I am working on a prototype framework.
Basically I need to generate a model or profile for each individual's lifestyle based on some sensor data about him/her, such as GPS, motions, heart rate, surrounding environment readings, temperature etc.
The proposed model or profile is a knowledge representation of an individual's lifestyle pattern. Maybe a graph with probabilities.
I am thinking to use Hidden Markov Model to implement this. As the states in HMM can be Working, Sleeping, Leisure, Sport and etc. Observations can be a set of various sensor data.
My understanding of HMM is that next state S(t) is only depends on previous one state S(t-1). However in reality, a person's activity might depends on previous n states. Is it still a good idea to use HMM? Or should I use some other more appropriate models? I have seen some work on second order and multiple order of Markov Chains, does it also apply HMM?
I really appreciate if you can give me a detailed explanation.