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Diagnosing Parkinson’s Disease

First symptoms of Parkinson's
KeySense - Diagnosis of Parkinson's Disease
® KeySense is a registered trademark of Parkinson’s Research (Australia) © Copyright 2022 Parkinson’s Research (Australia)

Parkinsons Research

(AUSTRALIA)

How KeySense Works

There has been a need for more accurate and objective diagnoses of Parkinson’s Disease (PD), particularly in its early stages where the observable symptoms may be subtle and imprecise. In order to be effective, such a new assessment technique needs to - Be significantly more accurate than current non-specialist clinician diagnoses. Provide an objective, quantifiable and repeatable diagnostic test for PD. Be able to diag- nose PD where there are just mild symptoms present. Be able to detect it significantly earlier in the disease progression. Not require specialised equipment, training or referral. Take place in the patient’s home or office environment as they type normally on a computer. Also be capable of providing ongoing monitoring of medication, treatments & disease progression.

KeySense Methodology

This involves the use of typing – by analyzing the rhythm and cadence of keystrokes as someone types on a keyboard. There is a wealth of information embedded in those keystroke. Way back in the mid-19th century, when the tele- graph was in wide use, telegraph opera- tors could identify other operators based on their rhythm as they tapped on the telegraph key. Much later, during World War II, a methodology known as the ‘Fist of the Sender’ was used to identify the sender of the telegraph by using the rhythm, pace and cadence of their Morse code. Even without decoding the messages, this allowed German troop movements to be monitored by the Allied
Powers, as particular telegraph operators were attached to a specific army unit. These days, as someone types on a computer keyboard, the characteristics of their finger movement can be measured down to millisecond accuracy.

KeySense Development

Based on 4 years of peer-reviewed scientific research, with nearly 500 worldwide participants and 9,000 typing samples. Capture & recording of keystroke char- acteristics as someone types normally on a computer keyboard. Detection of separate types of PD feature, especially the cardinal features required for clinical diagnosis - bradyki- nesia, rigidity & tremor. Use of sophisticated artificial intelli- gence (AI) machine learning models. As implemented, the technique provides objective, quantifiable, repeatable measurements and results.

The Outcome

Provides a tool for both detection & ongoing monitoring of disease status and
progression. The patient can take the assessment reports to their medical practitioner (GP) or specialist. KeySense has a high and repeatable accuracy, with a 90% - 95% detection rate for even early, mild symptoms of Parkinson’s. This compares with GP accuracies of less than 75% across all disease severities, and much lower than that for early, mild cases. See here for more details.

The User Assessment Process

The user types 350 words of text (two thirds of a page) on their own computer and KeySense records a myriad of timings about their finger movement. This data is then fed through a series of 11 machine learning models which identify the probabilities of specific movement features being within – or outside – normal expected ranges. Specifically, KeySense measures incon- sistency, drop-off and sidedness of movement and tremor, as well as combining these into an overall score. The results are accompanied by descrip- tive details which are also emailed to the user.

Privacy and Anonymity

The KeySense assessment does not need any personal details to be provided and the participants remain anonymous at all times.
How KeySense works for Parkinson's diagnosis Detection of Parkinson's using typing on a keyboard with KeySense
PARKINSONS RESEARCH
© 2022 Parkinson’s Research (Australia)

How KeySense Works

There has been a need for more accurate and objective diagnoses of Parkinson’s Disease (PD), particularly in its early stages where the observable symptoms may be subtle and imprecise. In order to be effective, such a new assessment technique needs to - Be significantly more accurate than current non- specialist clinician diagnoses. Provide an objective, quantifiable and repeatable diag- nostic test for PD. Be able to diagnose PD where there are just mild symp- toms present. Be able to detect it significantly earlier in the disease progression. Not require specialised equipment, training or referral. Take place in the patient’s home or office environment as they type normally on a computer. Also be capable of providing ongoing monitoring of medication, treatments & disease progression.

KeySense Methodology

This involves the use of typing – by analyzing the rhythm and cadence of keystrokes as someone types on a keyboard. There is a wealth of information embedded in those keystroke. Way back in the mid-19th century, when the telegraph was in wide use, telegraph operators could iden- tify other operators based on their rhythm as they tapped on the telegraph key. Much later, during World War II, a methodology known as the ‘Fist of the Sender’ was used to identify the sender of the telegraph by using the rhythm, pace and cadence of their Morse code. Even without decoding the messages, this allowed German troop move- ments to be monitored by the Allied Powers, as particular telegraph operators were attached to a specific army unit. These days, as someone types on a computer keyboard, the characteristics of their finger movement can be measured down to millisecond accuracy.

KeySense Development

It has been based on 4 years of peer-reviewed scientific research, with nearly 500 worldwide participants and 9,000 typing samples. Capture & recording of keystroke characteristics as someone types normally on a computer keyboard. Detection of 4 separate types of PD feature, especially two of the cardinal features for clinical diagnosis - bradykinesia & tremor. Use of sophisticated artificial intelligence (AI) machine learning models. As implemented, the technique provides objective, quan- tifiable, repeatable measurements and results.

The Outcome

Provides a tool for both detection & ongoing monitoring of disease status and progression. The patient can take the assessment reports to their medical practitioner (GP) or specialist. KeySense has a high and repeatable accuracy, with an 86% detection rate for even early, mild symptoms of Parkinson’s, and just 5% false positives. This compares with GP accuracies of less than 75% across all disease severities, and much lower than that for early, mild cases.

The User Assessment Process

The user types 350 words of text (two thirds of a page) on their own computer and KeySense records a myriad of timings about their finger movement. This data is then fed through a series of machine learning models which identify the probabilities of specific move- ment features being within – or outside – normal expected ranges. Specifically, KeySense measures inconsistency, drop-off and sidedness of movement and tremor, as well as combining these into an overall score. The results are accompanied by descriptive details which are also emailed to the user.

Privacy and Anonymity

The KeySense assessment does not need any personal details to be provided and the participants remain anony- mous at all times.

GET STARTED NOW

See whether the way you type shows any indications of early Parkinson’s Disease. Less than a page is needed for immediate analysis, based on characteris - tics and rhythms in your typing. YOU NEED TO BE USING A DEVICE WITH A PHYSICAL KEYBOARD THOUGH. SO GO TO A LAPTOP OR DESKTOP COMPUTER & RE-VISIT WWW.PARKINSONS-RESEARCH.ORG
Detection of Parkinson's using typing on a keyboard with KeySense PARKINSONS RESEARCH (AUSTRALIA)