Skip to content

When you choose to publish with PLOS, your research makes an impact. Make your work accessible to all, without restrictions, and accelerate scientific discovery with options like preprints and published peer review that make your work more Open.


Looking to the future: What can smartphones tell us about gait?

The 21st of October 2015 was the day Doc Brown and Marty McFly from the famous movie franchise ‘Back to the Future’ were supposed to arrive in the future, all the way from 1985. This day was supposed to be ripe with flying cars, hoverboards and self-drying jackets. They would have found it sadly lacking (though a version of hoverboards seem to be an emerging reality, albeit a dangerous one). However, Nike strived to have one such thing become a reality for this highly anticipated day: the first self-tying sneakers. The significance of this design was not lost upon Michael J. Fox, the man behind Marty McFly who has developed Parkinson’s Disease (PD) since filming the series. As show in the video below, Nike presented the sneakers to Fox, with a note that all proceeds from the sale of these limited edition Nike Mags would go towards PD research. But have you thought about what is going on when Michael J. Fox puts those sneakers on the ground and begins to walk? And what his walk, and the walk of the many other individuals with PD, can tell us about the disease?

PD is a common neurodegenerative disorder associated with aging, caused by loss of dopaminergic neurons in the basal ganglia. With approximately four million people living with PD in 2005, these numbers are predicted to reach more than nine million by 2030. PD is recognised by four cardinal symptoms: resting tremor, rigidity, slowness of movement and unstable posture. Other features of the disease include loss of autonomic functions, sleep disturbances and neuropsychiatric problems such as depression, anxiety, cognitive impairments and psychosis. The increasing numbers of PD sufferers affects not only the individuals and their day-to-day living, it also has a major impact on the economy. In 2011, researchers found that the total cost of healthcare for the 1.2 million PD patients in Europe rounded up to €13.9 billion. This has greater implications for developing countries where expertise in care for PD patients is extremely limited. This phenomena is evident in China, one of the largest countries in the world, where it is estimated that 68% and 37% of the rural and urban population respectively suffer from undiagnosed PD. The situation is compounded by the significantly smaller proportion of neurologists and movement disorder specialists in China than that of wealthier countries. It is apparent that cost-effective and easily accessible diagnostic tools need to be developed to combat the rapid rise in PD and provide better support to PD patients, particularly those living in developing countries.

Gait and Parkinson’s Disease

One of the most notable features of PD is the presence of shuffling gait. It is so characteristic of the disorder, in fact, that Ramachandran noted in his book Phantoms in the Brain that an old professor insisted on his class diagnosing PD with their eyes shut, listening only for the sound of the patients’ distinct footsteps. This distinguishing feature is caused by changes to a variety of different gait domains. A study investigating 16 gait characteristics within the domains of pace, postural control, asymmetry, variability and rhythm reported that there was a significant impairment in 12 of the discrete variables for PD patients when compared to healthy controls. These changes occur subtly over time and usually 80% of the dopaminergic neurons in the basal ganglia have depleted by time of diagnosis. By this time, motor symptoms are prominent and impact heavily on everyday living. Optimal treatment of PD relies on an early diagnosis – which we may be able to provide by developing more sensitive measures to assess motor function.

We can obtain information about an individual’s walk with just a stopwatch and a long corridor. This has been a popular method for clinicians and researchers without access to more sophisticated analytical methods – but it only provides information about one aspect of gait: speed. This does not allow a comprehensive insight into functions of many subtle qualities of gait. The gold standard in gait assessment technology is 3D motion analysis systems. These measure both spatiotemporal characteristics, such as walking speed and stride length, and dynamic features, such as consistency across steps, in gait. Another popular measure of gait performance is pressure-sensor walkways, such as GAITRite, which captures information about gait characteristics from an individual’s footfall patterns. However, both methods prove expensive and require lab-based settings, which may not provide an accurate picture of an individual’s gait. Wearable sensors such as tri-axial accelerometers and gyroscopes have been useful in measuring acceleration, motion and posture. These are not costly and measure gait parameters effectively; however, they may require specialist knowledge to interpret the data obtained.

