In this paper, a tele-gait monitoring system which consists of an inertial measurement unit (IMU) and Smart Shoes is
proposed for gait monitoring and rehabilitation. In our previous works, a mobile gait monitoring system (MGMS) was proposed,
which utilized the ground reaction forces (GRFs) measured by Smart Shoes. Although the GRF patterns measured
by the MGMS provide useful information for the diagnoses of a patient's walking motions, Smart Shoes do not measure
the position of the feet, which is necessary for a complete walking motion diagnosis. In the proposed tele-gait monitoring
system, an IMU is used in addition to Smart Shoes for a complete observation of walking motions. By analyzing the signals
measured by the IMU and Smart Shoes, it is possible to thoroughly diagnose the patient's walking motion, including:
the trajectory of the foot, the walking distance, and the length of each stride. Furthermore, the proposed gait monitoring
system makes use of the Internet such that physical therapists can monitor their patients' status anywhere anytime.
Conventionally, rehabilitation treatments for gait disorders are performed by physical therapists in a clinical
setting. Although an array of equipment, such as motion capture devices and multi-directional force plates, has
been devised to provide the physical therapists with more objective diagnostic data, restriction of the time and
space limits the effective use of such devices. To overcome this limitation various wearable sensors for patients to
directly monitor their health conditions anywhere at anytime have been studied in recent years. In this paper,
a mobile gait monitoring system (MGMS) is introduced, which integrates Smart Shoes and the monitoring
algorithms in a mobile microprocessor with a touch screen display. The mobility of the MGMS allows patients
to take advantage of the gait monitoring device in their daily lives. The monitoring algorithms embedded in
the MGMS observe various physical quantities useful for objective gait diagnoses, such as the ground contact
forces (GCFs) and the center of ground contact forces (CoGCF). Also it calculates the gait abnormality which
shows how far the GCFs are from the normal GCF patterns. By the visual feedback information displayed on
the MGMS, the patients can self correct their walking patterns. The preliminary results of clinical verification
are also given.
Health monitoring systems require a means for detecting and quantifying abnormalities from measured signals. In this
paper, a new method for detecting abnormalities in a human gait is proposed for an improved gait monitoring system for
patients with walking problems. In the previous work, we introduced a fuzzy logic algorithm for detecting phases in a
human gait based on four foot pressure sensors for each of the right and left foot. The fuzzy logic algorithm detects the
gait phases smoothly and continuously, and retains all information obtained from sensors. In this paper, a higher level
algorithm for detecting abnormalities in the gait phases obtained from the fuzzy logic is discussed. In the proposed
algorithm, two major abnormalities are detected 1) when the sensors measure improper foot pressure patterns, and 2)
when the human does not follow a natural sequence of gait phases. For mathematical realization of the algorithm, the
gait phases are dealt with by a vector analysis method. The proposed detection algorithm is verified by experiments on
abnormal gaits as well as normal gaits. The experiment makes use of the Smart Shoes that embeds four bladders filled
with air, the pressure changes in which are detected by pressure transducers.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.