Classification of human fall from activities of daily life using joint measurements

Falls are a major health concern to most of communities with aging population. There are different approaches used in developing fall detection system such as some sort of wearable, non-wearable ambient sensor and vision based systems. This paper proposes a fall detection system using Kinect for Windows to generate depth stream which is used to classify human fall from other activities of daily life. From the experimental results our system was able to achieve an average accuracy of 94.43% with a sensitivity of 94.44% and specificity of 68.18%. The results also showed that brutally sitting on floor has a higher acceleration, which is very close to the acceleration shown by fall. Even then the system was able to achieve a high accuracy in determining brutal movements with the use of joint positions, this is an indication that further improvements to the algorithm can make the system more robust