169 research outputs found
Functionality-power-packaging considerations in context aware wearable systems
Wearable computing places tighter constraints on architecture design than traditional mobile computing. The architecture is described in terms of miniaturization, power-awareness, global low-power design and suitability for an application. In this article we present a new methodology based on three different system properties. Functionality, power and electronic Packaging metrics are proposed and evaluated to study different trade offs. We analyze the trade offs in different context recognition scenarios. The proof of concept case study is analyzed by studying (a) interaction with household appliances by a wrist worn device (acceleration, light sensors) (b) studying walking behavior with acceleration sensors, (c) computational task and (d) gesture recognition in a wood-workshop using the combination of accelerometer and microphone sensors. After analyzing the case study, we highlight the size aspect by electronic packaging for a given functionality and present the miniaturization trends for ‘autonomous sensor button
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
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
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
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