Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization.

Distinguishing sideways and backward falls from normal activities of daily living using angular rate sensors (gyroscopes) was explored in this paper. Gyroscopes were secured on a shirt at the positions of sternum (S), front of the waist (FW) and right underarm (RU) to measure angular rate in lateral and sagittal planes of the body during falls and normal activities. Moreover, the motions of the fall incidents were captured by a high-speed camera at a frame rate of 250 frames per second (fps) to study the body configuration during fall. The high-speed camera and the sensor data capture system were activated simultaneously to synchronize the picture frame of high-speed camera and the sensor data. The threshold level for each sensor was set to distinguish fall activities from normal activities. Lead time of fall activities (time after threshold value is surpassed to the time when the hip hits the ground) and relative angle of body configuration (angle beta between the vertical line and the line from the center point of the foot or the center point between the two legs to that of the waist) at the threshold level were studied. For sideways falls, lead times of sensors at positions FW and S were about 200-220ms and 135-182ms, respectively. The lead time of the slippery backward fall (about 98ms) from the sensor at position RU was shorter than that of the sideways falls from the sensors at positions FW and S. The relative angle of body configuration at threshold level for sideways and backward falls were about 40-43 degrees for the sensor at position FW, about 43-52 degrees for the sensor at position S and about 54 degrees for the sensor at position RU, respectively. This is the first study that investigates fall dynamics in detection of fall before the person hits the ground using angular rate sensors (gyroscopes).

Classification of gait patterns in the time-frequency domain.

This paper describes the classification of gait patterns among descending stairs, ascending stairs and level walking activities using accelerometers arranged in antero-posterior and vertical direction on the shoulder of a garment. Gait patterns in continuous accelerometer records were classified in two steps. In the first step, direct spatial correlation of discrete dyadic wavelet coefficients was applied to separate the segments of gait patterns in the continuous accelerometer record. Compared to the reference system, averaged absolute error 0.387 s for ascending stairs and 0.404 s for descending stairs were achieved. The overall sensitivity and specificity of ascending stairs were 98.79% and 99.52%, and those of descending stairs were 97.35% and 99.62%. In the second step, powers of wavelet coefficients of 2 s time duration from separated segments of vertical and antero-posterior acceleration signals were used as features in classification. Our results proved a reliable technique of measuring gait patterns during physical activity.