Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic physical activity recommendation on personalised mobile health
  information service: A deep reinforcement learning approach   Mobile health (mHealth) information service makes healthcare management
easier for users, who want to increase physical activity and improve health.
However, the differences in activity preference among the individual, adherence
problems, and uncertainty of future health outcomes may reduce the effect of
the mHealth information service. The current health service system usually
provides recommendations based on fixed exercise plans that do not satisfy the
user specific needs. This paper seeks an efficient way to make physical
activity recommendation decisions on physical activity promotion in
personalised mHealth information service by establishing data-driven model. In
this study, we propose a real-time interaction model to select the optimal
exercise plan for the individual considering the time-varying characteristics
in maximising the long-term health utility of the user. We construct a
framework for mHealth information service system comprising a personalised AI
module, which is based on the scientific knowledge about physical activity to
evaluate the individual exercise performance, which may increase the awareness
of the mHealth artificial intelligence system. The proposed deep reinforcement
learning (DRL) methodology combining two classes of approaches to improve the
learning capability for the mHealth information service system. A deep learning
method is introduced to construct the hybrid neural network combing long-short
term memory (LSTM) network and deep neural network (DNN) techniques to infer
the individual exercise behavior from the time series data. A reinforcement
learning method is applied based on the asynchronous advantage actor-critic
algorithm to find the optimal policy through exploration and exploitation.

34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess