23 research outputs found
Fall Detection Using Channel State Information from WiFi Devices
Falls among the independently living elderly population are a major public health worry, leading to injuries, loss of confidence to live independently and even to death. Each year, one in three people aged 65 and older falls and one in five of them suffers fatal or non fatal injuries. Therefore, detecting a fall early and alerting caregivers can potentially save lives and increase the standard of living. Existing solutions, e.g. push-button, wearables, cameras, radar, pressure and vibration sensors, have limited public adoption either due to the requirement for wearing the device at all times or installing specialized and expensive infrastructure. In this thesis, a device-free, low cost indoor fall detection system using commodity WiFi devices is presented. The system uses physical layer Channel State Information (CSI) to detect falls. Commercial WiFi hardware is cheap and ubiquitous and CSI provides a wealth of information which helps in maintaining good fall detection accuracy even in challenging environments. The goals of the research in this thesis are the design, implementation and experimentation of a device-free fall detection system using CSI extracted from commercial WiFi devices. To achieve these objectives, the following contributions are made herein. A novel time domain human presence detection scheme is developed as a precursor to detecting falls. As the next contribution, a novel fall detection system is designed and developed. Finally, two main enhancements to the fall detection system are proposed to improve the resilience to changes in operating environment. Experiments were performed to validate system performance in diverse environments. It can be argued that through collection of real world CSI traces, understanding the behavior of CSI during human motion, the development of a signal processing tool-set to facilitate the recognition of falls and validation of the system using real world experiments significantly advances the state of the art by providing a more robust fall detection scheme
Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios
The present paper analyzes the performance of localization systems, based on dual-band Direction of Arrival (DoA) approach, in multi-path affected scenarios. The implemented DoA estimation, which belongs to the so-called Space and Frequency Division Multiple Access (SFDMA) technique, takes advantage of the use of two uncorrelated communication carrier frequencies, as already demonstrated by the authors. Starting from these results, this paper provides, first, the methodology followed to describe the localization system in the proposed simulation environment, and, as a second step, describes how multi-path effects may be taken into account through a set of full-wave simulations. The latter follows an approach based on the two-ray model. The validation of the proposed approach is demonstrated by simulations over a wide range of virtual scenarios. The analysis of the results highlights the ability of the proposed approach to describe multi-path effects and confirms enhancements in DoA estimation as experimentally evaluated by the same authors. To further assess the performance of the aforementioned simulation environment, a comparison between simulated and measured results was carried out, confirming the capability to predict DoA performance
์ฌ๋ฌผ์ธํฐ๋ท์ ์ํ ๋ฌด์ ์ค๋ด ์ธก์ ์๊ณ ๋ฆฌ์ฆ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2022.2. ๊น์ฑ์ฒ .์ค๋ด ์์น ๊ธฐ๋ฐ ์๋น์ค๋ ์ค๋งํธํฐ์ ์ด์ฉํ ์ค๋ด์์์ ๊ฒฝ๋ก์๋ด, ์ค๋งํธ ๊ณต์ฅ์์์ ์์ ๊ด๋ฆฌ, ์ค๋ด ๋ก๋ด์ ์์จ์ฃผํ ๋ฑ ๋ง์ ๋ถ์ผ์ ์ ๋ชฉ๋ ์ ์์ผ๋ฉฐ, ์ฌ๋ฌผ์ธํฐ๋ท ์์ฉ์๋ ํ์์ ์ธ ๊ธฐ์ ์ด๋ค. ๋ค์ํ ์์น ๊ธฐ๋ฐ ์๋น์ค๋ฅผ ๊ตฌํํ๊ธฐ ์ํด์๋ ์ ํํ ์์น ์ ๋ณด๊ฐ ํ์ํ๋ฉฐ, ์ ํฉํ ๊ฑฐ๋ฆฌ ๋ฐ ์์น๋ฅผ ์ถ์ ๊ธฐ์ ์ด ํต์ฌ์ ์ด๋ค. ์ผ์ธ์์๋ ์์ฑํญ๋ฒ์์คํ
์ ์ด์ฉํด์ ์์น ์ ๋ณด๋ฅผ ํ๋ํ ์ ์๋ค.
