507 research outputs found
Real-Time Step Detection Using Unconstrained Smartphone
Nowadays smartphones are carrying more and more sensors among which are inertial
sensors. These devices provide information about the movement and forces acting on
the device, but they can also provide information about the movement of the user. Step
detection is at the core of many smartphone applications such as indoor location, virtual
reality, health and activity monitoring, and some of these require high levels of precision.
Current state of the art step detection methods rely heavily in the prediction of the
movements performed by the user and the smartphone or on methods of activity recognition
for parameter tuning. These methods are limited by the number of situations the
researchers can predict and do not consider false positive situations which occur in daily
living such as jumps or stationary movements, which in turn will contribute to lower
performances.
In this thesis, a novel unconstrained smartphone step detection method is proposed
using Convolutional Neural Networks. The model utilizes the data from the accelerometer
and gyroscope of the smartphone for step detection. For the training of the model, a
data set containing step and false step situations was built with a total of 4 smartphone
placements, 5 step activities and 2 false step activities. The model was tested using the
data from a volunteer which it has not previously seen.
The proposed model achieved an overall recall of 89.87% and an overall precision of
87.90%, while being able to distinguish step and non-step situations. The model also
revealed little difference between the performance in different smartphone placements,
indicating a strong capability towards unconstrained use. The proposed solution demonstrates
more versatility than state of the art alternatives, by presenting comparable results
without the need of parameter tuning or adjustments for the smartphone use case, potentially
allowing for better performances in free living scenarios
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity
Making applications aware of the mobility experienced by the user can open
the door to a wide range of novel services in different use-cases, from smart
parking to vehicular traffic monitoring. In the literature, there are many
different studies demonstrating the theoretical possibility of performing
Transportation Mode Detection (TMD) by mining smart-phones embedded sensors
data. However, very few of them provide details on the benchmarking process and
on how to implement the detection process in practice. In this study, we
provide guidelines and fundamental results that can be useful for both
researcher and practitioners aiming at implementing a working TMD system. These
guidelines consist of three main contributions. First, we detail the
construction of a training dataset, gathered by heterogeneous users and
including five different transportation modes; the dataset is made available to
the research community as reference benchmark. Second, we provide an in-depth
analysis of the sensor-relevance for the case of Dual TDM, which is required by
most of mobility-aware applications. Third, we investigate the possibility to
perform TMD of unknown users/instances not present in the training set and we
compare with state-of-the-art Android APIs for activity recognition.Comment: Pre-print of the accepted version for the 14th Workshop on Context
and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece,
March 19-23, 201
Mobility increases localizability: A survey on wireless indoor localization using inertial sensors
Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.</jats:p
์ฌ์ฉ์ ์ํฉ์ธ์ง ๋ฅ๋ฌ๋์ ์ฌ์ฉํ GPS ๋ฐ์กํ / ๊ด์ฑ ์ผ์ ๊ฒฐํฉ ์ค๋งํธํฐ ๋ณดํ์ ํญ๋ฒ
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2019. 2. ์ฌ์ฌ์ต.๋ณธ ๋
ผ๋ฌธ์์๋ ์ค๋งํธํฐ Galaxy S8 ํ๊ฒฝ์์ GPS / INS ๊ฒฐํฉ ๋ณดํ์ ํญ๋ฒ์ ์ํํ์์ผ๋ฉฐ, ์ค๋งํธํฐ ์ผ์์ ํน์ฑ์ ์์ธํ ๋ถ์ํ์๋ค. ์ด์ ์ต๊ทผ ๊ณต๊ฐ๋ Android GNSS API ๋ฅผ ์ฌ์ฉํ์ฌ GPS ์์๋ฐ์ดํฐ๋ฅผ ํญ๋ฒ์ ์ด์ฉํ๋ฉด์, Cycle slip ์ ๋ณด์ ํ Carrier phase ๋ฅผ ์ด์ฉํ ์๋ ๊ฒฐ์ ๋ฒ์ ์ฌ์ฉํ์๋ค. ์ด๋ก ์ธํด ๊ธฐ์กด์ NMEA GPS ๋ฅผ ์ฌ์ฉํ ๋ฐฉ์์ ์ค๋งํธํฐ ๋ณดํ์ ํญ๋ฒ๋ณด๋ค ์ ๋ฐํ ์์น, ์๋ ํญ๋ฒ์ด ๊ฐ๋ฅํ์๊ณ , ์ฑ๋ฅ์ ํฅ์ ์์ผฐ๋ค. ๋ํ ์ฌ์ฉ์ ์ํฉ ๋ถ์์ด ๊ฐ๋ฅํ ๋ถ๋ฅ ๋ฅ๋ฌ๋ ๊ธฐ๋ฒ์ ์ฌ์ฉํ์ฌ ๊ฐ ๋ณดํ ์ํฉ์ ๋ฐ๋ฅธ ๋ถ๋ฅ๊ฐ์ง๊ฐ ๊ฐ๋ฅํ์์ ๋ณด์์ผ๋ฉฐ, LSTM ์ ์
