Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation - PubMed (original) (raw)

Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation

Nicole A Capela et al. J Neuroeng Rehabil. 2015.

Abstract

Background: The 6-minute walk test (6MWT: the maximum distance walked in 6 minutes) is used by rehabilitation professionals as a measure of exercise capacity. Today's smartphones contain hardware that can be used for wearable sensor applications and mobile data analysis. A smartphone application can run the 6MWT and provide typically unavailable biomechanical information about how the person moves during the test.

Methods: A new algorithm for a calibration-free 6MWT smartphone application was developed that uses the test's inherent conditions and smartphone accelerometer-gyroscope data to report the total distance walked, step timing, gait symmetry, and walking changes over time. This information is not available with a standard 6MWT and could help with clinical decision-making. The 6MWT application was evaluated with 15 able-bodied participants. A BlackBerry Z10 smartphone was worn on a belt at the mid lower back. Audio from the phone instructed the person to start and stop walking. Digital video was independently recorded during the trial as a gold-standard comparator.

Results: The average difference between smartphone and gold standard foot strike timing was 0.014 ± 0.015 s. The total distance calculated by the application was within 1 m of the measured distance for all but one participant, which was more accurate than other smartphone-based studies.

Conclusions: These results demonstrated that clinically relevant 6MWT results can be achieved with typical smartphone hardware and a novel algorithm.

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Figures

Figure 1

Figure 1

Forward acceleration with circles identifying foot strikes. (a) Triaxial accelerometer at 100Hz, filtered at 20Hz, with asterisks showing foot strike (modified from Zijstra [24]); (b) Android smartphone accelerometer downsampled to 50Hz (modified from Mellone [15]); (c) Representative data sample from the current study showing similar peaks, highlighted by squares, which would produce incorrect step identification without a locking period. Raw signal is inversed to match convention used by [24] and [15] and asterisks represent foot strike.

Figure 2

Figure 2

Raw and corrected azimuth signals (turn highlighted).

Figure 3

Figure 3

Azimuth correction flowchart. The threshold was 10°.

Figure 4

Figure 4

Step detection. The dashed square represents the locking period, the circle is the detected peak and the arrows indicate the difference between the peak and min on either side. Asterisks represent foot strikes.

Figure 5

Figure 5

Flowchart for detecting missed steps.

Figure 6

Figure 6

Tangent method for identifying left and right steps.

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