Wearable sensors for recognizing individuals undertaking daily activities

With the fast evolution in the area of processing units and sensors, wearable devices are becoming more popular among people of all ages. Recently, there has been renewed interest in exploiting the capabilities of wearable sensors for person recognition while undertaking their normal daily activities. In this paper, we focus on utilizing motion information of known daily activities gathered from wearable sensors for recognizing the person. The analysis of the results demonstrates that different fundamental classification factors have an impact on person recognition success-rates. Furthermore, the results of comparison among subjects prove that some subjects have high classification results and can be easily identifiable compared to other subjects which have high confusability rates. Lastly, a significant improvement in subject classification success rate was found for activities with little or no movement which can successfully distinguish among persons and hence producing higher classification results compared to activities with large movement.