๋ณธ ํ์๋
ผ๋ฌธ์์๋ ์์ดํ์ด ๊ธฐ๋ฐ ์ธก์ ๊ธฐ์ ์ ๋ํด ๋ค๋ฃฌ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก, ์ ํ์ ์ ํธ ์ธ๊ธฐ ๋ฐ ๋๋ฌ ์๊ฐ์ ์ด์ฉํ ์ ๋ฐํ ์ค๋ด ์์น ์ถ์ ์ ์ํ ์ธ ๊ฐ์ง ๊ธฐ์ ์ ๋ํด ๋ค๋ฃฌ๋ค. ๋จผ์ , ๋น๊ฐ์๊ฒฝ๋ก ํ๊ฒฝ์์์ ๊ฑฐ๋ฆฌ ์ถ์ ์ ํ๋๋ฅผ ํฅ์์์ผ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ ์ธก์์ ์ ํ๋๋ฅผ ํฅ์์ํค๋ ํ์ด๋ธ๋ฆฌ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์๋์ผ ๋ฐด๋ ๋์ญ์ ์ ํธ์ธ๊ธฐ๋ฅผ ๊ฐ์๋์ ์ธก์ ํ์ฌ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ ์ธก์ ๊ธฐ๋ฒ์ ์ ์ฉํ ๋, ๊ฑฐ๋ฆฌ ์ถ์ ๋ถ ๋จ๊ณ๋ง์ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ํ์ต์ ์ด์ฉํ ๊น์ ์ ๊ฒฝ๋ง ํ๊ท ๋ชจ๋ธ๋ก ๋์ฒดํ ๋ฐฉ์์ด๋ค. ์ ์ ํ ํ์ต๋ ๊น์ ํ๊ท ๋ชจ๋ธ์ ์ฌ์ฉ์ผ๋ก ๋น๊ฐ์๊ฒฝ๋ก ํ๊ฒฝ์์ ๋ฐ์ํ๋ ๊ฑฐ๋ฆฌ ์ถ์ ์ค์ฐจ๋ฅผ ํจ๊ณผ์ ์ผ๋ก ๊ฐ์์ํฌ ์ ์์ผ๋ฉฐ, ๊ฒฐ๊ณผ์ ์ผ๋ก ์์น ์ถ์ ์ค์ฐจ ๋ํ ๊ฐ์์์ผฐ๋ค. ์ ์ํ ๋ฐฉ๋ฒ์ ์ค๋ด ๊ด์ ์ถ์ ๊ธฐ๋ฐ ๋ชจ์์คํ์ผ๋ก ํ๊ฐํ์ ๋, ๊ธฐ์กด ๊ธฐ๋ฒ๋ค์ ๋นํด์ ์์น ์ถ์ ์ค์ฐจ๋ฅผ ์ค๊ฐ๊ฐ์ ๊ธฐ์ค์ผ๋ก 22.3% ์ด์ ์ค์ผ ์ ์์์ ๊ฒ์ฆํ๋ค. ์ถ๊ฐ์ ์ผ๋ก, ์ ์ํ ๋ฐฉ๋ฒ์ ์ค๋ด์์์ AP ์์น๋ณํ ๋ฑ์ ๊ฐ์ธํจ์ ํ์ธํ๋ค.
