Development of a real-time activity classification and stability assessment system for activities of daily living
|關鍵字:||日常生活動作;動作分類;動作穩定性評估;三軸加速規;即時性系統;Activities of daily living;Activity classification;Activity stability assessment;Triaxial accelerometer;Real-time system|
Activities of daily living (ADLs) are major components of our life. However, instability situations, such as loss of balance or falling, might jeopardize the completion of these fundamental activities and ultimately decrease the quality of life. Therefore, providing a real-time stability assessment methods, which is suitable for multiple activities in real-life situations, is extremely crucial. Previous research studies focused on assessing stability during individual activity, while none of them successfully provided a comprehensive manner to assess multiple activities in real-life situation. In this study, integrating real-time activity classification algorithm with multiple activity stability assessment methods was proposed. Since the movement control ability varies with each individual, relative stability, comparing current activity with personal normal activity, was also proposed to quantify activity stability. In the first-stage experiment, activity classification algorithm construction, thirty subjects performed nine ADLs with four tri-axial accelerometers attached to spine (C7), sacrum, dominant wrist and dominant thigh. The acceleration data were divided into consecutive sliding window for further analysis. Activity features were extracted and the process of setting individualized thresholds was established. Finally, multiple activity stability assessment methods were integrated into the developed algorithm for further evaluation. In the second-stage experiment, system evaluation, five subjects performed two series of continuous activities with two tri-axial accelerometers attached on sacrum and front side of dominant thigh. First series was performed without any disturbance, whereas second series was performed with disturbances, such as slippery surface and obstacle crossing. Activity classification algorithm provided 94% accuracy on classifying normal activities in first series. The validity of integrating activity classification algorithm with multiple assessment methods was also been authenticated by comparing the results from first series with and without classification algorithm. Finally, the relative stability results during second series also met the results of previous studies. In this study, we successfully developed a real-time activity classification and stability assessment system, which can be applied under the conditions of daily living and multiple activities.