Gait assessment - Kinesis Health Technologies

Quantitative measurement of movement using wearable sensors - a valuable clinical tool for rehabilitation.

Kinesis Gait™: Accurate, reliable and portable gait assessment

Quantitative analysis of gait

Gait analysis is the study of human locomotion and is made up of up rhythmic stepping motions; quantitative measurement of gait is a valuable tool in clinical practice and rehabiltation and as a clinical endpoints for research.
In normal gait as a person moves forward, one leg will support their weight while the other leg will swing forward in order to provide support for the next stride.
Each stride, (also known as a gait cycle (GC)) is composed of stance and swing phases, which are defined using the time each heel hits the ground (known as heel strike or initial contact) and the time each toe leaves the ground (known as toe-off time or terminal contact). Quantitative gait analysis is essentially the analysis of each of these phases, their relationship to each other and their variation over time and space.

Wearable sensors are relatively low-cost and can be used to accurately and reliably measure gait in the field.

Each stride is composed of stance and swing phases

What are the gait parameters?

Gait parameters are standardised measurements used to quantify gait and can be broadly categorised into two groups: temporal and spatial gait parameters. Common temporal gait parameters are stride time (defined as the time from one heel strike to the next heel strike of the same foot), swing time (defined as the time during a stride when foot is not on the ground) and double support time (defined as the proportion of a gait cycle spent in double support, i.e. standing on two feet).
Similarly, common spatial gait parameters are gait velocity (walking speed) and stride length (distance travelled during a stride). Gait variability then examines the variation of these quantitative gait parameters (usually through calculation of the coefficient of variation). Asymmetric gait can be examined by quantitatively assessing relationships of the stride patterns in one leg compared to the other.

Gait analysis
Quantitative gait parameters can be broadly categorised into spatial and temporal gait parameters
Mobility & Gait assessment

How can Kinesis Gait™ help to identify gait impairment?

Kinesis Gait™ is a portable clinical tool for quantitative assessment of gait and mobility. Using wireless sensors worn on the legs, Kinesis Gait™ can monitor gait and mobility during any clinical walking test protocol (e.g. 25 foot walk or 6 minute walk).

Quantitative gait analysis can be used to identify the presence of gait abnormality, pathological gait or specific gait deviations associated with injury and disease, predict falls in the elderly, and quantify improvements due to rehabilitation (1-3).

Wireless sensors placed on the legs during a Kinesis Gait assessment

What can quantitative gait analysis do for assessment of gait in Multiple Sclerosis and Parkinson's disease?

Multiple Sclerosis

Multiple Sclerosis (MS) is a chronic, progressive neurological disorder affecting between 2 and 2.5 million people globally (10). Impaired mobility is a common symptom of MS, even at lower levels of the disease, and has significant negative effects on quality of life (11). Current best practice for clinical assessment of MS includes assessment of gait and mobility. Quantitative assessment of mobility using wearable sensors has value as a novel clinical endpoint for clinical trials, providing objective, longitudinal monitoring of patients with MS.

Why use quantitative gait analysis
for assessing MS?

  • Monitor disease progression and response to disease modifying therapy (DMT)
  • Screen for mobility problems associated with early stage MS
  • Detailed analysis and comparison of temporal, spatial variability and symmetry measures of gait to reference values

Recent research (12,13) has shown that Kinesis systems provides reliable measures of mobility from MS patients while completing the TUG test, and may have utility in assessing disease state as measured using standard clinical measures of MS (Expanded Disease Status Scale - EDSS and Multiple Sclerosis Impact Scale - MSIS-20). Inertial sensor technologies have utility in screening for early stage MS (compared against healthy control subjects).

Parkinson's disease

Parkinson's disease (PD) is a chronic neurodegenerative disorder of the central nervous system affecting approximately seven million people worldwide. Gait and balance problems as well as increased risk of falls are strongly associated with Parkinson's. Objective measurement of gait and mobility may be a reliable, objective method for ongoing assessment of global motor function in PD patients.

Why use quantitative gait analysis
for assessing Parkinson's?

