407 research outputs found
Magnetic and radar sensing for multimodal remote health monitoring
With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained
Accuracy Evaluation on the Respiration Rate Estimation using Off-the-shelf Pulse-Doppler Radar
This student paper presents preliminary results of using a pulse Doppler radar to detect the respiration rate of human subjects, examining the accuracy of the approach and evaluating the parameters to obtain the most precise result. In the study, the respiration data is recorded by repeatedly detecting people seated in front of the radar at different ranges as well as different aspect angles each time. Then Movement Target Indication, short-time Fourier transform and the analysis of the choice of doppler bins and window size of STFT are performed to evaluate the respiration rate and its precision. The results indicate that the respiration rate can be successfully detected at various ranges and angles and the relationship between Doppler bins and window size in processing is also observed to help us find the most accurate respiration rate
On the optimality of misspecified spectral algorithms
In the misspecified spectral algorithms problem, researchers usually assume
the underground true function , a
less-smooth interpolation space of a reproducing kernel Hilbert space (RKHS)
for some . The existing minimax optimal results
require where is the embedding index, a constant
depending on . Whether the spectral algorithms are optimal for all
is an outstanding problem lasting for years. In this paper, we
show that spectral algorithms are minimax optimal for any
, where is the eigenvalue decay
rate of . We also give several classes of RKHSs whose embedding
index satisfies . Thus, the spectral algorithms
are minimax optimal for all on these RKHSs.Comment: 48 pages, 2 figure
Kernel interpolation generalizes poorly
One of the most interesting problems in the recent renaissance of the studies
in kernel regression might be whether the kernel interpolation can generalize
well, since it may help us understand the `benign overfitting henomenon'
reported in the literature on deep networks. In this paper, under mild
conditions, we show that for any , the generalization error of
kernel interpolation is lower bounded by . In other
words, the kernel interpolation generalizes poorly for a large class of
kernels. As a direct corollary, we can show that overfitted wide neural
networks defined on sphere generalize poorly
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay
The widely observed 'benign overfitting phenomenon' in the neural network
literature raises the challenge to the 'bias-variance trade-off' doctrine in
the statistical learning theory. Since the generalization ability of the 'lazy
trained' over-parametrized neural network can be well approximated by that of
the neural tangent kernel regression, the curve of the excess risk (namely, the
learning curve) of kernel ridge regression attracts increasing attention
recently. However, most recent arguments on the learning curve are heuristic
and are based on the 'Gaussian design' assumption. In this paper, under mild
and more realistic assumptions, we rigorously provide a full characterization
of the learning curve: elaborating the effect and the interplay of the choice
of the regularization parameter, the source condition and the noise. In
particular, our results suggest that the 'benign overfitting phenomenon' exists
in very wide neural networks only when the noise level is small
Multimodal radar sensing for ambient assisted living
Data acquired from health and behavioural monitoring of daily life activities can be exploited to provide real-time medical and nursing service with affordable cost and higher efficiency. A variety of sensing technologies for this purpose have been developed and presented in the literature, for instance, wearable IMU (Inertial Measurement Unit) to measure acceleration and angular speed of the person, cameras to record the images or video sequence, PIR (Pyroelectric infrared) sensor to detect the presence of the person based on Pyroelectric Effect, and radar to estimate distance and radial velocity of the person.
Each sensing technology has pros and cons, and may not be optimal for the tasks. It is possible to leverage the strength of all these sensors through information fusion in a multimodal fashion. The fusion can take place at three different levels, namely, i) signal level where commensurate data are combined, ii) feature level where feature vectors of different sensors are concatenated and iii) decision level where confidence level or prediction label of classifiers are used to generate a new output. For each level, there are different fusion algorithms, the key challenge here is mainly on choosing the best existing fusion algorithm and developing novel fusion algorithms that more suitable for the current application.
