1,377 research outputs found
Development of a Novel Handheld Device for Active Compensation of Physiological Tremor
In microsurgery, the human hand imposes certain limitations in accurately positioning the tip of a device such as scalpel. Any errors in the motion of the hand make microsurgical procedures difficult and involuntary motions such as hand tremors can make some procedures significantly difficult to perform. This is particularly true in the case of vitreoretinal microsurgery. The most familiar source of involuntary motion is physiological tremor. Real-time compensation of tremor is, therefore, necessary to assist surgeons to precisely position and manipulate the tool-tip to accurately perform a microsurgery. In this thesis, a novel handheld device (AID) is described for compensation of physiological tremor in the hand. MEMS-based accelerometers and gyroscopes have been used for sensing the motion of the hand in six degrees of freedom (DOF). An augmented state complementary Kalman filter is used to calculate 2 DOF orientation. An adaptive filtering algorithm, band-limited Multiple Fourier linear combiner (BMFLC), is used to calculate the tremor component in the hand in real-time. Ionic Polymer Metallic Composites (IPMCs) have been used as actuators for deflecting the tool-tip to compensate for the tremor
Step Detection Algorithm For Accurate Distance Estimation Using Dynamic Step Length
In this paper, a new Smartphone sensor based algorithm is proposed to detect
accurate distance estimation. The algorithm consists of two phases, the first
phase is for detecting the peaks from the Smartphone accelerometer sensor. The
other one is for detecting the step length which varies from step to step. The
proposed algorithm is tested and implemented in real environment and it showed
promising results. Unlike the conventional approaches, the error of the
proposed algorithm is fixed and is not affected by the long distance.
Keywords distance estimation, peaks, step length, accelerometer.Comment: this paper contains of 5 pages and 6 figure
Security of GPS/INS based On-road Location Tracking Systems
Location information is critical to a wide-variety of navigation and tracking
applications. Today, GPS is the de-facto outdoor localization system but has
been shown to be vulnerable to signal spoofing attacks. Inertial Navigation
Systems (INS) are emerging as a popular complementary system, especially in
road transportation systems as they enable improved navigation and tracking as
well as offer resilience to wireless signals spoofing, and jamming attacks. In
this paper, we evaluate the security guarantees of INS-aided GPS tracking and
navigation for road transportation systems. We consider an adversary required
to travel from a source location to a destination, and monitored by a INS-aided
GPS system. The goal of the adversary is to travel to alternate locations
without being detected. We developed and evaluated algorithms that achieve such
goal, providing the adversary significant latitude. Our algorithms build a
graph model for a given road network and enable us to derive potential
destinations an attacker can reach without raising alarms even with the
INS-aided GPS tracking and navigation system. The algorithms render the
gyroscope and accelerometer sensors useless as they generate road trajectories
indistinguishable from plausible paths (both in terms of turn angles and roads
curvature). We also designed, built, and demonstrated that the magnetometer can
be actively spoofed using a combination of carefully controlled coils. We
implemented and evaluated the impact of the attack using both real-world and
simulated driving traces in more than 10 cities located around the world. Our
evaluations show that it is possible for an attacker to reach destinations that
are as far as 30 km away from the true destination without being detected. We
also show that it is possible for the adversary to reach almost 60-80% of
possible points within the target region in some cities
์ ๋ถ ๋ฐ ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ ์ตํฉ์ ์ด์ฉํ ์ค๋งํธํฐ ๋ค์ค ๋์์์ ๋ณดํ ํญ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2020. 8. ๋ฐ์ฐฌ๊ตญ.In this dissertation, an IA-PA fusion-based PDR (Pedestrian Dead Reckoning) using low-cost inertial sensors is proposed to improve the indoor position estimation. Specifically, an IA (Integration Approach)-based PDR algorithm combined with measurements from PA (Parametric Approach) is constructed so that the algorithm is operated even in various poses that occur when a pedestrian moves with a smartphone indoors. In addition, I propose an algorithm that estimates the device attitude robustly in a disturbing situation by an ellipsoidal method. In addition, by using the machine learning-based pose recognition, it is possible to improve the position estimation performance by varying the measurement update according to the poses.