๋ค์์ผ๋ก, ๋ณธ ๋
ผ๋ฌธ์์๋ ๋น๊ฐ์๊ฒฝ๋ก์์ ๋จ์ผ ๋์ญ ์์ ์ ํธ์ธ๊ธฐ๋ฅผ ์ธก์ ํ์ ๋ ๋น๊ฐ์๊ฒฝ๋ก๊ฐ ๋ง์ ์ค๋ด ํ๊ฒฝ์์ ์์น ์ถ์ ์ ํ๋๋ฅผ ๋์ด๊ธฐ ์ํ ๋ฐฉ์์ ์ ์ํ๋ค. ๋จ์ผ ๋์ญ ์์ ์ ํธ์ธ๊ธฐ๋ฅผ ์ด์ฉํ๋ ๋ฐฉ์์ ๊ธฐ์กด์ ์ด์ฉ๋๋ ์์ดํ์ด, ๋ธ๋ฃจํฌ์ค, ์ง๋น ๋ฑ์ ๊ธฐ๋ฐ์์ค์ ์ฝ๊ฒ ์ ์ฉ๋ ์ ์๊ธฐ ๋๋ฌธ์ ๋๋ฆฌ ์ด์ฉ๋๋ค. ํ์ง๋ง ์ ํธ ์ธ๊ธฐ์ ๋จ์ผ ๊ฒฝ๋ก์์ค ๋ชจ๋ธ์ ์ด์ฉํ ๊ฑฐ๋ฆฌ ์ถ์ ์ ์๋นํ ์ค์ฐจ๋ฅผ ์ง๋
์ ์์น ์ถ์ ์ ํ๋๋ฅผ ๊ฐ์์ํจ๋ค. ์ด๋ฌํ ๋ฌธ์ ์ ์์ธ์ ๋จ์ผ ๊ฒฝ๋ก์์ค ๋ชจ๋ธ๋ก๋ ์ค๋ด์์์ ๋ณต์กํ ์ ํ ์ฑ๋ ํน์ฑ์ ๋ฐ์ํ๊ธฐ ์ด๋ ต๊ธฐ ๋๋ฌธ์ด๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์ค๋ด ์์น ์ถ์ ์ ์ํ ๋ชฉ์ ์ผ๋ก, ์ค์ฒฉ๋ ๋ค์ค ์ํ ๊ฒฝ๋ก ๊ฐ์ ๋ชจ๋ธ์ ์๋กญ๊ฒ ์ ์ํ๋ค. ์ ์ํ ๋ชจ๋ธ์ ๊ฐ์๊ฒฝ๋ก ๋ฐ ๋น๊ฐ์๊ฒฝ๋ก์์์ ์ฑ๋ ํน์ฑ์ ๊ณ ๋ คํ์ฌ ์ ์ฌ์ ์ธ ํ๋ณด ์ํ๋ค์ ์ง๋๋ค. ํ ์๊ฐ์ ์์ ์ ํธ ์ธ๊ธฐ ์ธก์ ์น์ ๋ํด ๊ฐ ๊ธฐ์ค ๊ธฐ์ง๊ตญ๋ณ๋ก ์ต์ ์ ๊ฒฝ๋ก์์ค ๋ชจ๋ธ ์ํ๋ฅผ ๊ฒฐ์ ํ๋ ํจ์จ์ ์ธ ๋ฐฉ์์ ์ ์ํ๋ค. ์ด๋ฅผ ์ํด ๊ธฐ์ง๊ตญ๋ณ ๊ฒฝ๋ก์์ค๋ชจ๋ธ ์ํ์ ์กฐํฉ์ ๋ฐ๋ฅธ ์ธก์ ๊ฒฐ๊ณผ๋ฅผ ํ๊ฐํ ์งํ๋ก์ ๋น์ฉํจ์๋ฅผ ์ ์ํ์๋ค. ๊ฐ ๊ธฐ์ง๊ตญ๋ณ ์ต์ ์ ์ฑ๋ ๋ชจ๋ธ์ ์ฐพ๋๋ฐ ํ์ํ ๊ณ์ฐ ๋ณต์ก๋๋ ๊ธฐ์ง๊ตญ ์์ ์ฆ๊ฐ์ ๋ฐ๋ผ ๊ธฐํ๊ธ์์ ์ผ๋ก ์ฆ๊ฐํ๋๋ฐ, ์ ์ ์๊ณ ๋ฆฌ์ฆ์ ์ด์ฉํ ํ์์ ์ ์ฉํ์ฌ ๊ณ์ฐ๋์ ์ต์ ํ์๋ค. ์ค๋ด ๊ด์ ์ถ์ ๋ชจ์์คํ์ ํตํ ๊ฒ์ฆ๊ณผ ์ค์ธก ๊ฒฐ๊ณผ๋ฅผ ์ด์ฉํ ๊ฒ์ฆ์ ์งํํ์์ผ๋ฉฐ, ์ ์ํ ๋ฐฉ์์ ์ค์ ์ค๋ด ํ๊ฒฝ์์ ๊ธฐ์กด์ ๊ธฐ๋ฒ๋ค์ ๋นํด ์์น ์ถ์ ์ค์ฐจ๋ฅผ ์ฝ 31% ๊ฐ์์์ผฐ์ผ๋ฉฐ ํ๊ท ์ ์ผ๋ก 1.92 m ์์ค์ ์ ํ๋๋ฅผ ๋ฌ์ฑํจ์ ํ์ธํ๋ค.
