Using a Smartphone to Detect the Standing-to-Kneeling and Kneeling-to-Standing Postural Transitions
Sammanfattning: In this report we investigate how well a smartphone can be used to detect the standing-to-kneeling and kneeling-to-standing postural transitions. Possible applications include measuring time spent kneeling in certain groups of workers prone to knee-straining work. Accelerometer and gyroscope data was recorded from a group of 10 volunteers while performing a set of postural transitions according to an experimental script. The set of postural transitions included the standing-to-kneeling and kneeling-to-standing transitions, in addition to a selection of transitions common in knee-straining occupations. Using recorded video, the recorded data was labeled and segmented into a data set consisting of 3-second sensor data segments in 9 different classes. The classification performance of a number of different LSTM-networks were evaluated on the data set. When evaluated in a user-specific setting, the best network achieved an overall classification accuracy of 89.4 %. The network achieved precision 0.982 and recall 0.917 for the standing-to-kneeling transitions, and precision 0.900 and recall 0.900 for the kneeling-to-standing transitions. When the same network was evaluated in a user-independent setting it achieved an overall accuracy of 66.3 %, with precision 0.720 and recall 0.746 for the standing-to-kneeling transitions, and precision 0.707 and recall 0.604 for the kneeling-to-standing transitions. The network was also evaluated in a setting where only accelerometer data was used. The achieved performance was similar to that achieved when using data from both the accelerometer and gyroscope. The classification speed of the network was evaluated on a smartphone. On a Samsung Galaxy S7 the average time needed to perform one classification was 38.5 milliseconds. The classification can therefore be done in real time.
HÄR KAN DU HÄMTA UPPSATSEN I FULLTEXT. (följ länken till nästa sida)