First, I propose an adaptive attitude estimation based on ellipsoid technique to accurately estimate the direction of movement of a smartphone device. The AHRS (Attitude and Heading Reference System) uses an accelerometer and a magnetometer as measurements to calculate the attitude based on the gyro and to compensate for drift caused by gyro sensor errors. In general, the attitude estimation performance is poor in acceleration and geomagnetic disturbance situations, but in order to effectively improve the estimation performance, this dissertation proposes an ellipsoid-based adaptive attitude estimation technique. When a measurement disturbance comes in, it is possible to update the measurement more accurately than the adaptive estimation technique without considering the direction by adjusting the measurement covariance with the ellipsoid method considering the direction of the disturbance. In particular, when the disturbance only comes in one axis, the proposed algorithm can use the measurement partly by updating the other two axes considering the direction. The proposed algorithm shows its effectiveness in attitude estimation under disturbances through the rate table and motion capture equipment.
Next, I propose a PDR algorithm that integrates IA and PA that can be operated in various poses. When moving indoors using a smartphone, there are many degrees of freedom, so various poses such as making a phone call, texting, and putting a pants pocket are possible. In the existing smartphone-based positioning algorithms, the position is estimated based on the PA, which can be used only when the pedestrian's walking direction and the device's direction coincide, and if it does not, the position error due to the mismatch in angle is large. In order to solve this problem, this dissertation proposes an algorithm that constructs state variables based on the IA and uses the position vector from the PA as a measurement. If the walking direction and the device heading do not match based on the pose recognized through machine learning technique, the position is updated in consideration of the direction calculated using PCA (Principal Component Analysis) and the step length obtained through the PA. It can be operated robustly even in various poses that occur.
Through experiments considering various operating conditions and paths, it is confirmed that the proposed method stably estimates the position and improves performance even in various indoor environments.๋ณธ ๋
ผ๋ฌธ์์๋ ์ ๊ฐํ ๊ด์ฑ์ผ์๋ฅผ ์ด์ฉํ ๋ณดํํญ๋ฒ์์คํ
(PDR: Pedestrian Dead Reckoning)์ ์ฑ๋ฅ ํฅ์ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก ๋ณดํ์๊ฐ ์ค๋ด์์ ์ค๋งํธํฐ์ ๋ค๊ณ ์ด๋ํ ๋ ๋ฐ์ํ๋ ๋ค์ํ ๋์ ์ํฉ์์๋ ์ด์ฉ๋ ์ ์๋๋ก, ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฐ ์ธก์ ์น๋ฅผ ์ฌ์ฉํ๋ ์ ๋ถ ๊ธฐ๋ฐ์ ๋ณดํ์ ํญ๋ฒ ์๊ณ ๋ฆฌ์ฆ์ ๊ตฌ์ฑํ๋ค. ๋ํ ํ์์ฒด ๊ธฐ๋ฐ ์์ธ ์ถ์ ์๊ณ ๋ฆฌ์ฆ์ ๊ตฌ์ฑํ์ฌ ์ธ๋ ์ํฉ์์๋ ๊ฐ์ธํ๊ฒ ์์ธ๋ฅผ ์ถ์ ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ถ๊ฐ์ ์ผ๋ก ๊ธฐ๊ณํ์ต ๊ธฐ๋ฐ์ ๋์ ์ธ์ ์ ๋ณด๋ฅผ ์ด์ฉ, ๋์์ ๋ฐ๋ฅธ ์ธก์ ์น ์
๋ฐ์ดํธ๋ฅผ ๋ฌ๋ฆฌํจ์ผ๋ก์จ ์์น ์ถ์ ์ฑ๋ฅ์ ํฅ์์ํจ๋ค.
