Improving classification of sit, stand, and lie in a smartphone human activity recognition system

Human Activity Recognition (HAR) allows healthcare specialists to obtain clinically useful information about a person´s mobility. When characterizing immobile states with a smartphone, HAR typically relies on phone orientation to differentiate between sit, stand, and lie. While phone orientation is effective for identifying when a person is lying down, sitting and standing can be misclassified since pelvis orientation can be similar. Therefore, training a classifier from this data is difficult. In this paper, a hierarchical classifier that includes the transition phases into and out of a sitting state is proposed to improve sit-stand classification. For evaluation, young (age 26 ± 8.9 yrs) and senior (age 73 ± 5.9yrs) participants wore a Blackberry Z10 smartphone on their right front waist and performed a continuous series of 16 activities of daily living. Z10 accelerometer and gyroscope data were processed with a custom HAR classifier that used previous state awareness and transition identification to classify immobile states. Immobile state classification results were compared with (WT) and without (WOT) transition identification and previous state awareness. The WT classifier had significantly greater sit sensitivity and F-score (p<;0.05) than WOT. Stand specificity and F-score for WT were significantly greater than WOT for seniors. WT sit sensitivity was greater than WOT for the young population, though not significantly. All outcomes improved for the young population. These results indicated that examining the transition period before an immobile state can improve immobile state recognition. Sit-stand classification on a continuous daily activity data set was comparable to the current literature and was achieved without the use of computationally intensive feature spaces or classifiers.