Beom-Chan Lee, Jeonghee Kim, Shu Chen and Kathleen H Sienko*
Corresponding author: Kathleen H Sienko email@example.com
Journal of NeuroEngineering and Rehabilitation 2012, 9:10 doi:10.1186/1743-0003-9-10
(2012-02-20 11:56) Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Harvard Medical School
Technology that enables clinicians and researchers to quantify and treat balance and
gait problems is rapidly evolving. For a long time, laboratory-based, specialized
equipment that included multiple cameras and embedded force platforms were the heart
and soul of the analysis of gait and balance. Many treatments were also centered around
these powerful technologies. Over the past two decades or so, much effort has been
invested into the use of wearable computers, also commonly referred to as body-fixed
sensors. Advantages of an approach based on body-fixed sensors include cost and space,
and the possibility of bringing the measurement system to the patient and clinic rather
than the other way around. An additional key advantage is the potential for long-term
recordings that may more closely reflect everyday abilities, their dynamics and changes
over time and that may be less prone to "white coat" syndromes [1,2].
More recently, a number of studies have explored the use of body-fixed sensors as
the basis of therapeutic interventions [3,4]. Here too, issues of cost and accessibility
play an important role. A traditional approach requires a patient to make her way
to an outpatient clinic several times a week. However, with body-fixed sensors, therapy
can be performed in the home, potentially enhancing compliance and the frequency and
duration of treatment, thereby augmenting the therapeutic benefits, while lowering
costs, travel time, and therapist time.
The pilot study by B.C. Lee et al. (JNER, 2012) nicely illustrates this type of approach
while taking advantage of even more recent advantages in technology: the smartphone.
Many of today's smartphones incorporate inertial sensors, processing power, memory
and other features that are needed to train balance and gait using body-fixed sensors
[5,6]. In their intriguing pilot study, Lee et al. use a smartphone and a low-cost
vibro-tactile sensor to provide feedback that is designed to enhance postural control.
The initial results with this system are quite promising. With minimal extra cost,
the smartphone technology helped controls and patients with vestibular impairment
to markedly improve their postural control. While the number of subjects is quite
small and many questions about efficacy and retention remain to be determined, the
approach nicely demonstrates the potential of the smartphone. More work is needed
for this line of research, however, these initial findings are promising and consistent
with other pilot studies [5,6].
Is there a place for more expensive, dedicated equipment for quantifying gait and
balance and as a therapeutic tool? Of course. Nonetheless, in an era when the population
is aging and funds available for rehabilitation and research are declining, an approach
that takes maximal advantage of existing, ubiquitous, low-cost technologies like the
smartphone seems like a smart way to go. At least until the next generation of technology
1. Hausdorff JM: Gait dynamics in Parkinson's disease: common and distinct behavior
among stride length, gait variability, and fractal-like scaling. Chaos 2009, 19: 026113.
2. Weiss A, Sharifi S, Plotnik M, van Vugt JP, Giladi N, Hausdorff JM: Toward automated,
at-home assessment of mobility among patients with Parkinson disease, using a body-worn
accelerometer. Neurorehabil Neural Repair 2011, 25: 810-818.
3. Chiari L: Wearable systems with minimal set-up for monitoring and training of
balance and mobility. Conf Proc IEEE Eng Med Biol Soc 2011, 2011: 5828-5832.
4. Mirelman A, Herman T, Nicolai S, Zijlstra A, Zijlstra W, Becker C, Chiari L,
Hausdorff JM: Audio-biofeedback training for posture and balance in patients with
Parkinson's disease. J Neuroeng Rehabil 2011, 8: 35.
5. Palmerini L, Mellone S, Rocchi L, Chiari L: Dimensionality reduction for the
quantitative evaluation of a smartphone-based Timed Up and Go test. Conf Proc IEEE
Eng Med Biol Soc 2011, 2011: 7179-7182.
6. Yamada M, Aoyama T, Mori S, Nishiguchi S, Okamoto K, Ito T, Muto S, Ishihara
T, Yoshitomi H, Ito H: Objective assessment of abnormal gait in patients with rheumatoid
arthritis using a smartphone. Rheumatol Int 2011.
The author is a participant in two projects funded by the European Union - Seventh
Framework Programme (FP7/2007-2013, CuPiD project #288516 and WIISEL project # 288878)
that are, among other things, developing smartphone-based approaches for quantifying
gait and detecting and treating freezing of gait.
BioMed Central Ltd unless otherwise stated. Part of Springer Science+Business Media.