| Abstract [eng] |
Companies are interested in retaining workers healthy, productive, and satisfied while cutting health-care and insurance costs. Using a computer at work can cause back, neck and shoulder pain, eyestrain, and overuse injuries of human hands and wrists. It is possible to reduce these risks with better posture and good habits, such as taking rest breaks. During these breaks computer users should be encouraged to stand, stretch, and move around. For people who forget about a break or truly are focused on their direct work need help from special equipment for evaluation of real physical activity of computer user. Method for recording accelerometer data from moving human as he or she performs daily activities and for identification of type, duration and intensity of movements by using wearable wireless sensing system is presented in this paper. The extraction of orientation independent acceleration data has positive effect on recognition accuracy of k-nearest neighbour classification scheme used for classification task. The recognition accuracy of algorithm is 78.9% and these results are better than accuracy obtained from raw accelerometer data. The method presented is simple, exhibited good performance and does not require significant computational recourses. |
