46 research outputs found
TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation
Wearable sensor-based human activity recognition (HAR) has emerged as a
principal research area and is utilized in a variety of applications. Recently,
deep learning-based methods have achieved significant improvement in the HAR
field with the development of human-computer interaction applications. However,
they are limited to operating in a local neighborhood in the process of a
standard convolution neural network, and correlations between different sensors
on body positions are ignored. In addition, they still face significant
challenging problems with performance degradation due to large gaps in the
distribution of training and test data, and behavioral differences between
subjects. In this work, we propose a novel Transformer-based Adversarial
learning framework for human activity recognition using wearable sensors via
Self-KnowledgE Distillation (TASKED), that accounts for individual sensor
orientations and spatial and temporal features. The proposed method is capable
of learning cross-domain embedding feature representations from multiple
subjects datasets using adversarial learning and the maximum mean discrepancy
(MMD) regularization to align the data distribution over multiple domains. In
the proposed method, we adopt the teacher-free self-knowledge distillation to
improve the stability of the training procedure and the performance of human
activity recognition. Experimental results show that TASKED not only
outperforms state-of-the-art methods on the four real-world public HAR datasets
(alone or combined) but also improves the subject generalization effectively.Comment: 17 pages, 5 figures, Submitted to Knowledge-Based Systems, Elsevier.
arXiv admin note: substantial text overlap with arXiv:2110.1216
Learning Graph Patterns of Reflection Coefficient for Non-destructive Diagnosis of Cu Interconnects
With the increasing operating frequencies and clock speeds in processors,
interconnects affect both the reliability and performance of entire electronic
systems. Fault detection and diagnosis of the interconnects are crucial for
prognostics and health management (PHM) of electronics. However, traditional
approaches using electrical signals as prognostic factors often face challenges
in distinguishing defect root causes, necessitating additional destructive
evaluations, and are prone to noise interference, leading to potential false
alarms. To address these limitations, this paper introduces a novel approach
for non-destructive detection and diagnosis of defects in Cu interconnects,
offering early detection, enhanced diagnostic accuracy, and noise resilience.
Our approach uniquely analyzes both the root cause and severity of interconnect
defects by leveraging graph patterns of reflection coefficient, a technique
distinct from traditional time series signal analysis. We experimentally
demonstrate that the graph patterns possess the capability for fault diagnosis
and serve as effective input data for learning algorithms. Additionally, we
introduce a novel severity rating ensemble learning (SREL) approach, which
significantly enhances diagnostic accuracy and noise robustness. Experimental
results demonstrate that the proposed method outperforms conventional machine
learning methods and multi-class convolutional neural networks (CNN), achieving
a maximum accuracy of 99.3%, especially under elevated noise levels
Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition
We address the well-known wearable activity recognition problem of having to
work with sensors that are non-optimal in terms of information they provide but
have to be used due to wearability/usability concerns (e.g. the need to work
with wrist-worn IMUs because they are embedded in most smart watches). To
mitigate this problem we propose a method that facilitates the use of
information from sensors that are only present during the training process and
are unavailable during the later use of the system. The method transfers
information from the source sensors to the latent representation of the target
sensor data through contrastive loss that is combined with the classification
loss during joint training. We evaluate the method on the well-known PAMAP2 and
Opportunity benchmarks for different combinations of source and target sensors
showing average (over all activities) F1 score improvements of between 5% and
13% with the improvement on individual activities, particularly well suited to
benefit from the additional information going up to between 20% and 40%.Comment: Presented at Ubicomp/ISWC 202
MeciFace: Mechanomyography and Inertial Fusion based Glasses for Edge Real-Time Recognition of Facial and Eating Activities
The increasing prevalence of stress-related eating behaviors and their impact
on overall health highlights the importance of effective monitoring systems. In
this paper, we present MeciFace, an innovative wearable technology designed to
monitor facial expressions and eating activities in real-time on-the-edge
(RTE). MeciFace aims to provide a low-power, privacy-conscious, and highly
accurate tool for promoting healthy eating behaviors and stress management. We
employ lightweight convolutional neural networks as backbone models for facial
expression and eating monitoring scenarios. The MeciFace system ensures
efficient data processing with a tiny memory footprint, ranging from 11KB to
19KB. During RTE evaluation, the system achieves impressive performance,
yielding an F1-score of < 86% for facial expression recognition and 90% for
eating/drinking monitoring, even for the RTE of an unseen user.Comment: Submitted to Nature Scientific Report
Selecting the motion ground truth for loose-fitting wearables: benchmarking optical MoCap methods
To help smart wearable researchers choose the optimal ground truth methods
for motion capturing (MoCap) for all types of loose garments, we present a
benchmark, DrapeMoCapBench (DMCB), specifically designed to evaluate the
performance of optical marker-based and marker-less MoCap. High-cost
marker-based MoCap systems are well-known as precise golden standards. However,
a less well-known caveat is that they require skin-tight fitting markers on
bony areas to ensure the specified precision, making them questionable for
loose garments. On the other hand, marker-less MoCap methods powered by
computer vision models have matured over the years, which have meager costs as
smartphone cameras would suffice. To this end, DMCB uses large real-world
recorded MoCap datasets to perform parallel 3D physics simulations with a wide
range of diversities: six levels of drape from skin-tight to extremely draped
garments, three levels of motions and six body type - gender combinations to
benchmark state-of-the-art optical marker-based and marker-less MoCap methods
to identify the best-performing method in different scenarios. In assessing the
performance of marker-based and low-cost marker-less MoCap for casual loose
garments both approaches exhibit significant performance loss (>10cm), but for
everyday activities involving basic and fast motions, marker-less MoCap
slightly outperforms marker-based MoCap, making it a favorable and
cost-effective choice for wearable studies
PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation
Ground pressure exerted by the human body is a valuable source of information
for human activity recognition (HAR) in unobtrusive pervasive sensing. While
data collection from pressure sensors to develop HAR solutions requires
significant resources and effort, we present a novel end-to-end framework,
PresSim, to synthesize sensor data from videos of human activities to reduce
such effort significantly. PresSim adopts a 3-stage process: first, extract the
3D activity information from videos with computer vision architectures; then
simulate the floor mesh deformation profiles based on the 3D activity
information and gravity-included physics simulation; lastly, generate the
simulated pressure sensor data with deep learning models. We explored two
approaches for the 3D activity information: inverse kinematics with mesh
re-targeting, and volumetric pose and shape estimation. We validated PresSim
with an experimental setup with a monocular camera to provide input and a
pressure-sensing fitness mat (80x28 spatial resolution) to provide the sensor
ground truth, where nine participants performed a set of predefined yoga
sequences.Comment: Percom2023 workshop(UMUM2023
Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition
Recognizing human activities from sensor data is a vital task in various
domains, but obtaining diverse and labeled sensor data remains challenging and
costly. In this paper, we propose an unsupervised statistical feature-guided
diffusion model for sensor-based human activity recognition. The proposed
method aims to generate synthetic time-series sensor data without relying on
labeled data, addressing the scarcity and annotation difficulties associated
with real-world sensor data. By conditioning the diffusion model on statistical
information such as mean, standard deviation, Z-score, and skewness, we
generate diverse and representative synthetic sensor data. We conducted
experiments on public human activity recognition datasets and compared the
proposed method to conventional oversampling methods and state-of-the-art
generative adversarial network methods. The experimental results demonstrate
that the proposed method can improve the performance of human activity
recognition and outperform existing techniques