20 research outputs found
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values
Human activity recognition (HAR) using IMU sensors, namely accelerometer and
gyroscope, has several applications in smart homes, healthcare and
human-machine interface systems. In practice, the IMU-based HAR system is
expected to encounter variations in measurement due to sensor degradation,
alien environment or sensor noise and will be subjected to unknown activities.
In view of practical deployment of the solution, analysis of statistical
confidence over the activity class score are important metrics. In this paper,
we therefore propose XAI-BayesHAR, an integrated Bayesian framework, that
improves the overall activity classification accuracy of IMU-based HAR
solutions by recursively tracking the feature embedding vector and its
associated uncertainty via Kalman filter. Additionally, XAI-BayesHAR acts as an
out of data distribution (OOD) detector using the predictive uncertainty which
help to evaluate and detect alien input data distribution. Furthermore, Shapley
value-based performance of the proposed framework is also evaluated to
understand the importance of the feature embedding vector and accordingly used
for model compressio
Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
The increasing integration of technology in our daily lives demands the development of
more convenient human–computer interaction (HCI) methods. Most of the current hand-based HCI
strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations
on the operating environment. Further, the deployment of such systems is often not performed
in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper
presents a novel hand gesture recognition system based on frequency-modulated continuous wave
(FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems.
First of all, the paper introduces a method to simplify radar preprocessing while preserving the main
information of the performed gestures. Then, a deep neural classifier with the novel Depthwise
Expansion Module based on the depthwise separable convolutions is presented. The introduced
classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts
eight different hand gestures performed by five users, offering a classification accuracy of 98.13%
while operating in a low-power and resource-constrained environment.Electronic Components and Systems for European
Leadership Joint Undertaking under grant agreement No. 826655 (Tempo).European Union’s Horizon 2020 research and innovation programme and
Belgium, France, Germany, Switzerland, and the NetherlandsLodz University of Technology
XAI-Increment: A Novel Approach Leveraging LIME Explanations for Improved Incremental Learning
Explainability of neural network prediction is essential to understand
feature importance and gain interpretable insight into neural network
performance. In this work, model explanations are fed back to the feed-forward
training to help the model generalize better. To this extent, a custom weighted
loss where the weights are generated by considering the Euclidean distances
between true LIME (Local Interpretable Model-Agnostic Explanations)
explanations and model-predicted LIME explanations is proposed. Also, in
practical training scenarios, developing a solution that can help the model
learn sequentially without losing information on previous data distribution is
imperative due to the unavailability of all the training data at once. Thus,
the framework known as XAI-Increment incorporates the custom weighted loss
developed with elastic weight consolidation (EWC), to maintain performance in
sequential testing sets. Finally, the training procedure involving the custom
weighted loss shows around 1% accuracy improvement compared to the traditional
loss based training for the keyword spotting task on the Google Speech Commands
dataset and also shows low loss of information when coupled with EWC in the
incremental learning setup
Utilizing Explainable AI for improving the Performance of Neural Networks
Nowadays, deep neural networks are widely used in a variety of fields that
have a direct impact on society. Although those models typically show
outstanding performance, they have been used for a long time as black boxes. To
address this, Explainable Artificial Intelligence (XAI) has been developing as
a field that aims to improve the transparency of the model and increase their
trustworthiness. We propose a retraining pipeline that consistently improves
the model predictions starting from XAI and utilizing state-of-the-art
techniques. To do that, we use the XAI results, namely SHapley Additive
exPlanations (SHAP) values, to give specific training weights to the data
samples. This leads to an improved training of the model and, consequently,
better performance. In order to benchmark our method, we evaluate it on both
real-life and public datasets. First, we perform the method on a radar-based
people counting scenario. Afterward, we test it on the CIFAR-10, a public
Computer Vision dataset. Experiments using the SHAP-based retraining approach
achieve a 4% more accuracy w.r.t. the standard equal weight retraining for
people counting tasks. Moreover, on the CIFAR-10, our SHAP-based weighting
strategy ends up with a 3% accuracy rate than the training procedure with equal
weighted samples.Comment: accepted at ICMLA 202
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling
Data selection is essential for any data-based optimization technique, such
as Reinforcement Learning. State-of-the-art sampling strategies for the
experience replay buffer improve the performance of the Reinforcement Learning
agent. However, they do not incorporate uncertainty in the Q-Value estimation.
Consequently, they cannot adapt the sampling strategies, including exploration
and exploitation of transitions, to the complexity of the task. To address
this, this paper proposes a new sampling strategy that leverages the
exploration-exploitation trade-off. This is enabled by the uncertainty
estimation of the Q-Value function, which guides the sampling to explore more
significant transitions and, thus, learn a more efficient policy. Experiments
on classical control environments demonstrate stable results across various
environments. They show that the proposed method outperforms state-of-the-art
sampling strategies for dense rewards w.r.t. convergence and peak performance
by 26% on average.Comment: Accepted at ICASSP 202
Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition
Gesture recognition is one of the most intuitive ways of interaction and has
gathered particular attention for human computer interaction. Radar sensors
possess multiple intrinsic properties, such as their ability to work in low
illumination, harsh weather conditions, and being low-cost and compact, making
them highly preferable for a gesture recognition solution. However, most
literature work focuses on solutions with a limited range that is lower than a
meter. We propose a novel architecture for a long-range (1m - 2m) gesture
recognition solution that leverages a point cloud-based cross-learning approach
from camera point cloud to 60-GHz FMCW radar point cloud, which allows learning
better representations while suppressing noise. We use a variant of Dynamic
Graph CNN (DGCNN) for the cross-learning, enabling us to model relationships
between the points at a local and global level and to model the temporal
dynamics a Bi-LSTM network is employed. In the experimental results section, we
demonstrate our model's overall accuracy of 98.4% for five gestures and its
generalization capability.Comment: Submitted to IEEE Sensor Array and Multichannel Signal Processing
Workshop (SAM 2022
Space-Time Waveform Coding for Joint Radar and Wireless Communications (RadCom) Applications
Character Recognition in Air-Writing Based on Network of Radars for Human-Machine Interface
Interdiffusion in the Fe-Pt System
Diffusion-couple experiments are conducted in the Fe-Pt system. The phase boundary compositions of the phases measured in this study are found to be different than the compositions published previously. In the gamma-FePt solid solution, the interdiffusion coefficient increases with the Pt content up to 25 at. pct Pt. Fe is the faster diffusing species in this phase. The trend in the interdiffusion coefficient is explained with the help of calculated driving force for diffusion. To reduce errors, the average interdiffusion coefficients are calculated in the FePt and FePt3 compounds