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P2LHAP: Wearable-sensor-based human activity recognition, Segmentation, and Forecast Through Patch-to-Label Seq2Seq Transformer

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IEEE INTERNET OF THINGS JOURNAL 2025年第6期12卷 6818-6830页

作者： Li, Shuangjian Zhu, Tao Nie, Mingxing Ning, Huansheng Liu, Zhenyu Chen, Liming Univ South China Sch Comp Sci Hengyang 421001 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields, such as healthcare and assisted living, where real-time understanding of ongoing and upcoming activities is crucial. This article introduces P2LHAP, a novel Patch-to-Label Seq2Seq framework that tackles all three tasks in an efficient single-task model. P2LHAP divides sensor data streams into a sequence of "patches," served as input tokens, and outputs a sequence of patch-level activity labels, including the predicted future activities. A unique smoothing technique based on surrounding patch labels, is proposed to identify activity boundaries accurately. Additionally, P2LHAP learns patch-level representation by sensor signal channel-independent Transformer encoders and decoders. All channels share embedding and Transformer weights across all sequences. Evaluated on the three public datasets, P2LHAP significantly outperforms the state-of-the-art in all three tasks, demonstrating its effectiveness and potential for real-world applications.

关键词： Predictive models Transformers Data models Multitasking Time series analysis human activity recognition Feature extraction Computational modeling Computer architecture Streams activity forecast activity segmentation deep learning (DL) patch-to-label Seq2Seq framework sensor-based human activity recognition

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Robust two stages federated learning for sensor based human activity recognition with label noise

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SCIENTIFIC REPORTS 2025年第1期15卷 1-16页

作者： Sun, Haifeng Yao, Junping Li, Xiaojun Liu, Yanfei Gu, Hongyang Rocket Force Univ Engn Xian 710025 Peoples R China

Federated learning is widely used for collaborative training of human activity recognition models across multiple devices with limited local data. However, label noise caused by human and time constraints during data annotation is common and severely limits model performance. Existing studies mainly address this through client selection and sample filtering, but still face key limitations: (1) insufficient granularity in client quality evaluation;(2) aggregation methods ignoring data quality differences;(3) client drift under non-IID data distribution. To overcome these challenges of complex label noise and feature drift, this paper proposes LN-FHAR, a two-stage federated learning framework with label noise robustness. This framework effectively mitigates the coupling problem of noise and data heterogeneity by assessing client data quality and designing differentiated training strategies. In the client selection stage, clients are graded based on class-level loss analysis and a Gaussian Mixture Model. In the noise-robust training stage, reliable neighbors are introduced to collaboratively filter clean samples, and prototype regularization is employed to constrain the consistency between local models and global feature representations. Additionally, a data-aware aggregation method is designed, which assigns weights based on both the quality and quantity of client data, reducing the negative impact of noisy clients. Experimental results demonstrate that LN-FHAR has robustness and generalization ability in complex noise environments.

关键词： Federated noisy learning Data quality sensor-based human activity recognition

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Codebook Approach for sensor-based human activity recognition 16

Codebook Approach for Sensor-based Human Activity Recognitio...

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ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / 20th ACM International Symposium on Wearable Computers (ISWC)

作者： Shirahama, Kimiaki Koeping, Lukas Grzegorzek, Marcin Univ Siegen Pattern Recognit Grp Holderlinst 3 D-57076 Siegen Germany

ISBN: (纸本)9781450344623

One crucial problem in sensor-based human activity recognition is how to model features that can precisely represent characteristics of a sequence of sensor values. For this, we study a codebook approach that represents the sequence as a distribution of characteristic subsequences. The extensive experiments on different recognition tasks for physical, mental and eye-based activities validate the effectiveness, generality and usability of the codebook approach, where only a few intuitive parameters need to be tuned.

关键词： sensor-based human activity recognition Feature learning Codebook approach Sequence classification

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sensor-based Open-Set human activity recognition Using Representation Learning With Mixup Triplets

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IEEE ACCESS 2022年 10卷 119333-119344页

作者： Lee, Minjung Kim, Seoung Bum Korea Univ Sch Ind & Management Engn Seoul 02841 South Korea

The main objective of sensor-based human activity recognition (HAR) is to classify predefined human physical activities with multichannel signals acquired from wearable sensors. In a real-world scenario, signal data is changing over time and undefined activities may occur. Thus, an open-set classifier for HAR is required to detect it as an unknown class rather than to assign it to the one of the known classes. However, open-set HAR is a challenging task because of the small variability of inter-activities and the large variability of intra-activities. To tackle this problem, we propose mixup triplet learning for Mahalanobis distance (MTMD) for open-set recognition. In MTMD, we introduce a new representation learning with stochastically linearly interpolated triplets (mixup triplets) that makes discriminative features of the known from the unknown. In the trained embedding space with the proposed representation learning, thresholds based on Mahalanobis distances from the centers of each known activity are defined for the proposed open-set action recognition. To demonstrate the effectiveness of MTMD, we conducted experiments on sensor-based HAR benchmark datasets. The experimental results show that the proposed MTMD outperforms the existing methods for various open-set experimental settings in terms of numerical results of F1, recall, precision, and accuracy.

关键词： Open-set recognition human activity recognition sensor-based human activity recognition mixup triplet loss Mahalanobis distance

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Hierarchical Feature Recovery for Robust human activity recognition in Body sensor Networks

Hierarchical Feature Recovery for Robust Human Activity Reco...