๋ง์ง๋ง์ผ๋ก FTM ํ๋กํ ์ฝ์ ์ด์ฉํ ์ค๋ด ์์น ์ถ์ ์๊ณ ๋ฆฌ์ฆ์ ๋ํด ์ฐ๊ตฌํ์๋ค. ์ค๋งํธํฐ์ ๋ด์ฅ ๊ด์ฑ ์ผ์์ ์์ดํ์ด ํต์ ์์ ์ ๊ณตํ๋ FTM ํ๋กํ ์ฝ์ ํตํ ๊ฑฐ๋ฆฌ ์ถ์ ์ ์ด์ฉํ์ฌ ์ค๋ด์์ ์ฌ์ฉ์์ ์์น๋ฅผ ์ถ์ ํ ์ ์๋ค. ํ์ง๋ง ์ค๋ด์ ๋ณต์กํ ๋ค์ค๊ฒฝ๋ก ํ๊ฒฝ์ผ๋ก ์ธํ ํผํฌ ๊ฒ์ถ ์คํจ๋ ๊ฑฐ๋ฆฌ ์ธก์ ์น์ ํธํฅ์ฑ์ ์ ๋ฐํ๋ค. ๋ํ ์ฌ์ฉํ๋ ๋๋ฐ์ด์ค์ ์ข
๋ฅ์ ๋ฐ๋ผ ์์์น ๋ชปํ ๊ฑฐ๋ฆฌ ์ค์ฐจ๊ฐ ๋ฐ์ํ ์์๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ค์ ํ๊ฒฝ์์ FTM ๊ฑฐ๋ฆฌ ์ถ์ ์ ์ด์ฉํ ๋ ๋ฐ์ํ ์ ์๋ ์ค์ฐจ๋ค์ ๊ณ ๋ คํ๊ณ ์ด๋ฅผ ๋ณด์ํ๋ ๋ฐฉ์์ ๋ํด ์ ์ํ๋ค. ํ์ฅ ์นผ๋ง ํํฐ์ ๊ฒฐํฉํ์ฌ FTM ๊ฒฐ๊ณผ๋ฅผ ์ฌ์ ํํฐ๋ง ํ์ฌ ์ด์๊ฐ์ ์ ๊ฑฐํ๊ณ , ๊ฑฐ๋ฆฌ ์ธก์ ์น์ ํธํฅ์ฑ์ ์ ๊ฑฐํ์ฌ ์์น ์ถ์ ์ ํ๋๋ฅผ ํฅ์์ํจ๋ค. ์ค๋ด์์์ ์คํ ๊ฒฐ๊ณผ ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ๊ฑฐ์น ์ธก์ ์น์ ํธํฅ์ฑ์ ์ฝ 44-65% ๊ฐ์์์ผฐ์ผ๋ฉฐ ์ต์ข
์ ์ผ๋ก ์ฌ์ฉ์์ ์์น๋ฅผ ์๋ธ๋ฏธํฐ๊ธ์ผ๋ก ์ถ์ ํ ์ ์์์ ๊ฒ์ฆํ๋ค.Indoor location-based services (LBS) can be combined with various applications such as indoor navigation for smartphone users, resource management in smart factories, and autonomous driving of robots. It is also indispensable for Internet of Things (IoT) applications. For various LBS, accurate location information is essential. Therefore, a proper ranging and positioning algorithm is important. For outdoors, the global navigation satellite system (GNSS) is available to provide position information. However, the GNSS is inappropriate indoors owing to the issue of the blocking of the signals from satellites. It is necessary to develop a technology that can replace GNSS in GNSS-denied environments. Among the various alternative systems, the one of promising technology is to use a Wi-Fi system that has already been applied to many commercial devices, and the infrastructure is in place in many regions.
