Pedestrian dead reckoning (PDR) is easy to introduce because it requires no equipment for the environment. PDR results can provide an atomic physical behavior such as step detection and turning in walking, however providing a lexible response to a user's daily actions other than walking like sitting, moving in a line or standing is tough. Our research objective is to make PDR more usable in spite of these daily actions by estimating the movement situation using the same sensor information as conventional PDR. We applied a state transition in the movement situation recognition using the transition restrictions existing between the moving situations. A new method for moving context recognition using machine learning with accelerometer and gyroscope feature vectors simultaneous with PDR is proposed herein. The experiments show that, the movement situation recognition and the state transition technique are eicient for the whole dead reckoning performance. The proposed method can also be applied to improve the PDR positioning accuracy.
Positioning techniques have been actively studied in the recent years with the
spread of services using location information. Indoor location information is
useful because it can be used for working management of factory workers and
navigation in a complex urban indoor environment, among others. Pedestrian dead
reckoning (PDR) is a typical positioning method that is functional in both indoor
and outdoor environments and achieved by continuously estimating the
walking conditions, distance and direction using the accelerometers, gyroscope and
magnetometer of the user’s smartphone, and assimilating these measurements to
estimate the current position. Moreover it does not depend on the environment
thereafter and does not require extra equipment.However, the problem is that
positioning errors are accumulated. Since PDR is a relative positioning method,
no absolute location calibration arrives. Therefore, our research focuses on the
user’s most of all kinds of movement not only walking steps that may cause
any positioning error. The current PDR, only focuses on walking recognition the
other body action states of the user are hardly considered. An erroneous step
detection when sitting in a chair at the same place, which is a daily action often
seen indoors is conirmed because of the changing leg or body posture and one’s
orientation even though he/she does not move at all. For example, we frequently
perform a changing movement in posture and body orientation, stay at the same
place with some steps , or sit for a long time as well as perform irregular
walking in which stopping is frequently and forcibly repeated such as in the line or
during congestion (hereinafter referred to as non-moving state). These states
include motion without movement, which leads to a false detection of walking
and direction estimation in PDR. The sitting condition requires to distinguish it
from the standing condition which may start moving at any time. The machine
learning technique is efective in recognizing such irregular actions and states. In
order to improve the positioning accuracy, some methods perform movement or
state identiication using highly expensive and high-precision sensors such as the
Inertial Measurement Unit(IMU)[
2.1
Makita et al.[
One method uses the IMU as a method of utilizing other sensors. The IMU is
a very high accuracy sensor compared to the micro electro mechanical system
mounted on a general smartphone and often used for PDR. Coskun et al.[
Qian et al.[
In the proposed method, we created a model by logistic regression. Logistic
regression is a regression model classiied as supervised machine learning having
the features that enable inding useful features because the weight of each feature
that leads to classiication is known. Normal walking is deined as ”walking”,
walking which is similar to stopping is deined as ”moored walk”(e.g, crowded).
Standing is deined as ”stop”, sitting is deined as ”sitting”. In addition, we also
identify standing and sitting movements for using the state transitions(3.1)
Evaluation phase In the evaluation phase, the user’s walking context is
determined by analyzing the sensor data using a trained context-aware model. The
adopted t value is the same as that used in the learning phase. The extracted
feature vectors are plotted on the data space, and a trained context recognition
model detects the context of the feature vectors. The identiied walking context
is merged with the ordinal PDR result to obtain the PDR result with context
information. The PDR algorithm is adapted to the PDR method proposed by
Yoshimi et al. [
In this evaluation, an attempt was made to recognize the state in which the user is moving in the experimental building. Several chaise lounges were used in the experimental environment. The subject was asked to walk, move in a row, sit down, and stop. The subject in their twenties walked a speciic route around the laboratory. The subject sat on a bench on the route, asked to change pace at any time, and was allowed to move his posture, stride, foot, etc. As a means of data collection, special sensor devices and sensors are not suitable for practical use;thus we used the common smartphone Nexus 5 for evaluation. The smartphone is put in the pocket of the pants in consideration of the burden on the user.
For the feature quantities of the test data, the weighting factors in each classiier were added up. The result of the classiier with the largest weighting factor value was adopted as the inal estimation result. The weighting factor for the feature value changed with each learning;thus 10 classiiers were created for each label, and the weighting factor values were summed. In order to compare the estimation accuracy using multiple classiiers performed by the proposed method, the case of using a single classiier is compared. The results of using a single classiier and the F value of each class of the proposed method (with or without state transition) are shown in the table, and the accuracy rate of each method is shown.(1)
When a single discriminator was used the window width was set to 250 ms, and suicient features (steps 1 and 2) of walking and mooring walk cannot be obtained. The identiication accuracy of stand and sit motions were high, but the state identiication accuracy was low. However, in the proposed method that created two classiiers, gait etc. could be captured by other classiiers. Moreover the features could be captured, and the classiication rate of the state could be improved. Therefore, the method using classiiers with diferent window widths was efective, but the state transition did not improve the estimation accuracy that much. This is considered to be caused by a mistake, which caused an erroneous transition by giving priority to the triggering of the leaving / sitting motion. Therefore, it is thought that a mechanism such as a transition pending state is needed so that it does not immediately transition when coming and going sitting behavior comes in the future, and decides the transition by looking at some subsequent estimation results.The experiment for DDR was conducted with the route and behavior as shown in the FIG3.
As a result, compared to the conventional method, the step detection error and the direction estimation error at the time of stopping, sitting, and moored walk are reduced(Fig4). During the stop, while the user is seated, the proposed method does not move almost at all, and the moored walk has few positioning errors due to a step estimation error. In the case shown in the igure, the efect looks small because it is a short time experiment, but it can be said that it is necessary to optimize according to the user’s state like the proposed method, assuming long time use. This study proposed a method that used sensor data such as acceleration and angular acceleration that are commonly used in conventional PDR technology to detect characteristic daily walking patterns and movements,judge the user’s condition and use PDR. We proposed DDR to optimize the algorithm and reduce the positioning error. Our future tasks aim to utilize this method in the actual environment(e.g.oices) improve the estimation accuracy of the classiier, and add motions and states to be identiied. As a state to be added, we think about the movement to the side where the direction and movement direction of the body are diferent, and the movement to the rear. The PDR operates with the same movement direction as the direction of the body, these movements cannot be distinguished from normal walking, which causes positioning errors. We also aim to discover new issues by operating the proposed method in an environment closer to the actual usage environment.