A Hidden Markov Model for Indoor Trajectory Tracking of Elderly People

Daniel Alshamaa (1) , Aly Chkeir (1) , Farah Mourad-Chehade (1) , Paul Honeine (2)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 DocApp - LITIS - Equipe Apprentissage

Tracking of elderly people is indispensable to assist them as fast as possible. In this paper, we propose a new trajectory tracking technique to localize elderly people in real time in indoor environments. A mobility model is constructed, based on the hidden Markov models, to estimate the trajectory followed by each person. However, mobility models can not be used as standalone tracking techniques due to accumulation of error with time. For that reason, the proposed mobility model is combined with measurements from the network. Here, we use the power of the WiFi signals received from surrounding Access Points installed in the building. The combination between the mobility model and the measurements result in tracking of elderly people. Real experiments are realized to evaluate the performance of the proposed approach.

Keywords: hidden Markov models; elderly people; mobility; tracking; WiFi; RSSI

Domains: Machine Learning [stat.ML]; Signal and Image processing; Statistics [math.ST]; Signal and Image Processing; Neural and Evolutionary Computing [cs.NE]; Machine Learning [cs.LG]; Computers and Society [cs.CY]; Computer Vision and Pattern Recognition [cs.CV]; Artificial Intelligence [cs.AI]

Daniel Alshamaa, Aly Chkeir, Farah Mourad-Chehade, Paul Honeine. A Hidden Markov Model for Indoor Trajectory Tracking of Elderly People. 2019 IEEE Sensors Applications Symposium (SAS), Mar 2019, Sophia Antipolis, France. ⟨10.1109/SAS.2019.8706002⟩. ⟨hal-01995170⟩