๋จผ์ ์ค๋งํธํฐ ๊ธฐ๊ธฐ์ ์ด๋ ๋ฐฉํฅ์ ์ ํํ๊ฒ ์ถ์ ํ๊ธฐ ์ํด ํ์์ฒด ๊ธฐ๋ฒ ๊ธฐ๋ฐ ์ ์ ์์ธ ์ถ์ ์ ์ ์ํ๋ค. ์์ธ ์ถ์ ๊ธฐ๋ฒ (AHRS: Attitude and Heading Reference System)์ ์์ด๋ก๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์์ธ๋ฅผ ๊ณ์ฐํ๊ณ ์์ด๋ก ์ผ์์ค์ฐจ์ ์ํด ๋ฐ์ํ๋ ๋๋ฆฌํํธ๋ฅผ ๋ณด์ ํ๊ธฐ ์ํด ์ธก์ ์น๋ก ๊ฐ์๋๊ณ์ ์ง์๊ณ๋ฅผ ์ฌ์ฉํ๋ค. ์ผ๋ฐ์ ์ผ๋ก ๊ฐ์ ๋ฐ ์ง์๊ณ ์ธ๋ ์ํฉ์์๋ ์์ธ ์ถ์ ์ฑ๋ฅ์ด ๋จ์ด์ง๋๋ฐ, ์ถ์ ์ฑ๋ฅ์ ํจ๊ณผ์ ์ผ๋ก ํฅ์์ํค๊ธฐ ์ํด ๋ณธ ๋
ผ๋ฌธ์์๋ ํ์์ฒด ๊ธฐ๋ฐ ์ ์ ์์ธ ์ถ์ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ์ธก์ ์น ์ธ๋์ด ๋ค์ด์ค๋ ๊ฒฝ์ฐ, ์ธ๋์ ๋ฐฉํฅ์ ๊ณ ๋ คํ์ฌ ํ์์ฒด ๊ธฐ๋ฒ์ผ๋ก ์ธก์ ์น ๊ณต๋ถ์ฐ์ ์กฐ์ ํด์ค์ผ๋ก์จ ๋ฐฉํฅ์ ๊ณ ๋ คํ์ง ์์ ์ ์ ์ถ์ ๊ธฐ๋ฒ๋ณด๋ค ์ ํํ๊ฒ ์ธก์ ์น ์
๋ฐ์ดํธ๋ฅผ ํ ์ ์๋ค. ํนํ ์ธ๋์ด ํ ์ถ์ผ๋ก๋ง ๋ค์ด์ค๋ ๊ฒฝ์ฐ, ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ๋ฐฉํฅ์ ๊ณ ๋ คํด ๋๋จธ์ง ๋ ์ถ์ ๋ํด์๋ ์
๋ฐ์ดํธ ํด์ค์ผ๋ก์จ ์ธก์ ์น๋ฅผ ๋ถ๋ถ์ ์ผ๋ก ์ฌ์ฉํ ์ ์๋ค. ๋ ์ดํธ ํ
์ด๋ธ, ๋ชจ์
์บก์ณ ์ฅ๋น๋ฅผ ํตํด ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ์์ธ ์ฑ๋ฅ์ด ํฅ์๋จ์ ํ์ธํ์๋ค.
๋ค์์ผ๋ก ๋ค์ํ ๋์์์๋ ์ด์ฉ ๊ฐ๋ฅํ ์ ๋ถ ๋ฐ ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ์ ์ตํฉํ๋ ๋ณดํํญ๋ฒ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ค๋งํธํฐ์ ์ด์ฉํด ์ค๋ด๋ฅผ ์ด๋ํ ๋์๋ ์์ ๋๊ฐ ํฌ๊ธฐ ๋๋ฌธ์ ์ ํ ๊ฑธ๊ธฐ, ๋ฌธ์, ๋ฐ์ง ์ฃผ๋จธ๋ ๋ฃ๊ธฐ ๋ฑ ๋ค์ํ ๋์์ด ๋ฐ์ ๊ฐ๋ฅํ๋ค. ๊ธฐ์กด์ ์ค๋งํธํฐ ๊ธฐ๋ฐ ๋ณดํ ํญ๋ฒ์์๋ ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ์ ๊ธฐ๋ฐ์ผ๋ก ์์น๋ฅผ ์ถ์ ํ๋๋ฐ, ์ด๋ ๋ณดํ์์ ์งํ ๋ฐฉํฅ๊ณผ ๊ธฐ๊ธฐ์ ๋ฐฉํฅ์ด ์ผ์นํ๋ ๊ฒฝ์ฐ์๋ง ์ฌ์ฉ ๊ฐ๋ฅํ๋ฉฐ ์ผ์นํ์ง ์๋ ๊ฒฝ์ฐ ์์ธ ์ค์ฐจ๋ก ์ธํ ์์น ์ค์ฐจ๊ฐ ํฌ๊ฒ ๋ฐ์ํ๋ค. ์ด๋ฌํ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ๋ณธ ๋
ผ๋ฌธ์์๋ ์ ๋ถ ๊ธฐ๋ฐ ๊ธฐ๋ฒ์ ๊ธฐ๋ฐ์ผ๋ก ์ํ๋ณ์๋ฅผ ๊ตฌ์ฑํ๊ณ ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ์ ํตํด ๋์ค๋ ์์น ๋ฒกํฐ๋ฅผ ์ธก์ ์น๋ก ์ฌ์ฉํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ๋ง์ฝ ๊ธฐ๊ณํ์ต์ ํตํด ์ธ์ํ ๋์์ ๋ฐํ์ผ๋ก ์งํ ๋ฐฉํฅ๊ณผ ๊ธฐ๊ธฐ ๋ฐฉํฅ์ด ์ผ์นํ์ง ์๋ ๊ฒฝ์ฐ, ์ฃผ์ฑ๋ถ ๋ถ์์ ํตํด ๊ณ์ฐํ ์งํ๋ฐฉํฅ์ ์ด์ฉํด ์งํ ๋ฐฉํฅ์, ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ์ ํตํด ์ป์ ๋ณดํญ์ผ๋ก ๊ฑฐ๋ฆฌ๋ฅผ ์
๋ฐ์ดํธํด ์ค์ผ๋ก์จ ๋ณดํ ์ค ๋ฐ์ํ๋ ์ฌ๋ฌ ๋์์์๋ ๊ฐ์ธํ๊ฒ ์ด์ฉํ ์ ์๋ค.
๋ค์ํ ๋์ ์ํฉ ๋ฐ ๊ฒฝ๋ก๋ฅผ ๊ณ ๋ คํ ์คํ์ ํตํด ์์์ ์ ์ํ ๋ฐฉ๋ฒ์ด ๋ค์ํ ์ค๋ด ํ๊ฒฝ์์๋ ์์ ์ ์ผ๋ก ์์น๋ฅผ ์ถ์ ํ๊ณ ์ฑ๋ฅ์ด ํฅ์๋จ์ ํ์ธํ์๋ค.Chapter 1 Introduction 1
1.1 Motivation and Background 1
1.2 Objectives and Contribution 5
1.3 Organization of the Dissertation 6
Chapter 2 Pedestrian Dead Reckoning System 8
2.1 Overview of Pedestrian Dead Reckoning 8
2.2 Parametric Approach 9
2.2.1 Step detection algorithm 11
2.2.2 Step length estimation algorithm 13
2.2.3 Heading estimation 14
2.3 Integration Approach 15
2.3.1 Extended Kalman filter 16
2.3.2 INS-EKF-ZUPT 19
2.4 Activity Recognition using Machine Learning 21
2.4.1 Challenges in HAR 21
2.4.2 Activity recognition chain 22
Chapter 3 Attitude Estimation in Smartphone 26
3.1 Adaptive Attitude Estimation in Smartphone 26
