629 research outputs found
RIDI: Robust IMU Double Integration
This paper proposes a novel data-driven approach for inertial navigation,
which learns to estimate trajectories of natural human motions just from an
inertial measurement unit (IMU) in every smartphone. The key observation is
that human motions are repetitive and consist of a few major modes (e.g.,
standing, walking, or turning). Our algorithm regresses a velocity vector from
the history of linear accelerations and angular velocities, then corrects
low-frequency bias in the linear accelerations, which are integrated twice to
estimate positions. We have acquired training data with ground-truth motions
across multiple human subjects and multiple phone placements (e.g., in a bag or
a hand). The qualitatively and quantitatively evaluations have demonstrated
that our algorithm has surprisingly shown comparable results to full Visual
Inertial navigation. To our knowledge, this paper is the first to integrate
sophisticated machine learning techniques with inertial navigation, potentially
opening up a new line of research in the domain of data-driven inertial
navigation. We will publicly share our code and data to facilitate further
research
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
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
An adaptive, real-time cadence algorithm for unconstrained sensor placement
This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods.22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s- 1), running (3.4 m s- 1), and sprinting (5.2 and 5.7 m s- 1), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm.The described algorithm resulted in consistent numbers of true positives (85.6-100.0%) and false positives (0.0-2.9%) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ยฑ SD: 98.9 ยฑ 0.2%), compared to conventional cross-correlation (87.3 ยฑ 13.5%), biased (73.0 ยฑ 16.2) and unbiased (82.2 ยฑ 20.6) autocorrelation procedures.This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions
์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
Recommendations for determining the validity of consumer wearable and smartphone step count: expert statement and checklist of the INTERLIVE network
PBJ is supported by the Portuguese Foundation for Science and Technology (SFRH/BPD/115977/2016). WJ is partly funded by Science Foundation Ireland (12/RC/2289_P2). PMG and FBO are supported by grants from the MINECO/FEDER (DEP2016-79512-R) and from the University of Granada, Plan Propio de Investigacion 2016, Excellence actions: Units of Excellence; Scientific Excellence Unit on Exercise and Health (UCEES); Junta de Andalucia, Consejeria de Conocimiento, Investigacion y Universidades and European Regional Development Funds (ref. SOMM17/6107/UGR). JMM is partly funded by Private Stiftung Ewald Marquardt fur Wissenschaft und Technik, Kunst und Kultur. UE and JS are partly funded by the Research Council of Norway (249932/F20). AG is supported a European Research Council Grant (grant number 716657). ELS is supported by TrygFonden (grant number 310081). This research was partly funded by Huawei Technologies, Finland.Consumer wearable and smartphone devices provide an accessible means to objectively measure physical activity (PA) through step counts. With the increasing proliferation of this technology, consumers, practitioners and researchers are interested in leveraging these devices as a means to track and facilitate PA behavioural change. However, while the acceptance of these devices is increasing, the validity of many consumer devices have not been rigorously and transparently evaluated. The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives to develop best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice consumer wearable and smartphone step counter validation protocol. A two-step process was used to aggregate data and form a scientific foundation for the development of an optimal and feasible validation protocol: (1) a systematic literature review and (2) additional searches of the wider literature pertaining to factors that may introduce bias during the validation of these devices. The systematic literature review process identified 2897 potential articles, with 85 articles deemed eligible for the final dataset. From the synthesised data, we identified a set of six key domains to be considered during design and reporting of validation studies: target population, criterion measure, index measure, validation conditions, data processing and statistical analysis. Based on these six domains, a set of key variables of interest were identified and a 'basic' and 'advanced' multistage protocol for the validation of consumer wearable and smartphone step counters was developed. The INTERLIVE consortium recommends that the proposed protocol is used when considering the validation of any consumer wearable or smartphone step counter. Checklists have been provided to guide validation protocol development and reporting. The network also provide guidance for future research activities, highlighting the imminent need for the development of feasible alternative 'gold-standard' criterion measures for free-living validation. Adherence to these validation and reporting standards will help ensure methodological and reporting consistency, facilitating comparison between consumer devices. Ultimately, this will ensure that as these devices are integrated into standard medical care, consumers, practitioners, industry and researchers can use this technology safely and to its full potential.Portuguese Foundation for Science and Technology SFRH/BPD/115977/2016Science Foundation IrelandEuropean Commission 12/RC/2289_P2MINECO/FEDER DEP2016-79512-RUniversity of Granada, Plan Propio de Investigacion 2016, Excellence actions: Units of ExcellenceScientific Excellence Unit on Exercise and Health (UCEES)European Commission SOMM17/6107/UGRPrivate Stiftung Ewald Marquardt fur Wissenschaft und Technik, Kunst und KulturResearch Council of Norway 249932/F20European Research Council (ERC)
European Commission 716657TrygFonden 310081Huawei TechnologiesJunta de Andaluci
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