REVIEW PAPER ON REAL TIME HUMAN ACTION RECOGNITION USING SHAPE FEATURES HISTOGRAMS

Human Activity Recognition (HAR) is the understanding of human behaviour from data captured by pervasive sensors, such as cameras or wearable devices. It is a powerful tool in medical application areas, where consistent and continuous patient monitoring can be insightful. Wearable devices provide an unobtrusive platform for such monitoring, and due to their increasing market penetration, feel intrinsic to the user. This daily integration into a user’s life is crucial for increasing the understanding of overall human health and wellbeing. This is referred to as the “quantified self” movement. Wearables, such as actigraph accelerometers, generate a continuous time series of a person’s daily physical exertion and rest. This ubiquitous monitoring presents substantial amounts of data, which can (i) provide new insights by enriching the feature set in health studies, and (ii) enhance the personalization and effectiveness of health, wellness, and fitness applications. By decomposing an accelerometer’s time series into distinctive activity modes or actions, a comprehensive understanding of an individual’s daily physical activity can be inferred. The advantages of longitudinal data are however complemented by the potential of noise in data collection from an uncontrolled environment. Therefore, the data sensitivity calls for robust automated evaluation procedures.