Pervasive and Mobile Computing最新文献

Resilient UAVs location sharing service based on information freshness and opportunistic deliveries 基于信息新鲜度和机会交付的弹性无人机位置共享服务

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-01 DOI: 10.1016/j.pmcj.2025.102066

Agnaldo de Souza Batista , Aldri Luiz dos Santos

{"title":"Resilient UAVs location sharing service based on information freshness and opportunistic deliveries","authors":"Agnaldo de Souza Batista , Aldri Luiz dos Santos","doi":"10.1016/j.pmcj.2025.102066","DOIUrl":"10.1016/j.pmcj.2025.102066","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAV) have been recognized as a versatile platform for various services. During the flight, these vehicles must avoid collisions to operate safely. In this way, they demand to keep spatial awareness, i.e., to know others in their coverage area. However, mobility and positioning hamper building UAV network infrastructure to support reliable basic services. Thus, such vehicles call for a location service with up-to-date information resilient to false location injection threats. This work proposes FlySafe, a resilient UAV location-sharing service that employs opportunistic approaches to deliver UAVs’ location. FlySafe takes into account the freshness of UAVs’ location to maintain their spatial awareness. Further, it counts on the age of the UAV’s location information to trigger device discovery. Simulation results showed that FlySafe achieved spatial awareness up to 94.15% of UAV operations, being resilient to false locations injected in the network. Moreover, the accuracy in device discovery achieved 94.53% with a location error of less than 2 m.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102066"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differentiating presence in virtual reality using physiological signals 利用生理信号区分虚拟现实中的存在感

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-23 DOI: 10.1016/j.pmcj.2025.102065

Shuvodeep Saha , Chelsea Dobbins , Anubha Gupta , Arindam Dey

{"title":"Differentiating presence in virtual reality using physiological signals","authors":"Shuvodeep Saha , Chelsea Dobbins , Anubha Gupta , Arindam Dey","doi":"10.1016/j.pmcj.2025.102065","DOIUrl":"10.1016/j.pmcj.2025.102065","url":null,"abstract":"<div><div>Advancements in wearable technologies have made the use of physiological signals, such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV), more prevalent for detecting changes in the autonomic nervous system within virtual reality (VR). However, the challenge lies in utilizing these signals to objectively detect presence in VR, which typically relies on self-reports that can be inherently biased. This paper addresses this issue and presents a study (<em>N</em>=26) that investigates the effect that different levels of presence has on physiological responses in VR. A neutral VR environment was created that incorporated three levels of presence (high, medium and low) that were invoked by tuning different parameters. Participants wore a wrist-worn wearable device that captured their physiological signals whilst they experienced each of these environments. Results indicated that tonic and phasic components of the EDA signal were significant in differentiating between the levels. Two novel features, constructed using both the phasic and tonic components of EDA, successfully differentiated between presence levels. Analysis of the HRV data illustrated a significant difference between the low and medium levels using the ratio between low frequency to high frequency.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102065"},"PeriodicalIF":3.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LiteFlex-YOLO:A lightweight small target detection network for maritime unmanned aerial vehicles LiteFlex-YOLO：用于海上无人机的轻型小型目标探测网络

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-22 DOI: 10.1016/j.pmcj.2025.102064

Peng Tang, Yong Zhang

{"title":"LiteFlex-YOLO:A lightweight small target detection network for maritime unmanned aerial vehicles","authors":"Peng Tang, Yong Zhang","doi":"10.1016/j.pmcj.2025.102064","DOIUrl":"10.1016/j.pmcj.2025.102064","url":null,"abstract":"<div><div>With frequent maritime activities, the number of overboard accidents at sea has increased, and rescue delays often lead to people being killed. Unmanned Aerial Vehicles (UAVs) have the advantages of fast localization and real-time monitoring in rescue, but the images taken by UAVs have many small targets, and the detection accuracy is insufficient; at the same time, target detection algorithms are difficult to be deployed due to the limitation of computational resources of UAVs. For this reason, this paper proposes a lightweight target detection model based on YOLOv8s improvement, LiteFlex-YOLO, which aims to improve the performance of target detection in UAVs sea rescue. Firstly, the small target sensing ability of the model is enhanced by introducing the P2 small target detection layer, secondly, replacing the C2f module with the lightweight C2fCIB module reduces the computational complexity to make the model more lightweight, furthermore, the feature extraction ability of the backbone is enhanced by using the ODConv (Omni-Dimensional Dynamic Convolution); Lastly, the attention mechanism of SimAM (Simple Attention Module) is introduced to enhance the attention of the key feature information. The final experimental results showed that, LiteFlex-YOLO achieves a [email protected] of 69.5% on the SeaDronesSee dataset, which is 18.2% improvement compared to YOLOv8s, and the model parameters are reduced to 71.2% of YOLOv8s. Moreover, compared with other SOTA algorithms, LiteFlex-YOLO performs excellently in small object detection accuracy, model lightweighting, and robustness.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102064"},"PeriodicalIF":3.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Octopus: Knapsack model-driven federated learning client selection in internet of vehicles Octopus：车联网中背包模型驱动的联合学习客户端选择

