Abstract
In this paper, a low-cost motion analysis system using a wireless ultrasonic sensor network is proposed and investigated. A methodology has been developed to extract spatial-temporal gait parameters including stride length, stride duration, stride velocity, stride cadence, and stride symmetry from 3D foot displacements estimated by the combination of spherical positioning technique and unscented Kalman filter. The performance of this system is validated against a camera-based system in the laboratory with 10 healthy volunteers. Numerical results show the feasibility of the proposed system with average error of 2.7% for all the estimated gait parameters. The influence of walking speed on the measurement accuracy of proposed system is also evaluated. Statistical analysis demonstrates its capability of being used as a gait assessment tool for some medical applications.
Highlights
The significance of spatial-temporal gait parameters measurement has been addressed in many research papers [1,2,3]
The mean and standard deviation in stride length, stride duration, and stride velocity estimation between the proposed system and the reference system together with Root Mean Square Error (RMSE) value are reported in Tables 2–4 for all subjects walking at normal speed
The mean and standard deviation in the estimation of the stride velocity is reported in Table 4, which shows that the proposed method slightly overestimated the stride velocity by 0.001 m/s with an RMSE value of 0.036 m/s, occupying 3.6% of the proposed estimates of stride velocity
Summary
The significance of spatial-temporal gait parameters measurement has been addressed in many research papers [1,2,3]. The quantitative analysis of such gait parameters can be helpful to diagnose impairments in balance control [4], monitor the progress in rehabilitation [5], and predict the risk of falling [6,7]. Such parameters include stride length, walking velocity, stride cadence, stride duration. Having instruments that are capable of measuring these gait parameters about the patients’ walking ability is useful in many clinical applications [10].
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