In this paper, we present a feature learning method for long-time sensor data. Although feature learning methods have been successfully used in many applications, they cannot extract features efficiently when the dimension of training data is quite large. To address this problem, we propose a method to search effective features from long-time sensor data. The important characteristic of our method is that it searches the features based on the gradient of input vectors to minimize the objective function of the learning algorithm. We apply our method to the estimation of physical capacity from wearable sensor data. The experimental results show that our method can estimate leg muscle strength more accurately than conventional methods using a feature learning method and current clinical index.