Human activity recognition (HAR) is a key challenge in pervasive computing
and its solutions have been presented based on various disciplines.
Specifically, for HAR in a smart space without privacy and accessibility
issues, data streams generated by deployed pervasive sensors are leveraged. In
this paper, we focus on a group activity by which a group of users perform a
collaborative task without user identification and propose an efficient group
activity recognition scheme which extracts causality patterns from pervasive
sensor event sequences generated by a group of users to support as good
recognition accuracy as the state-of-the-art graphical model. To filter out
irrelevant noise events from a given data stream, a set of rules is leveraged
to highlight causally related events. Then, a pattern-tree algorithm extracts
frequent causal patterns by means of a growing tree structure. Based on the
extracted patterns, a weighted sum-based pattern matching algorithm computes
the likelihoods of stored group activities to the given test event sequence by
means of matched event pattern counts for group activity recognition. We
evaluate the proposed scheme using the data collected from our testbed and
CASAS datasets where users perform their tasks on a daily basis and validate
its effectiveness in a real environment. Experiment results show that the
proposed scheme performs higher recognition accuracy and with a small amount of
runtime overhead than the existing schemes.

在没有隐私和可访问性问题的智能空间中，基于部署的普适传感器生成的数据流被利用来实现一种高效的群体活动识别方案，该方案从一组用户生成的普适传感器事件序列中提取因果模式，以支持与最先进的图形模型相似的识别准确性。

一种面向群组活动识别的因果感知模式挖掘方案

A Causality-Aware Pattern Mining Scheme for Group Activity Recognition  in a Pervasive Sensor Space

Pervasive computing allows the provision of services in many important areas,
including the relevant and dynamic field of health and well-being. In this
domain, Human Activity Recognition (HAR) has gained a lot of attention in
recent years. Current solutions rely on Machine Learning (ML) models and
achieve impressive results. However, the evolution of these models remains
difficult, as long as a complete retraining is not performed. To overcome this
problem, the concept of Continual Learning is very promising today and, more
particularly, the techniques based on regularization. These techniques are
particularly interesting for their simplicity and their low cost. Initial
studies have been conducted and have shown promising outcomes. However, they
remain very specific and difficult to compare. In this paper, we provide a
comprehensive comparison of three regularization-based methods that we adapted
to the HAR domain, highlighting their strengths and limitations. Our
experiments were conducted on the UCI HAR dataset and the results showed that
no single technique outperformed all others in all scenarios considered.

本研究提供了三种基于正则化的技术在人体活动识别领域的综合比较，强调它们的优势和局限性。实验结果表明，不单一种方法在所有情况下都优于其他方法。

基于正则化的持续学习方法评估：应用于 HAR

Evaluation of Regularization-based Continual Learning Approaches:  Application to HAR

Pervasive computing promotes the installation of connected devices in our
living spaces in order to provide services. Two major developments have gained
significant momentum recently: an advanced use of edge resources and the
integration of machine learning techniques for engineering applications. This
evolution raises major challenges, in particular related to the appropriate
distribution of computing elements along an edge-to-cloud continuum. About
this, Federated Learning has been recently proposed for distributed model
training in the edge. The principle of this approach is to aggregate models
learned on distributed clients in order to obtain a new, more general model.
The resulting model is then redistributed to clients for further training. To
date, the most popular federated learning algorithm uses coordinate-wise
averaging of the model parameters for aggregation. However, it has been shown
that this method is not adapted in heterogeneous environments where data is not
identically and independently distributed (non-iid). This corresponds directly
to some pervasive computing scenarios where heterogeneity of devices and users
challenges machine learning with the double objective of generalization and
personalization. In this paper, we propose a novel aggregation algorithm,
termed FedDist, which is able to modify its model architecture (here, deep
neural network) by identifying dissimilarities between specific neurons amongst
the clients. This permits to account for clients' specificity without impairing
generalization. Furthermore, we define a complete method to evaluate federated
learning in a realistic way taking generalization and personalization into
account.
Using this method, FedDist is extensively tested and compared with three
state-of-the-art federated learning algorithms on the pervasive domain of Human
Activity Recognition with smartphones.

本文探讨了如何应对异构设备和用户的挑战，提出了一种新型的聚合算法 FedDist，该算法能够考虑到客户端的差异性而不影响泛化能力，并在人类活动识别普适领域上通过对比测试发现其表现更好。