same accuracy for classification approach. These selected 
features have been used as inputs for the classification and 
recognition methods. We evaluated and compared several 
classifiers provided by WEKA, namely Baysian Network, the 
Support Vector Machine (LibSVM), k-Nearest Neighbor 
(kNN), and Artificial Neural Network (ANN) (Saeedi et al., 
2011). The SVM has the best performance in this case and have 
been shown in figure 6. 
Uncertainty is an integral part of the extracted context 
information and it is mostly caused by the imperfectness and 
incompleteness of sensed data and classification models. For 
example, it is difficult to detect if the phone is in the pocket or 
on the belt based on low-level sensing information such as 
accelerometer signals. Therefore, in our work we used Fuzzy 
Inference Engine (FIS) to transform the data into higher-level 
descriptions of context information. The hybrid method is 
capable of handling the uncertainty of the activities recognized 
using signal processing, removing the conflicts and preserving 
consistency of detected contexts, and filling the gaps. The list of 
linguistic variables and their corresponding membership 
functions is mentioned in table 3). 
Table 3. Definition of fuzzy input variables 
  
  
  
  
  
  
  
  
  
  
  
  
: Proper >.7 
Walking patiem Medium >4 &<.7 
correlation 
Poor <.4 
3t. high < 7 
Connectivity Medium 7.4 and <.7 
between activities 
low > 4 
Good 1-4 
Moderate 5-10 
GPSDOP fair 10-20 
Poor >20 
GPS velocity Driving >10 (m) 
Pedestrian <10 (m) 
  
  
  
  
  
In the next step fuzzy rules between the input and the output 
membership functions has been defined. These rules are 
determined using an experienced human. Based on the defined 
membership functions and the rules, fuzzy reasoning for the 
conjugate point determination is carried out in a Mamdani type 
(Zadeh, 1965) fuzzy reasoning structure. In the following four 
sample rules for detecting context information are presented: 
If walking correlation of dangling is proper and connectivity of 
dangling is high then context is dangling 
If GPS velocity is driving and GPS-DOP is good or moderate 
then environment is outdoor 
In designing rule repository, the designer can define specific 
constraints to incorporate common-sense knowledge. This will 
reduce the amount of required training data and makes the rule 
mining computationally efficient An example of such a 
constraint is that a person cannot drive while in an indoor 
environment. Therefore our rule repository is composed of a 
number of predicates generated by the user and designer along 
with the mined association rules. These rules are stored in a 
knowledge-base (KB) that facilitates the modification, updating 
or removing the rules. In the rule based engine, different types 
of rules have different levels of confidence and reliability. 
4. NAVIGATION SENSOR INTEGRATION 
The core of the vision-aided pedestrian navigation system 
consists of GPS location and velocity information for retrieving 
absolute positioning while the position aid (velocity and 
heading change rate) information is provided from frame to 
frame camera images. These measurements are integrated using 
a KF filter (Aggarwal et al., 2010) that is presented briefly in 
the following section. The design of the integrated pedestrian 
navigation algorithm is shown in figure 5. The contexts that are 
useful for vision-aided system include: device orientation (e.g. 
face-up/down, vertical or portrait modes), device location 
(texting mode) and activity of the user (e.g. walking mode). 
Also, the context information about sensor's availability and 
accuracy can be used to select the device dynamic and 
observation model in the KF. 
  
Context Recognition Module 
User Activity | Device Orientation 
    
  
  
  
  
  
  
  
  
Computer Y, V 
„Vision 
GPS (9,A),V 
  
  
  
  
  
  
  
  
  
  
T» ue 
uonel3oju] 
(ve) = 
uonisod (qz 
uernsopod 
  
  
  
  
Figure 5. The multi-sensor pedestrian navigation diagram using 
context-aware vision-aided observation 
In this paper the dynamic system is based on whether the user is 
in texting mode while walking in an outdoor environment. In 
order to model the characteristics of the two-dimensional 
motion of a walking user we have used Dead Reckoning (DR) 
algorithms. DR is the determination of a new position from the 
knowledge of a previously known position, using the current 
distance and heading information. In a 2D-navigation, the 
current coordinates (E;, N,) with respect to a previously known 
position (E+-1, Nç-1) can be computed as follows: 
E, — Ec, t Spi sin Uca (1) 
N, — Nea + Sie-1,t) COS Ye-1 (2) 
where Sy, denote the distance travelled by the user since 
time t — 1 and y. , is the user's heading since time t — 1. 
4.1 Kalman Filter 
The absolute position observations from GPS and heading 
measurements obtained from camera have been integrated using 
a KF. This filter uses the dynamic model to make a prediction 
of the state in the next time step. Then, it uses an observation 
model to compare the predicted and observed states. The 
dynamic equation of a KF is (Aggarwal et al., 2010): 
Xk = Pr-1Xk-1 + Wk-1 (3) 
where, x, is the state vector, dy , represents the transition 
matrix that relates the state of a previous time to the current 
time, and wy is the process noise which is assumed to be drawn 
from a Zero mean multivariate normal 
distribution with covariance Qy ( wy—N(0, Q&) ). In this case, 
the dynamic equations for vision aided GPS is: 
Ex+1 = Ex + Ve : sin Vi : At + wy (4) 
Nis1 = Nk t V: cos yy: At t w? (5) 
Vr+r — Uk t Ws (6) 
Yr+1ı — Uk: Atk t Vy + Wa (7) 
Vier = Vk + ws (8) 
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