How do we make gait analysis technology more accessible?

The SmartMOVE mobile app (a.) utilizes the smartphone’s inertial measurement unit to record gait movements during walking. Flexible parameter settings (b.) enable precise control over testing parameters. SmartMOVE outcome measures were validated against heel-mounted footswitches and a GAITRite sensor walkway (c.) while subjects walked along a prescribed path (d.). Figure courtesy Ellis and co. via PLOS ONE.
The SmartMOVE mobile app (a.) utilizes the smartphone’s inertial measurement unit to record gait movements during walking. Flexible parameter settings (b.) enable precise control over testing parameters. SmartMOVE outcome measures were validated against heel-mounted footswitches and a GAITRite sensor walkway (c.) while subjects walked along a prescribed path (d.). Figure courtesy Ellis and co. via PLOS ONE.

A study recently published in PLOS ONE set out to create a gait assessment tool that could be both cost-effective and easy to interpret for the general public. The researchers outline the necessary components to an optimal assessment system: sensing hardware recording participants’ movements, analysis software that interprets collected signals into outcome measures, and a display unit that communicates those outcomes. The researchers suggest that smartphones are the perfect tool for such assessment as most contain accelerometers and are affordable, customisable and portable. However, such methods have generally been employed on healthy individuals and thus invoke questions about their abilities to assess gait in PD patients. They also require concurrent validation with a conventional gait measurement system. The researchers aimed to determine the accuracy of outcome measures recorded by smartphones relative to heel-contact derived outcome measures of gait parameters in both individuals with PD and healthy controls (HCs).

There were 12 participants in each group. All subjects were aged between 40-85, screened for any factors that would affect walking ability and showed no signs of cognitive impairment. An app called SmartMOVE was used with the Apple iPod Touch, which was secured to the participants’ navels using elastic straps. This recorded tri-axial acceleration and gyroscopic rotation rate. A foot switch sensor was attached to each heel and subjects were also required to walk up and down a 26 metreGAITRite mat. All subjects participated in three conditions: self-paced walking, walking with a rhythmic auditory cue (RAC) and walking with a faster tempo RAC.

Results were similar to previous literature with PD patients displaying slower and more varied walking in the self-paced conditions than HCs and demonstrating increased walking speed in the faster tempo RAC than the self-paced condition. This study did obtain concurrent validity for SmartMOVE as a gait analysis technique, but the app did show inflated outcome measures for gait variability. Investigators maintain that SmartMOVE can offer moderate to high accuracy for gait assessment and could be particularly beneficial to clinicians without access to conventional approaches. They also suggest that use of the camera on the device could provide more detailed information about factors affecting gait, especially outside of clinical settings. While there are still details to be smoothed out and improved upon, use of smartphone-based technology appears to have an optimistic future in gait analysis.

Where do we go from here?

A woman walks with the support of a cane in Kuala Lumpar, Malaysia. Photo "Just Walk This Way" by Neil Moralee, CCBY via Flickr
A woman walks with the support of a cane in Kuala Lumpar, Malaysia. Photo “Just Walk This Way” by Neil Moralee, CCBY via Flickr.

The use of smartphones in gait analysis is an area ripe for exploration. In addition to their cost-effective nature, the application could be customised to meet the needs of the researchers, end user or technician. Smartphones could also allow long-distance transmission of data. This would be beneficial for researchers as the ease of gathering data could encourage higher study participation. Using smartphones for gait analysis could also allow clinicians to assess the efficacy of mobility improving interventions. However, information about smartphones’ analytical performance in cases of severe gait dysfunction is still lacking and a wider sampling among different PD stages is necessary to improve the robustness and accuracy of such measures. Studies in this area have also assessed different gait measures and employed various smartphone applications and placement of the device on the body. More consistency between research groups would provide a better understanding of the efficacy of such methods of gait assessment.

