Differentiating two daily activities through analysis of short ambulatory video clips

Automatically detecting daily activities using wearable smartphones would provide valuable information to clinicians. While accelerometer data is effective in this area, classifying stair ascent can be difficult. In this paper, video content analysis is performed on short videos captured from a wearable smartphone in order to distinguish between level ground walking and stair climbing. High contrast image features, such as corners, were tracked across consecutive video frames to create feature paths. Computing the median of the slope of the paths in each frame revealed substantial differences, in both magnitude and variation over time, for stair climbing as opposed to walking. A time series of median slope values was produced for each video clip, and the number of local maxima and minima above a threshold of 1.0 were computed. Results revealed that the number of peaks during stair climbing were substantially larger than walking and, therefore, could be used as a feature for distinguishing between these two activities.