatars using brain signals. COMMENT © 2 0 1 7 M a c m i l l a n P u b l i s h e r s L i m i t e d , p a r t o f S p r i n g e r N a t u r e . A l l r i g h t s r e s e r v e d .example. Moreover, researchers can already interpret a person's neural activity from functional magnetic resonance imaging scans at a rudimentary level 1 -that the individual is thinking of a person, say, rather than a car.It might take years or even decades until BCI and other neurotechnologies are part of our daily lives. But technological developments mean that we are on a path to a world in which it will be possible to decode people's mental processes and directly manipulate the brain mechanisms underlying their intentions, emotions and decisions; where individuals could communicate with others simply by thinking; and where powerful computational systems linked directly to people's brains aid their interactions with the world such that their mental and physical abilities are greatly enhanced.Such advances could revolutionize the treatment of many conditions, from brain injury and paralysis to epilepsy and schizophrenia, and transform human experience for the better. But the technology could also exacerbate social inequalities and offer corporations, hackers, governments or anyone else new ways to exploit and manipulate people. And it could profoundly alter some core human characteristics: private mental life, individual agency and an understanding of individuals as entities bound by their bodies.It is crucial to consider the possible ramifications now.The Morningside Group comprises neuroscientists, neurotechnologists, clinicians, ethicists and machine-intelligence engineers. It includes representatives from Google and Kernel (a neurotechnology start-up in Los Angeles, California); from international brain projects; and from academic and research institutions in the United States, Canada, Europe, Israel, China, Japan and Australia. We gathered at a workshop sponsored by the US National Science Foundation at Columbia University, New York, in May 2017 to discuss the ethics of neurotechnologies and machine intelligence.We believe that existing ethics guidelines are insufficient for this realm 2 . These include the Declaration of Helsinki, a statement of ethical principles first established in 1964 for medical research involving human subjects (go.nature.com/2z262ag); the Belmont Report, a 1979 statement crafted by the US National Commission for the Protection of Human Subjects of Biomedical and Behavioural Research (go.nature.com/2hrezmb); and the Asilomar artificial intelligence (AI) statement of cautionary principles, published early this year and signed by business leaders and AI researchers, among others (go.nature.com/2ihnqac).To begin to address this deficit, here we lay out recommendations relating to four areas of concern: privacy and consent; agency and identity; augmentation; and bias. Different nations and people of varying re...

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Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Cited by 39 publications

References 35 publications

The detection of hand tremor through the characteristics of finger movement while typing

The detection of hand tremor through the characteristics of finger movement while typing

Remote Monitoring of Treatment Response in Parkinson's Disease: The Habit of Typing on a Computer

Four ethical priorities for neurotechnologies and AI

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