Gait analysis not only provides vital information about PD, but also can be useful in many different healthcare areas. About 30% of the 65+ population fall at least once a year, with their fall risk increasing after each fall. Falls can have extremely adverse effects on the health of older adults, increasing their liability to injuries, loss of independence, and even death. Research shows that quantitative gait markers, such as slower gait speed and increased variability, have a strong predictive relationship with falls. Gait analysis could reveal these gait impairments as they subtly begin to progress. This could allow implementations for safer living to be put in place, such as increased ambulatory support in homes. Researchers have also been focusing on the relationship between gait impairments and cognitive function, with findings indicating that a decline in cognitive abilities, such as executive function and attention, are associated with changes in gait speed and variability. This could be predictive of dementia onset as slowing of gait has already been observed prior to onset of mild cognitive impairment. Reports such as these highlight the advantages of accessible gait analytical technology to both clinicians and researchers. Hopefully, we will see improvements in the availability and application of such techniques long before we take a ride in our first flying car.

Achey, M., Aldred, J. L., Aljehani, N., Bloem, B. R., Biglan, K. M., Chan, P., …& Wilkinson, J. R. (2014). The past, present, and future of telemedicine for Parkinson’s disease. Movement Disorders, 29(7), 871-883.

Bartels, A. L., &Leenders, K. L. (2009). Parkinson’s disease: The syndrome, the pathogenesis and pathophysiology. Cortex, 45(8), 915-921.
Beurskens, R., & Bock, O. (2012). Age-related deficits of dual-task walking: a review. Neural plasticity, 2012.

Buracchio, T., Dodge, H. H., Howieson, D., Wasserman, D., & Kaye, J. (2010). The trajectory of gait speed preceding mild cognitive impairment. Archives of neurology, 67(8), 980-986.

Chaudhuri, K. R., Healy, D. G., &Schapira, A. H. (2006). Non-motor symptoms of Parkinson’s disease: diagnosis and management. The Lancet Neurology,5(3), 235-245.
Dauer, W., &Przedborski, S. (2003). Parkinson’s disease: mechanisms and models. Neuron, 39(6), 889-909.

Dorsey, E. R., Constantinescu, R., Thompson, J. P., Biglan, K. M., Holloway, R. G., Kieburtz, K., …& Tanner, C. M. (2007). Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030.Neurology, 68(5), 384-386.

Ellis, R. J., Ng, Y. S., Zhu, S., Tan, D. M., Anderson, B., Schlaug, G., & Wang, Y. (2015). A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease. PloS one, 10(10), e0141694.

Fritz, S., & Lusardi, M. (2009). White paper:“walking speed: the sixth vital sign”. Journal of geriatric physical therapy, 32(2), 2-5

Galna, B., Lord, S., Burn, D. J., & Rochester, L. (2015). Progression of gait dysfunction in incident Parkinson’s disease: Impact of medication and phenotype. Movement Disorders, 30(3), 359-367.

Jankovic, J. (2008). Parkinson’s disease: clinical features and diagnosis.Journal of Neurology, Neurosurgery & Psychiatry, 79(4), 368-376.

Lord, S., Galna, B., & Rochester, L. (2013). Moving forward on gait measurement: toward a more refined approach. Movement Disorders, 28(11), 1534-1543.

Olesen, J., Gustavsson, A., Svensson, M., Wittchen, H. U., &Jönsson, B. (2012). The economic cost of brain disorders in Europe. European Journal of Neurology, 19(1), 155-162.

Ramachandran, V. S., & Blakeslee, S. (1999). Phantoms in the Brain: Human Nature and the Architecture of the Mind. Fourth Estate.

Tao, W., Liu, T., Zheng, R., & Feng, H. (2012). Gait analysis using wearable sensors. Sensors, 12(2), 2255-2283.

Verghese, J., Robbins, M., Holtzer, R., Zimmerman, M., Wang, C., Xue, X., & Lipton, R. B. (2008).Gait dysfunction in mild cognitive impairment syndromes.Journal of the American Geriatrics Society, 56(7), 1244-1251


Leave a Reply

Your email address will not be published. Required fields are marked *

Add your ORCID here. (e.g. 0000-0002-7299-680X)

Back to top