The fundamental contribution of this thesis is therefore exploring possible information fusion between radar, primarily FMCW (Frequency Modulated Continuous Wave) radar, and wearable IMU, between distributed radar sensors, and between UWB impulse radar and pressure sensor array. The objective is to sense and classify daily activities patterns, gait styles and micro-gestures as well as producing early warnings of high-risk events such as falls. Initially, only âsnapshotâ activities (single activity within a short X-s measurement) have been collected and analysed for verifying the accuracy improvement due to information fusion. Then continuous activities (activities that are performed one after another with random duration and transitions) have been collected to simulate the real-world case scenario. To overcome the drawbacks of conventional sliding-window approach on continuous data, a Bi-LSTM (Bidirectional Long Short-Term Memory) network is proposed to identify the transitions of daily activities. Meanwhile, a hybrid fusion framework is presented to exploit the power of soft and hard fusion. Moreover, a trilateration-based signal level fusion method has been successfully applied on the range information of three UWB (Ultra-wideband) impulse radar and the results show comparable performance as using micro-Doppler signature, at the price of much less computation loads. For classifying âsnapshotâ activities, fusion between radar and wearable shows approximately 12% accuracy improvement compared to using radar only, whereas for classifying continuous activities and gaits, our proposed hybrid fusion and trilateration-based signal level improves roughly 6.8% (before 89%, after 95.8%) and 7.3% (before 85.4%, after 92.7%), respectively
Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks
Fingerprint traits are widely recognized for their unique qualities and
security benefits. Despite their extensive use, fingerprint features can be
vulnerable to puppet attacks, where attackers manipulate a reluctant but
genuine user into completing the authentication process. Defending against such
attacks is challenging due to the coexistence of a legitimate identity and an
illegitimate intent. In this paper, we propose PUPGUARD, a solution designed to
guard against puppet attacks. This method is based on user behavioral patterns,
specifically, the user needs to press the capture device twice successively
with different fingers during the authentication process. PUPGUARD leverages
both the image features of fingerprints and the timing characteristics of the
pressing intervals to establish two-factor authentication. More specifically,
after extracting image features and timing characteristics, and performing
feature selection on the image features, PUPGUARD fuses these two features into
a one-dimensional feature vector, and feeds it into a one-class classifier to
obtain the classification result. This two-factor authentication method
emphasizes dynamic behavioral patterns during the authentication process,
thereby enhancing security against puppet attacks. To assess PUPGUARD's
effectiveness, we conducted experiments on datasets collected from 31 subjects,
including image features and timing characteristics. Our experimental results
demonstrate that PUPGUARD achieves an impressive accuracy rate of 97.87% and a
remarkably low false positive rate (FPR) of 1.89%. Furthermore, we conducted
comparative experiments to validate the superiority of combining image features
and timing characteristics within PUPGUARD for enhancing resistance against
puppet attacks
The Impact of Perception of CSR on Purchase Intention with Mediating Role of Loyalty and Trust
In the era of economic globalization, with the progress of society and the intensification of competition among enterprises, taking corresponding responsibilities to relevant stakeholders and strengthening corporate social responsibilities are becoming the new trend of modern corporate philosophy. China's dairy production and per capita consumption have increased substantially, but in recent years, a series of dairy safety problems have weakened consumers' confidence. Chinese consumers expect to buy safe and reliable dairy products and hope for dairy companies that are more capable of undertaking corporate social responsibility. The purpose of this study is to examine the impact of consumersâ perception of CSR in Chinaâs dairy industry on purchase intention and the mediating role of consumer trust and consumer loyalty. A survey methodology with quantitative data analysis was used. 218 valid questionnaires were collected online, and the data was analyzed via SPSS software. The empirical results of this study reveal that perception of CSR has positive influence on consumersâ purchase intention, and trust and loyalty both have mediating effect on perception of CSR and consumersâ purchase intention. The findings provide some management implications for dairy enterprises such as implementing diversified CSR activities, enhancing publicity, avoiding greenwashing, and insisting on long-term CSR strategy. Limitations and further research are also discussed
Activity recognition with cooperative radar systems at C and K band
Remote health monitoring is a key component in the future of healthcare with predictive and fall risk estimation applications required in great need and with urgency. Radar, through the exploitation of the micro-Doppler effect, is able to generate signatures that can be classified automatically. In this work, features from two different radar systems operating at C band and K band have been used together co-operatively to classify ten indoor human activities with data from 20 subjects with a support vector machine classifier. Feature selection has been applied to remove redundancies and find a set of salient features for the radar systems, individually and in the fused scenario. Using the aforementioned methods, we show improvements in the classification accuracy for the systems from 75 and 70% for the radar systems individually, up to 89% when fused
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