A robust method of fall and motionless detection is presented. The approach is able to detect falls and motionless periods (standing, sitting, and lying) using only one belt-worn kinematic sensor. The fall detection algorithm analyses the phase changes of vertical acceleration in relation to gravity and impact force using kinematic variables. A phase angle value was used as a threshold to distinguish between falls and normal motion activity. There are two advantages with this approach in comparison with existing approaches: (1) it is computationally efficient and theoretic (2) it is based on a single threshold value which was determined from a kinematic analysis for the falling processes. To evaluate the system, ten subjects were studied each of which performed different types of falls and motionless activities during a period of monitoring activity. These included: normal walking, standing, sitting, lying, a front bend of 90 degrees, tilt over 70 degrees and four kinds of falls (forward, backward, tilt left and right). The results show that 100% of heavy falling, 97% of all falls and 100% of motionless activity were correctly detected in a laboratory environment and the beginning and ends of these events were determined.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||Published - 4 Aug 2009|
|Event||Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference - London|
Duration: 4 Aug 2009 → …
|Conference||Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference|
|Period||4/08/09 → …|