3.1.1 Indirect Kalman filter-based attitude estimation 26
3.1.2 Conventional attitude estimation algorithms 29
3.1.3 Adaptive attitude estimation using ellipsoidal methods 30
3.2 Experimental Results 36
3.2.1 Simulation 36
3.2.2 Rate table experiment 44
3.2.3 Handheld rotation experiment 46
3.2.4 Magnetic disturbance experiment 49
3.3 Summary 53
Chapter 4 Pedestrian Dead Reckoning in Multiple Poses of a Smartphone 54
4.1 System Overview 55
4.2 Machine Learning-based Pose Classification 56
4.2.1 Training dataset 57
4.2.2 Feature extraction and selection 58
4.2.3 Pose classification result using supervised learning in PDR 62
4.3 Fusion of the Integration and Parametric Approaches in PDR 65
4.3.1 System model 67
4.3.2 Measurement model 67
4.3.3 Mode selection 74
4.3.4 Observability analysis 76
4.4 Experimental Results 82
4.4.1 AHRS results 82
4.4.2 PCA results 84
4.4.3 IA-PA results 88
4.5 Summary 100
Chapter 5 Conclusions 103
5.1 Summary of the Contributions 103
5.2 Future Works 105
๊ตญ๋ฌธ์ด๋ก 125
Acknowledgements 127Docto
IRT-SD-SLE:an improved real-time step detection and step length estimation using smartphone accelerometer
Smartphone sensor-based pedestrian dead reckoning (PDR) systems provide a viable solution to the problem of localization in an infrastructure-less area. Step detection (SD) and step length estimation (SLE), being two fundamental operations of the PDR-based localization technique, have drawn many researchersโ attention in the recent time. Most of the existing SD and SLE methods proposed over the years, however, provide either server-or cloud-based solution that consume additional network bandwidth and suffer from increased transmission delay. Moreover, nonavailability of the inertial sensors like gyroscope, magnetometers, etc., at every smartphone makes majority of the existing SLE methods less applicable to such devices. To address the above-said issues, in this article, we focus on devising an improved SLE method that would detect the pedestrianโs steps and subsequently estimate the step length in real-time by processing the accelerometer data at the device itself. Our proposed method transforms the measured acceleration values along the Earth coordinate system (ECS) and also applies sliding window meaning (SWM) to mitigate the negative effects of the smartphoneโs orientation and gravitational bias on the accuracy of SD and SLE. The performances of our proposed method are evaluated in terms of accuracy for ten different users by taking the device in two different postures (handheld and trouser pocket) under two different walking modes (normal and fast) to demonstrate its efficacy. Moreover, our proposed method obtains more than 80% average accuracy for SD and also obtains more than 75% accuracy (median) for SLE for all participants under four different scenarios considered here
