The Prediction and Prevention of Disease by Using Big Data in Motion Analysis

Variable patterns of gait disturbance can be found in patients with spine disease including the problems of gait initiation, freezing of gait, reduced balance and postural control, reduced step lengths, increased step times, and slow walking speed.

Gait analysis of patients with ankylosing spondylitis, patients with adoldescent idiopathic scoliosis and control group.

Gait analysis was performed on 20-m long corridor to collect gait data on more than 23 strides. The gait protocol was performed with an IMU sensor-based gait analysis system (DynaStab™, JEIOS, South Korea) consisting of a shoe-type data logger (Smart Balance1 SB-1, JEIOS, South Korea) and a data acquisition system (DynaStab-Spotfire1, Tibco Spotfire 7.10). The shoe-type data logger included an IMU sensor (IMU-3000™, InvenSense, USA) that measured tri-axial acceleration (up to ± 6 g) and tri-axial angular 136 velocity (up to ± 500˚ s-1) along three orthogonal axes.12,16 The IMU sensors were installed in both shoe outsoles, and the data were transmitted wirelessly to a data acquisition system via Bluetooth®. Shoe sizes were adapted to each participant, with available sizes ranging from 225 mm to 280 mm. The local coordinate system for the IMU sensors included the 140 anteroposterior, mediolateral, and vertical directions.

comparing the swing times performed by one leg with respect to the swing times performed by the other, according to the following formula: GA = 100 x ln(SSWT/LSWT)

Inclusion Criteria: Pusan National University Hospital orthopedics, otorhinolaryngology, neurology outpatient or inpatient who agreed to the study. For adult men and women 18 years of age or older. Exclusion Criteria: Patients unable to walk independently.