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-16 DOI: 10.1016/j.pmcj.2025.102063

Ling Xing , Jingjing Cui , Jianping Gao , Kaikai Deng , Honghai Wu , Huahong Ma

{"title":"Octopus: Knapsack model-driven federated learning client selection in internet of vehicles","authors":"Ling Xing , Jingjing Cui , Jianping Gao , Kaikai Deng , Honghai Wu , Huahong Ma","doi":"10.1016/j.pmcj.2025.102063","DOIUrl":"10.1016/j.pmcj.2025.102063","url":null,"abstract":"<div><div>Federated learning (FL), as a distributed way for processing real-time vehicle data, is widely used to improve driving experience and enhance service quality in Internet of Vehicles (IoV). However, considering the data and devices heterogeneity of vehicle nodes, randomly selecting vehicles that are involved in model training would suffer from data skewness, high resource consumption, and low convergence speed. To this end, we propose <span>Octopus</span>, which consists of two components: i) an <em>importance sampling-based local loss computation</em> method is designed to request resource information for each client and apply the importance sampling technique to assess each client’s contribution to the global model’s convergence, followed by utilizing a knapsack model that treats the local loss of each client as the item value, while treating the total system training time as the knapsack capacity to accelerate the client convergence; ii) a <em>knapsack model-based federated learning client selection</em> method is designed to select the client with optimal local loss and maximum model uploading speed to participate in training. In each training round, these clients download and update the model within a predefined time, followed by enabling the selected clients to continue uploading the updated model parameters for assisting the server to efficiently complete the model aggregation. Experimental results show that <span>Octopus</span> improved the model accuracy by 2.64% <span><math><mo>∼</mo></math></span>32.61% with heterogeneous data, and by 1.97% <span><math><mo>∼</mo></math></span>11.74% with device heterogeneity, compared to eight state-of-the-art baselines.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102063"},"PeriodicalIF":3.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Would you mind hiding my malware? Building malicious Android apps with StegoPack 你介意把我的恶意软件藏起来吗？使用StegoPack构建恶意Android应用程序

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-09 DOI: 10.1016/j.pmcj.2025.102060

Danilo Dell’Orco , Giorgio Bernardinetti , Giuseppe Bianchi , Alessio Merlo , Alessandro Pellegrini

{"title":"Would you mind hiding my malware? Building malicious Android apps with StegoPack","authors":"Danilo Dell’Orco , Giorgio Bernardinetti , Giuseppe Bianchi , Alessio Merlo , Alessandro Pellegrini","doi":"10.1016/j.pmcj.2025.102060","DOIUrl":"10.1016/j.pmcj.2025.102060","url":null,"abstract":"<div><div>This paper empirically explores the resilience of the current Android ecosystem against stegomalware, which involves both Java/Kotlin and native code. To this aim, we rely on a methodology that goes beyond traditional approaches by hiding malicious Java code and extending it to encoding and dynamically loading native libraries at runtime. By merging app resources, steganography, and repackaging, the methodology seamlessly embeds malware samples into the assets of a host app, making detection significantly more challenging. We implemented the methodology in a tool, StegoPack, which allows the extraction and execution of the payload at runtime through reverse steganography. We used StegoPack to embed well-known DEX and native malware samples over 14 years into real Android host apps. We then challenged top-notch antivirus engines, which previously had high detection rates on the original malware, to detect the embedded samples. Our results reveal a significant reduction in the number of detections (up to zero in most cases), indicating that current detection techniques, while thorough in analyzing app code, largely disregard app assets, leading us to believe that steganographic adversaries are not even included in the adversary models of most deployed defensive analysis systems. Thus, we propose potential countermeasures for StegoPack to detect steganographic data in the app assets and the dynamic loader used to execute malware.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102060"},"PeriodicalIF":3.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EdgePlantNet: Lightweight edge-aware cyber–physical system for plant disease detection using enhanced attention CNNs EdgePlantNet：轻量级边缘感知网络物理系统，用于植物病害检测，使用增强的关注cnn

