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 oce of the supervisor where the oce is equipped with a video camera to record the ground truth. We collected data during two months and used two models, a Naive Bayes Classier 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.
|Title of host publication||PETRA '12|
|Subtitle of host publication||Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments |
|Place of Publication||Heraklion|
|Publisher||Association for Computing Machinery|
|Publication status||Published - 2012|
|Name|| ACM International Conference Proceeding Series|