A “one-size-fits-most” walking recognition method for smartphones, smartwatches, and wearable accelerometers

Abstract

The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using “activity counts,” a measure which overlooks specific types of physical activities. We propose a walking recognition method for sub-second tri-axial accelerometer data, in which activity classification is based on the inherent features of walking: intensity, periodicity, and duration. We validate our method against 20 publicly available, annotated datasets on walking activity data collected at various body locations (thigh, waist, chest, arm, wrist). We demonstrate that our method can estimate walking periods with high sensitivity and specificity: average sensitivity ranged between 0.92 and 0.97 across various body locations, and average specificity for common daily activities was typically above 0.95. We also assess the method’s algorithmic fairness to demographic and anthropometric variables and measurement contexts (body location, environment). Finally, we release our method as open-source software in Python and MATLAB.

Validation of an open-source smartphone step counting algorithm in clinical and non-clinical settings

“…In this paper, we validated the performance of step counting using a previously published open-source walking recognition method for body-worn devices that contain an accelerometer (29). This method leverages the observation that regardless of sensor location on the body, during walking activity the predominant component of the accelerometer signal transformed to the frequency domain, i.e., step frequency, remains the same, enabling the calculation of the number of steps a person performed in a given time fragment.…”

“…Our method leveraged the observation that regardless of the sensor location, orientation, or subject, during walking activity device’s accelerometer signal oscillates around a local mean with a frequency equal to the performed steps (29). To extract this information, we used the continuous wavelet transform to project the original signal onto the time-frequency space of wavelet coefficients which are maximized when a particular frequency matches the frequency of the observed signal at a given time point ( Figure 1 ).…”

“…The available accelerometer data were processed using the walking recognition and step counting algorithm with default tuning parameters, as previously described (29). Depending on the validation approach, the resulting one-second step counts were then aggregated into step counts for the entire walking bout or specified time fragment.…”

“…Although smartphones can overcome many limitations of wearable activity trackers—e.g., through access to raw sensor data (25) and increased adoption among older individuals (26)— the quantification of gait-related biomarkers remains challenging. This is largely because of the variation in the location and orientation of smartphones in relation to the body in real-life conditions, which affects the data collected from smartphones’ inertial sensors (27–29).…”

“…To address this problem, we have recently proposed an open-source walking recognition method (29), which can be applied to accelerometer data collected from various locations on the body, making it suitable for smartphones. In this paper, we demonstrate how our method can be used for quantifying steps, and we validate its performance in eight independent datasets.…”