Current Issue

Over the past centuries, gait analysis has evolved by integrating measurements, descriptions, and evaluations to analyze human locomotion characteristics and provide information on gait kinematic and kinetic for researchers and clinicians (Ghoussayni et al, 2004). Gait analysis provides a baseline for planning, evaluation, and management for patient treatment in the medical field, also is used in elderly healthy to provide a valuable information (Dobkin et al, 2004; Yavuzer et al, 2008).

Gait analysis systems have been developed with its various needs, among which, due to the accuracy of the equipment, motion analysis systems have become the gold standard today but, these motion analysis systems required expensive high-speed cameras, specialized internal motion analysis laboratories, and post-processing periods to obtain accurate data, therefore, it was necessary to develop alternative gait analysis equipment using wearable sensors such as the accelerometer, gyroscope, magneto-resistive sensors, flexible goniometer, electromagnetic tracking system (ETS), sensing fabric, force sensor, and sensors for electromyography to overcome the limitations of the laboratory environment and high price (Tao et al, 2012; Wagner and Ganz, 2012; Park et al., 2019).

In recent years, various gait analysis devices based on wearable sensors have been proposed, and meaningful progress has been made. The approach reported by the researchers includes a 3-axis acceleration and gyro sensor based on the Quaternion calculation method (Tadano et al, 2013), the body inertial sensing network (Guo et al, 2012), an accelerometer and magnetometer (Kun et al, 2011), an anatomical coordinate system using an intra-shoes pressure measurement system (IPM) (Kim and Nussbaum, 2014) and foot switches and micro-controller, connected to an Android Smart-phone using Bluetooth (Wagner and Ganz, 2012).

There are many commercial gait analysis systems based on wearable sensors, but similar devices are still being developed, which is proof of the lack of satisfaction with existing products. Moreover, for individuals in special circumstances, such as patients in the recovery phase of musculoskeletal or nervous system diseases, being able to simultaneously collect the gait analysis results in a free examination environment may provide useful information for measuring and evaluating gait to the treatment provider. In particular, for patients requiring intervention, the collection of spatiotemporal gait parameters without post-processing time is useful to immediately reflect the intervention after measurement (Lopez-Meyer et al, 2011).

Therefore, we proposed a new portable gait analysis system (PGAS) with simple post-processing time, user-friendly, and no experimental site restrictions. The primary goal of this study was to compare the tempo-spatial gait parameters of PGAS developed based on the wearable sensor and the motion capture system to confirm its reliability.

For general characteristics of subjects, gender, age, height, weight, and leg length were checked <Table 1>.

The ICCs for each test within the PGAS were found to have good test-retest reliability in gait parameters (0.840 to 0.992) and joint ranges in the stance phase (0.907 to 0.988) and swing phase (0.909 to 0.982) <Table 2 and 3>.

The ICCs for each test between systems were found to have acceptable intra-tester reliability in gait parameters (0.809 to 0.961) and joint angles in the stance phase (0.800 to 0.977) and swing phase (0.817 to 0.942) <Table 4 and 5>.

We have demonstrated that the reliability for measured tempo-spatial gait parameters such as velocity, cadence, step length, stride length, and angle in joint (ankle, hip, and knee) of healthy subjects, using a newly developed PGAS. As a result, the ICC for each test within the developed PGAS showed strong agreement on measurement repeatability and acceptable reliability compared to the MCS.

In a similar approach, Tadano et al (2013) suggested the implementation of wearable tri-axial acceleration and gyro sensors attached to a segment of the lower limbs, and estimated the angular velocity data during gait in five healthy individuals. The joint trajectory was in the horizontal and sagittal planes, and a comparison of this system was performed with a camera-based motion analysis system, with average root mean square error (RMSE) and correlation coefficient (CC) of 10.14˚ and 0.98 for the hip flexion, 7.88˚ and 0.97 for the knee flexion, and 9.75˚ and 0.78 for the ankle flexion angles. As a result, the RMSE represented a variation; however, with the high CC observed, researchers proposed that this was caused by inaccuracies in calculating the measurements from the camera images.

In this study, the sensor units were constructed with the tri-axial acceleration and gyro sensors as in the previous study; however, it could measure each joint angle and gait parameter such as velocity, cadence, step length, and stride length simultaneously, and showed good ICCs compared with the MCS.

The body inertial-sensing network, which consisted of one three-axis accelerometer, one three-axis magnetometer, and one three-axis gyroscope, was tested by Guo et al (2012) and seven hemiplegia patients and seven healthy subjects were participated but only calculated the angle of knee flexion and extension in one gait cycle and then the researchers were considering other parameters such as the step length or hip joint angle in their future works. One of the advantages of PGAS developed compared to previous studies is that it can detect the sequence of gait cycles from the beginning to the end, and it is more appropriate for the analysis of the entire gait cycle to understand the variability during gait.

Lopez-Meyer et al (2011) used a wearable footwear-based sensor system like a Force-Sensitive Resister (FSR) and accelerometer to describe the temporal gait parameters of seven individuals with stroke and sixteen healthy subjects. This system automatically detected heel-strike and toe-off events, obtained temporal gait parameters as compared to the GAITRite system, and showed no significant difference in the cadence (p>0.35) and temporal gait parameters (p>0.18) in healthy subjects compared with the cadence (p>0.29) and temporal gait parameters (p>0.51) in subjects with stroke. As discussed, this method using FRS sensors only estimated cadence and temporal gait parameters. The present study focused on the accurate estimation of spatial and temporal parameters like velocity, walked distance, and joint angles of a subject by using data calculated from the accelerometer and gyro sensors. However, automatic detection of gait event is one of the strengths of wearable footwear-based sensor system; therefore, we also consider supplementation of FRS sensors in our future study.

Although decisive and excellent reliability were found in both test-retest reliability of a PGAS and intra-tester reliability of the two systems, the ICC for the ankle joint was lower than that for other joints in healthy subjects during gait. This indicated that there was a possibility of errors in the MCS or the PGAS. According to many researchers, even though Vicon motion analysis systems are referred to as the “Gold standard” for 3-D motion tracking system, they also have been reported an accuracy errors (Dorociak and Cuddeford, 1995; Windolf et al, 2008). The gait analysis system using a wearable sensor has also limitations such as attachment problems with strap and external signal noise (Tadano et al, 2013).

Regarding future studies on system implementation, several items could be considered for improvement. First, even though errors are very small, considering both a roll and a pitch can result in more precise stride or step lengths since a pitch can never be zero when patients walk. Second, since it seems that joint angles, especially angles of ankle, change faster than the 100 ms sampling speed, the sampling rate should be increased to, for example, 1 ms or 10 ms by adjusting the spaces of the Bluetooth devices due to the interference among them. Third, mechanical noises have been noticed owing to the body vibration when subjects walk. Therefore, since the phenomenon happens more with bigger or taller devices, smaller IMU devices with small batteries is preferred. Fourth, since the system provides only joint angles and step lengths, researchers use Excel to calculate stride lengths, cadences, velocities, and stance or swing durations. Instead, analytical tools could be embedded in the system, so that researchers can obtain the desired data easily. During experiments, researchers have to recharge the IMU devices from time to time, which means a low power design or high capacity batteries would be beneficial for researchers.

The results of this study indicated that the developed PGAS showed acceptable agreement compared with the MCS, high test-retest reliability within the PGAS for dynamic gait analysis, and good grade of ICCs compared to the results from the MCS statistically. The gait kinematic parameters in healthy subjects can be used as standard values for using this PGAS.