A Smart Device To Substitute The Neurothesiometer
This paper presents a patented smart point-of-care testing (POCT) system for the diagnosis and grading of peripheral neuropathy at the patientโs home or care center. The device aims to detect changes or worsening of a patientโs neuropathy. Our system utilizes the vibration motor within a smart-phone, applied through a 3D printed probe attachment to detect sensation loss in vibration sensitivity threshold (VST). A smartphone app displays several neuropathy questionnaires to the user to identify and monitor changes in their condition. This paper presents results from comparison between the new smart device and the gold standard Neurothesiometer. Results suggest that the new device performs closely to the gold standard in terms of the frequency and amplitude of vibration
Development and Testing of a Self-Contained, Portable Instrumentation System for a Fighter Pilot Helmet
A self-contained, portable, inertial and positional measurement system was developed and tested for an HGU-55 model fighter pilot helmet. The system, designated the Portable Helmet Instrumentation System (PHIS), demonstrated the recording of accelerations and rotational rates experienced by the human head in a flight environment. A compact, self-contained, โknee-boardโ sized computer recorded these accelerations and rotational rates during flight. The present research presents the results of a limited evaluation of this helmet-mounted instrumentation system flown in an Extra 300 fully aerobatic aircraft. The accuracy of the helmet-mounted, inertial head tracker system was compared to the aircraft-mounted referenced system. The ability of the Portable Helmet Instrumentation System to record position, orientation and inertial information in ground and flight conditions was evaluated. The capability of the Portable Helmet Instrumentation System to provide position, orientation and inertial information with sufficient fidelity was evaluated. The concepts demonstrated in this system are: 1) calibration of the inertial sensing element without external equipment 2) the use of differential inertial sensing equipment to remove the accelerations and rotational rates of a moving vehicle from the pilotโs head-tracking measurements 3) the determination of three-dimensional position and orientation from three corresponding points using a range sensor. The range sensor did not operate as planned. The helmet only managed to remain within the range sensorโs field of view for 37% of flight time. Vertical accelerations showed the greatest correlation when comparing helmet measurements to aircraft measurements. The PHIS operated well during level flight
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