Discover QTUG™
Kinesis Health Technologies is a leading provider of scientifically validated, machine learning technologies for evaluation of falls risk and rehabilitation as well as for monitoring response to subsequent preventive interventions.
From specialized laboratories to ambulatory and residential care, Kinesis technologies provide reliable measures, are easy to use and set up, and provide immediate, exportable results. Designed to cover many test conditions, from long walking trials on a treadmill to protocols restricted by space or time, our range of products enable assessment of participants of all ages and physical conditions, accounting for mobility aids, prosthesis, orthotics, and rehabilitation.
Please get in touch to find out more!
Our CEO Seamus Small discusses a recent survey of older adult experience with Kinesis QTUG assessments in four senior living facilities in the US.
Results were very encouraging, with 91% of participants feeling that the QTUG™ assessment was informative
and 93% saying they would recommend a QTUG™ assessment to other residents in their community!
Click here>> to read Seamus's blog.
In our new case study, we analysed from data 1,133 clients, assessed with Kinesis Gait™ in a US physical therapy clinic.
The case study highlights the value of gait analysis solutions for physical therapy practices, how measuring the impact of physical therapy on a client over multiple visits
and comparing clients across the population can be easily achieved using Kinesis Gait™.
Click here>> to read the case study.
Kinesis Balance, our smartphone solution for remote self-assessment of falls risk and balance.
Using machine learning algorithms combined with smartphone-embedded sensors and questionnaires, Kinesis Balance allows the reliable assessment of balance and falls risk in the home.
The app is now available on Google Play.
Please try it out and let us know if you have any feedback!
Discover Kinesis Balance™ now!
Portable, flexible, clinical-grade Gait Analysis, validated through thirteen years of research.
Proven and reliable measurement of gait. 89 different gait measures. Provides mean and bilateral temporal
and spatial gait parameters as well as gait variability, gait symmetry. Raw sensor data and individual stride data are also provided.
Record data for multiple walking tasks (including dedicated 6 minute walk mode, 30 metre walk and 25 foot walk).
Automatic analysis of patient data against reference values for patient's age and gender providing color-coded percentile scores to identify mobility impairment.
Kinesis QTUG is an objective tool for identifying older adults at risk of falling. QTUG™ (Quantitative Timed Up and Go) is based on the Timed Up and Go test and is instrumented with wireless sensors placed on each leg. This technology provides a method for objective assessment of mobility, frailty and falls risk. It provides automatic analysis of patient data against reference values for patient's age and gender with colour coding to provide percentile scores for mobility and indicate deviations from normality.
QTUG™ is used by a range of healthcare professionals and care staff to assess mobility and falls risk in the older adult populations they serve.
Older adults respond positively to the objective assessment, advice and guidance that QTUG™ provide to engage in falls prevention.
QTUG™ also incorporates a questionnaire based on the American Geriatric Society (AGS) and British Geriatric Society (BGS) guidelines recording standard falls risk-factors.
Kinesis Screen is a free tool, to allow an older adult to determine if they are at risk of falls. Based on 13 years of research and over 5,000 patients, Kinesis Screen is a secure portal to empower, engage and motivate older adults to manage fall risk factors and prevent falls from occurring.
Population health analysis of fall risk, based on a simple questionnaire and using our proprietary machine learning algorithms. Provides validated advice and guidance via email and web browser to help prevent falls.
Kinesis Screen™ - empowering fall prevention!
