Human Activity Recognition (HAR) is a cornerstone of ubiquitous computing,
with promising applications in diverse fields such as health monitoring and
ambient assisted living. Despite significant advancements, sensor-based HAR
methods often operate under the assumption that training and testing data have
identical distributions. However, in many real-world scenarios, particularly in
sensor-based HAR, this assumption is invalidated by out-of-distribution
($\displaystyle o.o.d.$) challenges, including differences from heterogeneous
sensors, change over time, and individual behavioural variability. This paper
centres on the latter, exploring the cross-user HAR problem where behavioural
variability across individuals results in differing data distributions. To
address this challenge, we introduce the Deep Temporal State Domain Adaptation
(DTSDA) model, an innovative approach tailored for time series domain
adaptation in cross-user HAR. Contrary to the common assumption of sample
independence in existing domain adaptation approaches, DTSDA recognizes and
harnesses the inherent temporal relations in the data. Therefore, we introduce
'Temporal State', a concept that defined the different sub-activities within an
activity, consistent across different users. We ensure these sub-activities
follow a logical time sequence through 'Temporal Consistency' property and
propose the 'Pseudo Temporal State Labeling' method to identify the
user-invariant temporal relations. Moreover, the design principle of DTSDA
integrates adversarial learning for better domain adaptation. Comprehensive
evaluations on three HAR datasets demonstrate DTSDA's superior performance in
cross-user HAR applications by briding individual behavioral variability using
temporal relations across sub-activities.

基于深度时间状态领域自适应（DTSDA）模型，本研究旨在解决跨用户人类活动识别（cross-user HAR）中的行为变异性问题，通过利用活动中的不同子活动之间的时间关系，DTSDA 模型能够在不同用户之间建立起用户不变的时间关系，从而在跨用户 HAR 应用中展现出卓越的性能。

基于深度域自适应和时间关系信息的跨用户活动识别

Cross-user activity recognition using deep domain adaptation with  temporal relation information

Human Activity Recognition (HAR) models often suffer from performance
degradation in real-world applications due to distribution shifts in activity
patterns across individuals. Test-Time Adaptation (TTA) is an emerging learning
paradigm that aims to utilize the test stream to adjust predictions in
real-time inference, which has not been explored in HAR before. However, the
high computational cost of optimization-based TTA algorithms makes it
intractable to run on resource-constrained edge devices. In this paper, we
propose an Optimization-Free Test-Time Adaptation (OFTTA) framework for
sensor-based HAR. OFTTA adjusts the feature extractor and linear classifier
simultaneously in an optimization-free manner. For the feature extractor, we
propose Exponential DecayTest-time Normalization (EDTN) to replace the
conventional batch normalization (CBN) layers. EDTN combines CBN and Test-time
batch Normalization (TBN) to extract reliable features against domain shifts
with TBN's influence decreasing exponentially in deeper layers. For the
classifier, we adjust the prediction by computing the distance between the
feature and the prototype, which is calculated by a maintained support set. In
addition, the update of the support set is based on the pseudo label, which can
benefit from reliable features extracted by EDTN. Extensive experiments on
three public cross-person HAR datasets and two different TTA settings
demonstrate that OFTTA outperforms the state-of-the-art TTA approaches in both
classification performance and computational efficiency. Finally, we verify the
superiority of our proposed OFTTA on edge devices, indicating possible
deployment in real applications. Our code is available at
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本文提出了一种无需优化的测试时适应（OFTTA）框架，用于基于传感器的人体活动识别（HAR），通过使用指数衰减测试时归一化（EDTN）和基于特征和原型之间的距离的分类器更新方法，OFTTA 能够在分类性能和计算效率方面超越最先进的测试时适应方法。

面向跨人活动识别的无需优化的测试时间适应

Optimization-Free Test-Time Adaptation for Cross-Person Activity  Recognition

Human activity recognition (HAR) is an essential research field that has been
used in different applications including home and workplace automation,
security and surveillance as well as healthcare. Starting from conventional
machine learning methods to the recently developing deep learning techniques
and the Internet of things, significant contributions have been shown in the
HAR area in the last decade. Even though several review and survey studies have
been published, there is a lack of sensor-based HAR overview studies focusing
on summarising the usage of wearable sensors and smart home sensors data as
well as applications of HAR and deep learning techniques. Hence, we overview
sensor-based HAR, discuss several important applications that rely on HAR, and
highlight the most common machine learning methods that have been used for HAR.
Finally, several challenges of HAR are explored that should be addressed to
further improve the robustness of HAR.

人体活动识别（HAR）是一项基础研究领域，已经在家庭与职场自动化、安全监控以及医疗保健等多个应用中得到应用。本文综述了基于传感器的 HAR，讨论了依赖于 HAR 的几个重要应用，并介绍了 HAR 中常用的机器学习方法，同时探讨了 HAR 存在的若干挑战以进一步提高其稳健性。