In this dissertation, Wi-Fi based indoor localization methods are presented. In the specific, I propose the three major issues related to accurate indoor localization using received signal strength (RSS) and fine timing measurement (FTM) protocol in the 802.11 standard for my dissertation topics.
First, I propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. I replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error.
Next, I study on positioning algorithm that considering NLOS conditions for each APs, using single band RSS measurement. The single band RSS information is widely used for indoor localization because they can be easily implemented by using existing infrastructure like Wi-Fi, Blutooth, or Zigbee. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, I develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and NLOS states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, I formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, I apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92~m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09\% over the benchmark scenario.
Finally, I investigate accurate indoor tracking algorithm using FTM protocol in this dissertation.
By using the FTM ranging and the built-in sensors in a smartphone, it is possible to track the user's location in indoor. However, the failure of first peak detection due to the multipath effect causes a bias in the FTM ranging results in the practical indoor environment. Additionally, the unexpected ranging error dependent on device type also degrades the indoor positioning accuracy. In this study, I considered the factors of ranging error in the FTM protocol in practical indoor environment, and proposed a method to compensate ranging error. I designed an EKF-based tracking algorithm that adaptively removes outliers from the FTM result and corrects bias to increase positioning accuracy. The experimental results verified that the proposed algorithm reduces the average ofthe ranging bias by 43-65\% in an indoor scenarios, and can achieve the sub-meter accuracy in average route mean squared error of user's position in the experiment scenarios.Abstract i
Contents iv
List of Tables vi
List of Figures vii
1 INTRODUCTION 1
2 Hybrid Approach for Indoor Localization Using Received Signal Strength
of Dual-BandWi-Fi 6
2.1 Motivation 6
2.2 Preliminary 8
2.3 System model 11
2.4 Proposed Ranging Method 13
2.5 Performance Evaluation 16
2.5.1 Ray-Tracing-Based Simulation 16
2.5.2 Analysis of the Ranging Accuracy 21
2.5.3 Analysis of the Neural Network Structure 25
2.5.4 Analysis of Positioning Accuracy 26
2.6 Summary 29
3 Genetic Algorithm for Path Loss Model Selection in Signal Strength Based
Indoor Localization 31
3.1 Motivation 31
3.2 Preliminary 34
3.2.1 RSS-based Ranging Techniques 35
3.2.2 Positioning Technique 37
3.3 Proposed localization method 38
3.3.1 Localization Algorithm with Overlapped Multi-State Path Loss
Model 38
3.3.2 Localization with Genetic Algorithm-Based Search 41
3.4 Performance evaluation 46
3.4.1 Numerical simulation 50
3.4.2 Experimental results 56
3.5 Summary 60
4 Indoor User Tracking with Self-calibrating Range Bias Using FTM Protocol
62
4.1 Motivation 62
4.2 Preliminary 63
4.2.1 FTM ranging 63
4.2.2 PDR-based trajectory estimation 65
4.3 EKF design for adaptive compensation of ranging bias 66
4.4 Performance evaluation 69
4.4.1 Experimental scenario 69
4.4.2 Experimental results 70
4.5 Summary 75
5 Conclusion 76
Abstract (In Korean) 89๋ฐ
Pattern Recognition
Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition
Image-set, Temporal and Spatiotemporal Representations of Videos for Recognizing, Localizing and Quantifying Actions
