This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment.
Conditions causing gait abnormalities are very common and their treatment requires the detailed assessment of gait. Currently such assessments are carried out in gait laboratories and require the use of complex and expensive equipment. To increase availability and use at home and clinics, we design and develop an affordable, user friendly, wireless, portable automatic system to extract spatiotemporal features of gait that can be used indoors and outdoors. This study determines the concurrent validity of extracted gait features from Inertial Measurement Units (IMUs) against 'gold standard' Motion Capture System (MoCap) using a hybrid gait features extraction method. The analysis of the proposed method is based on minimum prominence and abrupt transition points in the IMU signals. It also compares the degree of agreement for mean spatiotemporal gait features. The concurrent data from synchronized IMUs and MoCap are collected from 18 subjects. We validate our proposed system using two experiments; 1) IMU and MoCap with self-selected (free) walking and 2) IMU and MoCap at various walking speeds. Interclass correlations, Lin's concordance correlation coefficients and Pearson's correlation coefficients (r) are applied to determine the correlation between extracted gait features from IMU and MoCap measurements. Bland-Altman plots are also generated to evaluate any unknown bias between the mean extracted features. The experiments show that spatiotemporal features of gait extracted from IMUs are highly valid. Our methods facilitate gait assessment in clinics and at home including the possibility of self-assessment.
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