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-01 DOI: 10.1016/j.pmcj.2025.102059

Mohammad Zeeshan , Maryam Shojaei Baghini , Ankur Pandey

{"title":"EdgePlantNet: Lightweight edge-aware cyber–physical system for plant disease detection using enhanced attention CNNs","authors":"Mohammad Zeeshan , Maryam Shojaei Baghini , Ankur Pandey","doi":"10.1016/j.pmcj.2025.102059","DOIUrl":"10.1016/j.pmcj.2025.102059","url":null,"abstract":"<div><div>The advances in sensing and computing methodologies have allowed ubiquitous Cyber–Physical Systems (CPS) which have enabled intelligent monitoring and management of crop plants, leading to Smart Agriculture. Yet, the computational constraints of the edge-computing devices have been a roadblock for utilization of complex processing algorithms for real-time applications like leaf-disease detection, were immediate and highly accurate results are of paramount importance. To address this, we propose EdgePlantNet, a Lightweight Edge-Aware CPS for Plant Disease Detection using Enhanced Attention CNNs. It comprises a novel dual-branched Convolutional Neural Network (CNN) architecture that incorporates an improved multi-layered perceptron based spatial attention mechanism (MLP-ATCNN). The MLP-ATCNN is fed with both the original leaf image and its segmented copy, allowing it to simultaneously focus on the leaf image at two scales namely, the diseased regions, and the overall leaf. This allows it to learn robust discriminatory features corresponding to different diseases, even when trained with much lower samples of data. We validate the performance of the EdgePlantNet on two popular and diverse datasets that are the PlantVillage and the BPLD dataset. The novelty of our proposed CPS much lower computational complexity and high disease detection accuracy as compared to other state-of-the-art methods. We also implement the EdgePlantNet on a resource constraint IoT edge device, demonstrating its efficiency for mobile computing.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102059"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning with empirical insights: Leveraging gradient historical experiences for performance fairness 具有经验见解的联合学习：利用梯度历史经验来实现性能公平性

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-01 DOI: 10.1016/j.pmcj.2025.102061

Tongzhijun Zhu , Ying Lin , Yanzhen Qu , Zediao Liu , Yayu Luo , Tenglong Mao , Ziyi Chen

{"title":"Federated learning with empirical insights: Leveraging gradient historical experiences for performance fairness","authors":"Tongzhijun Zhu , Ying Lin , Yanzhen Qu , Zediao Liu , Yayu Luo , Tenglong Mao , Ziyi Chen","doi":"10.1016/j.pmcj.2025.102061","DOIUrl":"10.1016/j.pmcj.2025.102061","url":null,"abstract":"<div><div>Performance fairness has always been a key issue in federated learning (FL), however, the pursuit of performance consistency can lead to a trade-off where the accuracy of well-performing clients is compromised to enhance the accuracy of poor-performing clients. To ensure equitable treatment and unbiased outcomes for all participants in the FL process, we propose FedMH, a fair and fast multi-gradient descent federated learning algorithm with reinforced gradient historical empirical information. We have conducted a theoretical analysis of FedMH from the perspectives of fairness and convergence. Extensive experiments are performed on four federated datasets, revealing significant improvements achieved by FedMH compared to state-of-the-art baselines. Moreover, the experimental findings highlight FedMH’s superior performance in fine-grained classification problems when compared to existing advanced baselines. In brief, the proper utilization of gradient historical empirical information helps improve the effectiveness and fairness of FL, making it more suitable for large-scale and heterogeneous distributed environments.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102061"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trajectory prediction-based migration target selection method for vehicular network services 基于轨迹预测的车辆网络服务迁移目标选择方法

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-01 DOI: 10.1016/j.pmcj.2025.102062

