Activity recognition using multiple body-worn sensors can directly monitor the movement of each body part and can recognize various activities accurately. However, using multiple sensors increases the chance of sensor failure or communication failure, and most current activity recognition algorithms do not work when failure occurs due to the difference (reduction) of the dimension of the feature vector from that of complete sensor data expected in system design time. Therefore, we compared three possible techniques to solves this problem on the feature value level: a classifier trained with reduced feature values, feature value compensation with multiple regression, and feature value compensation with kernel regression, in a no failure situation. All of these techniques do not depend on classification algorithms. While creating a regression model, which is in the training phase, requires relatively high computational power, compensation itself can work with low computational power. As overall results, kernel regression had the best performance that was the closest to the no failure situation. Also, the results imply that each sensor position has its own effective method and more accurate coping can be viable with the appropriate choice of the method.