Gait Monitoring and Walk Distance Estimation With an Accelerometer During 6-Minute Walk Test

Abstract

BACKGROUND: The 6-min walk test (6MWT) encompasses potential and untapped information related to exercise capacity. However, this test does not yield any information about gait pattern. Recently, we used a ventilatory polygraph to reveal respiratory adaptation during the 6MWT with subjects having high or low body mass index (BMI). In this study, we aimed to determine gait parameters with the same device, which integrates an accelerometer. METHODS: Using a 30-m corridor, steps and U-turns were detected with a custom-made algorithm, compared to video recordings as a reference method, and analyzed offline. From the vertical acceleration signal, we were able to determine cadence and step length, and we could calculate the total distance covered in 6 min (6MWD). We then compared these variables between subjects with low BMI (n ‫؍‬ 13 subjects) or high BMI (n ‫؍‬ 29 subjects). RESULTS: Steps and U-turn detection correlated with video results (r ‫؍‬ 0.99, P < .001 for both). The 6MWD calculation was also in line with classical measurements (r ‫؍‬ 0.99, P < .001). High BMI subjects had a significantly lower 6MWD, cadence, and step length than controls (P < .001 for each). Walking speed was more closely correlated with step length (r ‫؍‬ 0.92) than with cadence (r ‫؍‬ 0.64) for both groups. CONCLUSION: Our results demonstrated that a ventilatory polygraph with an embedded accelerometer can be used to detect steps and U-turns, and to calculate 6MWD. This method is sufficiently sensitive to characterize significant BMI-dependent differences in gait pattern during a 6MWT and appears to be a promising tool for routine clinical use.

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

“…The majority used lowpass Butterworth filters, one paper used a band-pass filter and one paper explicitly processed raw data without filtering (see Table 2 for details). Data processing was performed mostly using Matlab (10 papers, 35.7%, [38,41,[44][45][46]51,58,59,62,63]). Eight papers did not report the software used (28.6%, [37,39,40,48,53,56,60,64]), 4 papers used custom software (14.3%, [47,49,50,57]).…”

“…Spatio-temporal Time between heel strike and toe-off [52] Stride time SD Spatio-temporal Stride time is defined as the time between two consecutive heel-strikes of the same foot [46,60] Stride time variability Spatio-temporal Stride time SD divided by mean stride time (%) [37,60] Swing time variability Spatio-temporal Swing time SD divided by mean swing time (%). Swing time is defined as the time interval between toe-off and the subsequent heel-strike of the same foot [37] Step length Spatio-temporal Number of steps between 2 consecutive U-turns divided by time taken [38] Stance ratio Spatio-temporal Percentage of the gait cycle during which the foot is in stance phase (%) [39] Load ratio Spatio-temporal Percentage of the stance corresponding to loading phase defined as the time between heel strike and toe strike (%) [39] Foot flat ratio Spatio-temporal Percentage of the stance corresponding to the foot-flat phase (%) [39] Push ratio Spatio-temporal Percentage of the stance corresponding to push phase defined as the time between heel off and toe off (%) [39] Symmetry of foot pitch angular velocity Spatio-temporal Pearson correlation coefficient (-) [39] Symmetry of foot pitch angular velocity Spatio-temporal Mean absolute difference between each left and right signal sample of cycle n divided by the mean range of the signals in the cycle (-) [39] Coefficient of stride cycle repetition Spatio-temporal Sum of positive autocorrelation coefficients of the three axes as a function of t (-) [39] Coefficient of step repetition Spatio-temporal…”

Transparent Quality Optimization for Machine Learning-Based Regression in Neurology

“…To date, there is no general automatic measurement approach to determine the actual distance walked after completion of the 2MWT, although numerous spatial and temporal gait parameters can be extracted. Digitization in this area through the use of IMUs is increasingly being used to determine gait disturbances [ 33 , 34 , 35 ] and supports more sensitive patient monitoring. Following this approach, we provide a proof of concept in this study for the application of state-of-the-art ML technology for comparatively little data to predict an important neurological measurement for a specific use case.…”

Automated Analysis of the Two-Minute Walk Test in Clinical Practice Using Accelerometer Data

“…The use of inertial measurement units (IMUs) is becoming increasingly popular for determining gait deficits, such as with the 6MWT [30]. In particular, the distance traveled is often calculated from IMU data [31][32][33]. Retory et al compared the distance traveled, which was classically calculated from the product of the number of steps (from video recordings) with the median step length, with IMU data from an accelerometer and a correlation of r = 0.99 [32] was shown.…”

“…In particular, the distance traveled is often calculated from IMU data [31][32][33]. Retory et al compared the distance traveled, which was classically calculated from the product of the number of steps (from video recordings) with the median step length, with IMU data from an accelerometer and a correlation of r = 0.99 [32] was shown. When calculating the distance traveled using IMUs no other measurement instruments were needed, thus resulting in less bias in the measuring method.…”