  • Screen for mobility problems associated with Parkinson's
  • Ongoing, objective measurement of response to treatment
  • Colour-coded analysis of 31 gait parameters compared against a large reference population

Recent research (14-16) has shown that Kinesis QTUG™ is accurate in predicting falls in PD and can provide reliable measures of gait and mobility in a variety of populations, including Parkinson's. Furthermore, QTUG™ may be useful as an ongoing, objective measure in assessing response to therapy and medication.

Discover how Kinesis Gait™ can help you improve gait assessment now!


  1. Paul SM, Siegel KL, Malley J, Jaeger RJ. Evaluating interventions to improve gait in cerebral palsy: a meta-analysis of spatiotemporal measures. Dev Med Child Neurol. 2007;49(7):542-549.
  2. Shrader MW, Bhowmik-Stoker M, Jacofsky MC, Jacofsky DJ. Gait and stair function in total and resurfacing hip arthroplasty: a pilot study. Clin Orthop Relat Res. 2009;467(6):1476-1484.
  3. Delahunt E, Monaghan K, Caulfield B. Altered neuromuscular control and ankle joint kinematics during walking in subjects with functional instability of the ankle joint. Am J Sports Med. 2006;34(12):1970-1976.
  4. Greene BR, Foran T, McGrath D, Doheny EP, Burns A. A comparison of algorithms for body-worn sensor based spatio-temporal gait parameters to GAITRite electronic walkway. J. Applied Biomech. 2012;28(3):349-355.
  5. Greene BR, McGrath D, O'Neill R, O'Donovan KJ, Burns A, Caulfield B. An adaptive gyroscope based algorithm for temporal gait analysis. Medical & Biological Engineering & Computing. 2010;48(12):1251-1260.
  6. D. Podsiadlo and S. Richardson, "The timed "Up & Go": a test of basic functional mobility for frail elderly persons," J Am Geriatr Soc, vol. 39, pp. 142-148, 1991.
  7. A. Shumway-Cook, S. Brauer, and M. Woollacott, "Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test," Phys Ther, vol. 80, pp. 896-903, 2000.
  8. American Geriatric Society, British Geriatric Society, and Orthopaedic Surgeons Panel On Falls Prevention, "Guideline for the Prevention of Falls in Older Persons," Journal of the American Geriatrics Society, vol. 49, pp. 664-672, 2001, "Guideline for the prevention of falls in older people," J Am Geriatr Soc, vol. 49, pp. 664-72, 2001.
  9. National Institute for Clinical Excellence (NICE), Clinical practice guideline for the assessment and prevention of falls in older people: National Institute of Clinical Excellence, 2004.
  10. World Health Organization (WHO). Atlas: Multiple Sclerosis Resources in the World. Geneva, 2008.
  11. Zwibel HL. Contribution of impaired mobility and general symptoms to the burden of multiple sclerosis. Adv Therapy. 2009;26(12):1043-1057.
  12. Greene BR, Healy M, Rutledge S, Caulfield B, Tubridy N. Quantitative assessment of multiple sclerosis using inertial sensors and the TUG test. IEEE Engineering in Medicine and Biology; 26-30 Aug. 2014.
  13. Greene BR, Rutledge S, McGurgan I, et al. Assessment and Classification of Early-Stage Multiple Sclerosis With Inertial Sensors: Comparison Against Clinical Measures of Disease State. IEEE Journal of Biomedical and Health Informatics, 2015;19(4):1356-1361.
  14. Barry R. Greene, Brian Caulfield, Dronacharya Lamichhane, William Bond, Jessica Svendsen, Connie Zurski, Dyveke Pratt, "Longitudinal assessment of falls in Parkinson’s disease using inertial sensors and the Timed Up and Go test", Journal of Rehabilitation and Assistive Technologies Engineering 2018; 5. DOI: 10.1177/2055668317750811
  15. K. McManus, D. McGrath, B. R. Greene, O. Lennon, L. McMahon, and B. Caulfield, "Impact of Exercise Intervention in Parkinson’s Disease can be Quantified Using Inertial Sensor Data and Clinical Tests," in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 3507-3510, Berlin, Germany, July 2019.
  16. J. Somerset, B. Hammersley, M. Bonello, “Can the Quantified Timed Up and Go (QTUG) device support decision making for patients undergoing Deep Brain Stimulation?”, 2019 International Congress of the International Parkinson and Movement Disorder Society, September 22-26, 2019 in Nice, France.