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Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers

作者： Nobuyuki Oishi Paula Lago Phil Birch Daniel Roggen Wearable Technologies Laboratory University of Sussex United Kingdom Concordia University Canada Engineering and Informatics University of Sussex United Kingdom University of Sussex United Kingdom

ISBN: (纸本)9781450394239

With the advances in Body sensor Networks (BSNs) and textile-integrated sensing, more sensor data becomes available from which human activities are recognised. However, some sensors may become unavailable unexpectedly in practice. Previous work proposed to complement the features of a missing sensor with regression-based methods but considered only up to one sensor missing and thus lacked a mechanism for selecting relevant sensors when multiple sensors were missing. The number of unique combinations of missing sensors increases exponentially when multiple sensors may be missing. To handle this, we propose a Hierarchical Feature Recovery (HFR) approach. We first assess the dependencies between sensors by comparing the feature mapping accuracy between each sensor and then evaluate the HFR approach on a dataset of activities of daily living with 17 gestures using 14 motion sensors. Our HFR method can alleviate classification performance drop by up to 8.3 pp compared to a baseline method.

关键词： Robustness Body sensor Networks Fault-tolerance sensor-based human activity recognition

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Progressive Cross-modal Knowledge Distillation for human Action recognition 22

Progressive Cross-modal Knowledge Distillation for Human Act...

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30th ACM International Conference on Multimedia (MM)

作者： Ni, Jianyuan Ngu, Anne H. H. Yan, Yan Texas State Univ San Marcos TX USA IIT Chicago IL 60616 USA

ISBN: (纸本)9781450392037

Wearable sensor-based human Action recognition (HAR) has achieved remarkable success recently. However, the accuracy performance of wearable sensor-based HAR is still far behind the ones from the visual modalities-based system (i.e., RGB video, skeleton and depth). Diverse input modalities can provide complementary cues and thus improve the accuracy performance of HAR, but how to take advantage of multi-modal data on wearable sensor-based HAR has rarely been explored. Currently, wearable devices, i.e., smartwatches, can only capture limited kinds of non-visual modality data. This hinders the multi-modal HAR association as it is unable to simultaneously use both visual and non-visual modality data. Another major challenge lies in how to efficiently utilize multi-modal data on wearable devices with their limited computation resources. In this work, we propose a novel Progressive Skeleton-to-sensor Knowledge Distillation (PSKD) model which utilizes only time-series data, i.e., accelerometer data, from a smartwatch for solving the wearable sensor-based HAR problem. Specifically, we construct multiple teacher models using data from both teacher (human skeleton sequence) and student (time-series accelerometer data) modalities. In addition, we propose an effective progressive learning scheme to eliminate the performance gap between teacher and student models. We also designed a novel loss function called Adaptive-Confidence Semantic (ACS), to allow the student model to adaptively select either one of the teacher models or the ground-truth label it needs to mimic. To demonstrate the effectiveness of our proposed PSKD method, we conduct extensive experiments on Berkeley-MHAD, UTD-MHAD and MMAct datasets. The results confirm that the proposed PSKD method has competitive performance compared to the previous mono sensor-based HAR methods.

关键词： Knowledge distillation Progressive learning sensor-based human activity recognition machine learning

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Open Set Mixed-Reality human activity recognition

Open Set Mixed-Reality Human Activity Recognition

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IEEE Global Communications Conference (GLOBECOM)

作者： Zhang, Zixuan Chu, Lei Xia, Songpengcheng Pei, Ling Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728181042

sensor-based human activity recognition (HAR) is a fundamental problem that can have a broad impact on many research/industrial fields. The deep learning methods pave the way for extracting robust and informative features from the non-stationary HAR data, achieving high-accuracy HAR. Most of the works in the literature consider the closed set activity recognition, which assumes all classes of activities are known in both the training and test stages. However, it is a challenging way to apply deep learning models to real-world applications, which can contain unseen activities. These activities will sharply decrease the performance of the deep learning models. To this end, we introduce the problem of Open Set Mixed-Reality (OSM) HAR, which aims to recognize unseen activities while classify seen samples. Furthermore, we propose a novel balanced open set backpropagation method to realize accurate and robust OSM-HAR. Lastly, we verify the effectiveness of the proposed method with a publicly available and our newly collected dataset.

关键词： sensor-based human activity recognition mixed reality open set recognition balanced open set backpropagation

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Recognizing multi-user activities using wearable sensors in a smart home

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PERVASIVE AND MOBILE COMPUTING 2011年第3期7卷 287-298页

作者： Wang, Liang Gu, Tao Tao, Xianping Chen, Hanhua Lu, Jian Univ So Denmark Dept Math & Comp Sci Odense Denmark Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China

The advances of wearable sensors and wireless networks offer many opportunities to recognize human activities from sensor readings in pervasive computing. Existing work so far focuses mainly on recognizing activities of a single user in a home environment. However, there are typically multiple inhabitants in a real home and they often perform activities together. In this paper, we investigate the problem of recognizing multi-user activities using wearable sensors in a home setting. We develop a multi-modal, wearable sensor platform to collect sensor data for multiple users, and study two temporal probabilistic models-Coupled Hidden Markov Model (CHMM) and Factorial Conditional Random Field (FCRF)-to model interacting processes in a sensor-based, multi-user scenario. We conduct a real-world trace collection done by two subjects over two weeks, and evaluate these two models through our experimental studies. Our experimental results show that we achieve an accuracy of 96.41% with CHMM and an accuracy of 87.93% with FCRF, respectively, for recognizing multi-user activities. (C) 2010 Elsevier B.V. All rights reserved.

关键词： sensor-based human activity recognition Multi-user activity recognition Probabilistic model Wireless sensor network

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