How busy is my supervisor?: Detecting the visits in the office of my supervisor using a sensor network

Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In real life there will be situations where the inhabitant receives visits from family members or professional health care givers. In such cases activity recognition is unreliable. In this paper, we investigate the problem of detecting multiple persons in an environment equipped with a sensor network consisting of binary sensors. We conduct a real-life experiment for detection of visits in the office of the supervisor where the office is equipped with a video camera to record the ground truth. We collected data during two months and used two models, a Naive Bayes Classifier and a Hidden Markov Model for a visitor detection. An evaluation of these two models shows that we achieve an accuracy of 83% with the NBC and an accuracy of 92% with a HMM, respectively.