๋ ฅ๋ถ๋ถ์ ๋ณํํ ๋ช๊ฐ์ง ๋ฅ๋ฌ๋ ๋ชจ๋ธ์ ์ฑ๋ฅ์ ๋น๊ตํ์๋ค. ์ด๋ฅผ ํตํด์ ์ฌ์ฉ์์ ๋ณดํ ์ํฉ์ ๋ฐ๋ฅธ ์ ์์ ๋ณดํ์ ํญ๋ฒ ํ๋ผ๋ฉํฐ ๊ฒฐ์ ์ด ๊ฐ๋ฅํจ์ ๊ฐ๋ฅ์ฑ์ ๋ณด์๋ค.In this research, the overall construction of the smartphone GPS / INS pedestrian dead reckoning system is detaily described with considering the smartphone sensor measurement properties. Also, the recent android GNSS API which can provide the raw GPS measurement is used. With carrier phase, the cycleslip compensated velocity determination is considered. As a result, the carrier phase /INS integrated pedestrian dead reckoning shows the more precise navigation accuracy than NMEA. Moreover, The deep learning approach is applied in the user context classification to change the parameters in the pedestrian dead reckoning system. The author compares the effect of several transformed inputs for the LSTM model and validate each classification performances.Abstract i
Contents ii
List of Figures iv
List of Tables vii
Chapter 1. Introduction 1
1.1 Motivation and Backgrounds 1
1.2 Research Purpose and Contribution 3
1.3 Contents and Methods of Research 3
Chapter 2. Smartphone GPS / INS measurements analysis 4
2.1 Smartphone GNSS measurements 4
2.1.1 Android Raw GNSS Measurements API 4
2.1.2 Raw GPS Measurements Properties 7
2.1.3 Smartphone NMEA Location Provider 8
2.1.4 Pseudorange Based Position Estimation 10
2.1.5 Position Determination Experiment 11
2.2 Smartphone INS Measurements 12
2.2.1 Android Sensor Manager API 12
2.2.2 INS Measurements Properties 13
2.2.3 Noise level, Constant bias, Scale factor, Calibration 14
2.2.4. Accelerometer, Gyroscope Calibration Experiment 17
2.2.5 Magnetometer Ellipse Fitting Calibration 22
2.2.6 Random Bias, Allan Variance Exiperiment 25
2.3 Developed Android Smartphone App 30
Chapter 3. Pedestrian Dead Reckoning 31
3.1 Pedestrian Dead-Reckoning System 31
3.1.1 Attitude Determination Quaternion Kalman Filter 32
3.1.2 Attitude Determination Simulation , Experiment 35
3.1.3 Walking Detection 39
3.1.4 Step Counting, Stride Length 41
3.1.5 Pedestrian Dead Reckoning Experiment 45
Chapter 4. Carrier phase / INS integrated Pedestrian Dead Reckoning 50
4.1 Carrier phase Cycleslip Compensation & Velocity Determination 50
4.1.1 Carrier phase Cycleslip Compensation 50
4.1.2 Android Environment Cycle slip Detection 51
4.1.3 False Alarm & Miss Detection Analysis 55
4.1.4 Doppler, Carrier Based Velocity Estimation 56
4.1.5 Cycle slip Compensation & Velocity Determination Experiment 58
4.2 Raw GPS / INS Integrated Pedestrian Dead Reckoning 63
4.2.1 GPS / INS Integration 63
4.2.2 Position Determination Extended Kalman Filter 65
4.2.3 Raw GPS / INS Integrated Pedestrian Dead Reckoning Experiment 66
Chapter 5. User Context Classification Deep learning for Adaptive PDR 69
5.1 Smartphone Location / Walking Context Classification 69
5.1.1 Smartphone Location / Walking Context Dataset 69
5.1.2 Deep Learning Models 71
5.1.3 Comparison of Input Transformations 72
Chapter 6. Conclusion & Future work 76
Chapter 7. Bibliography 77Maste
Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies
Walking is one of the most common modes of terrestrial locomotion for humans.
Walking is essential for humans to perform most kinds of daily activities. When
a person walks, there is a pattern in it, and it is known as gait. Gait
analysis is used in sports and healthcare. We can analyze this gait in
different ways, like using video captured by the surveillance cameras or depth
image cameras in the lab environment. It also can be recognized by wearable
sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer,
magneto resistive sensors, electromagnetic tracking system, force sensors, and
electromyography (EMG). Analysis through these sensors required a lab
condition, or users must wear these sensors. For detecting abnormality in gait
action of a human, we need to incorporate the sensors separately. We can know
about one's health condition by abnormal human gait after detecting it.
Understanding a regular gait vs. abnormal gait may give insights to the health
condition of the subject using the smart wearable technologies. Therefore, in
this paper, we proposed a way to analyze abnormal human gait through smartphone
sensors. Though smart devices like smartphones and smartwatches are used by
most of the person nowadays. So, we can track down their gait using sensors of
these intelligent wearable devices
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