This dissertation addresses the problem of learning video representations, which is defined here as transforming the video so that its essential structure is made more visible or accessible for action recognition and quantification. In the literature, a video can be represented by a set of images, by modeling motion or temporal dynamics, and by a 3D graph with pixels as nodes. This dissertation contributes in proposing a set of models to localize, track, segment, recognize and assess actions such as (1) image-set models via aggregating subset features given by regularizing normalized CNNs, (2) image-set models via inter-frame principal recovery and sparsely coding residual actions, (3) temporally local models with spatially global motion estimated by robust feature matching and local motion estimated by action detection with motion model added, (4) spatiotemporal models 3D graph and 3D CNN to model time as a space dimension, (5) supervised hashing by jointly learning embedding and quantization, respectively. State-of-the-art performances are achieved for tasks such as quantifying facial pain and human diving. Primary conclusions of this dissertation are categorized as follows: (i) Image set can capture facial actions that are about collective representation; (ii) Sparse and low-rank representations can have the expression, identity and pose cues untangled and can be learned via an image-set model and also a linear model; (iii) Norm is related with recognizability; similarity metrics and loss functions matter; (v) Combining the MIL based boosting tracker with the Particle Filter motion model induces a good trade-off between the appearance similarity and motion consistence; (iv) Segmenting object locally makes it amenable to assign shape priors; it is feasible to learn knowledge such as shape priors online from Web data with weak supervision; (v) It works locally in both space and time to represent videos as 3D graphs; 3D CNNs work effectively when inputted with temporally meaningful clips; (vi) the rich labeled images or videos help to learn better hash functions after learning binary embedded codes than the random projections. In addition, models proposed for videos can be adapted to other sequential images such as volumetric medical images which are not included in this dissertation
Machine Learning-based Detection of Compensatory Balance Responses and Environmental Fall Risks Using Wearable Sensors
Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide, with serious and costly consequences. Compensatory balance responses (CBRs) are reactions to recover stability following a loss of balance, potentially resulting in a fall if sufficient recovery mechanisms are not activated. While performance of CBRs are demonstrated risk factors for falls in seniors, the frequency, type, and underlying cause of these incidents occurring in everyday life have not been well investigated.
This study was spawned from the lack of research on development of fall risk assessment methods that can be used for continuous and long-term mobility monitoring of the geri- atric population, during activities of daily living, and in their dwellings. Wearable sensor systems (WSS) offer a promising approach for continuous real-time detection of gait and balance behavior to assess the risk of falling during activities of daily living. To detect CBRs, we record movement signals (e.g. acceleration) and activity patterns of four muscles involving in maintaining balance using wearable inertial measurement units (IMUs) and surface electromyography (sEMG) sensors. To develop more robust detection methods, we investigate machine learning approaches (e.g., support vector machines, neural networks) and successfully detect lateral CBRs, during normal gait with accuracies of 92.4% and 98.1% using sEMG and IMU signals, respectively.
Moreover, to detect environmental fall-related hazards that are associated with CBRs, and affect balance control behavior of seniors, we employ an egocentric mobile vision system mounted on participants chest. Two algorithms (e.g. Gabor Barcodes and Convolutional Neural Networks) are developed. Our vision-based method detects 17 different classes of environmental risk factors (e.g., stairs, ramps, curbs) with 88.5% accuracy. To the best of the authors knowledge, this study is the first to develop and evaluate an automated vision-based method for fall hazard detection
Symmetry-Adapted Machine Learning for Information Security
Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis
Biometric Systems
Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study