Recognizing human daily activity using a single inertial sensor

As robot assisted living is becoming increasingly important for elderly people, human daily activity recognition is necessary for human-robot interaction. In this paper, we proposed an approach to daily activity recognition for elderly people. This approach uses a single wearable inertial sensor worn on the right thigh of a human subject to collect motion data. This setup can reduce the obtrusiveness to the minimum. Human daily activities can be recognized in two steps. First, two neural networks are used to classify the basic activities. Second, the activity sequence is modeled by an HMM to consider the sequential constraints exhibited in human daily life and the modified short-time Viterbi algorithm is used for realtime daily activity recognition as the fine-grained classification. We conducted experiments in a mock apartment environment and the obtained results proved the effectiveness and accuracy of our approach.