Accelerometer based human activities and posture recognition

The quantity of elderly people like to live in their homes, secluded, in their brilliant age is expanding exponentially. This is not a perfect path for an elderly individual to live. However, the urbanization and resultant change of the social and social conduct makes it a more regular event. Falls are a noteworthy reason for death and horribleness in more established grown-ups. In this way, it has turn into an opportune need to create mechanized look after the elderly. The first end, purpose of such fall computer looking-glass is to ready caregivers of the fall event, which can then start an earlier process. In the present study, we amplify the use of wearable inertial sensors for fall identification and information of human posture and activities, by creating and assessing the precision of a sensor framework for identifying the same. We found that our system could discover fall events and monitor human activities with at least 95% accuracy.