Chuan Ying Peng , Wu Jun Yang , Zhi Xian Chang , Jin Ming Lv , Juan Guo

{"title":"Trajectory prediction-based migration target selection method for vehicular network services","authors":"Chuan Ying Peng , Wu Jun Yang , Zhi Xian Chang , Jin Ming Lv , Juan Guo","doi":"10.1016/j.pmcj.2025.102062","DOIUrl":"10.1016/j.pmcj.2025.102062","url":null,"abstract":"<div><div>In mobile vehicular networks, when edge servers (ES) provide services to high-speed moving vehicles, the problem of service interruption is particularly prominent due to the limitation of service coverage, which seriously affects the continuity and quality of services. To solve this problem, this paper proposes a service migration target selection method based on trajectory prediction. The method first predicts the future movement trajectories of vehicles by the TS-LSTM trajectory prediction model to identify potential activity areas and their associated edge servers; then, the target server selection is optimized using Deep Q-Network (DQN), which jointly incorporate delay and load fairness into the optimization objective function. In addition, pre-replication technology is introduced during the service migration process to ensure that the original servers can continue to provide services during the service switchover, allowing the target servers to seamlessly receive tasks, effectively ensuring service continuity. The experimental results show that, compared with the current state-of-the-art, the proposed method has significant advantages in terms of convergence speed, service delay and service stability: the average end-to-end service delay is reduced by 32% and the service rejection rate is reduced by 28%.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102062"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey of wearable devices to capture human factors for human-robot collaboration 一项可穿戴设备的调查，以捕捉人机协作的人为因素

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-04-21 DOI: 10.1016/j.pmcj.2025.102048

Hooman Sarvghadi , Andreas Reinhardt , Esther A. Semmelhack

{"title":"A survey of wearable devices to capture human factors for human-robot collaboration","authors":"Hooman Sarvghadi , Andreas Reinhardt , Esther A. Semmelhack","doi":"10.1016/j.pmcj.2025.102048","DOIUrl":"10.1016/j.pmcj.2025.102048","url":null,"abstract":"<div><div>Technology has rapidly evolved over the course of the last decades, and drastically transformed our way of life. Robots are no longer just mechanical aides, but have become collaborators on many tasks. Wearable gadgets have become virtually ubiquitous due to their ability to collect data, monitor health parameters, and assist users in various day-to-day tasks. In recent years, there has been a surge in interest around the use of wearable technologies to collect human psychological parameters for human–robot collaboration. With the field of robotics advancing, there is a growing need for robots to interact with humans seamlessly. To achieve this seamless human–robot connection, robots must be able to interpret human emotions and react appropriately. While understanding human emotions and behavior is a complex task in itself, wearable sensor systems contribute valuable insights. This survey provides a comprehensive overview of wearable gadgets and technologies proposed for measuring five key human factors — trust, cognitive workload, stress, safety perception, and fatigue — within the scope of human–robot collaboration, based on the systematic review of papers published between 2015 and the end of 2024 in six major databases. Our analysis indicates that trust and cognitive workload have received greater attention from researchers in recent years, as compared to other human factors. The Empatica E4 wristband, Shimmer3 GSR+ and EPOC X EEG headset are among the most widely used wearable devices, capable of capturing essential physiological parameters widely used for human–robot collaboration, including electrodermal activity, heart rate variability, skin temperature, and electroencephalogram. Besides reviewing the potentials and capabilities of these gadgets, we highlight their shortcomings and offer directions for future research in this domain.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102048"},"PeriodicalIF":3.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrastive-representation IMU-based fitness activity recognition enhanced by bio-impedance sensing 生物阻抗感知增强基于对比表示imu的健身活动识别

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-04-12 DOI: 10.1016/j.pmcj.2025.102047

Mengxi Liu, Vitor Fortes Rey, Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz

{"title":"Contrastive-representation IMU-based fitness activity recognition enhanced by bio-impedance sensing","authors":"Mengxi Liu, Vitor Fortes Rey, Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz","doi":"10.1016/j.pmcj.2025.102047","DOIUrl":"10.1016/j.pmcj.2025.102047","url":null,"abstract":"<div><div>While IMU-based Human Activity Recognition (HAR) has achieved significant success in wearable and pervasive computing areas over the past decade, the potential for further improvement of IMU-based HAR performance through the contrastive representation method enhanced by other sensing modalities remains underexplored. In this work, we propose a contrastive representation learning framework to demonstrate that bio-impedance can enhance IMU-based fitness activity recognition beyond the common sensor fusion method, which requires all sensing modalities to be available during both training and inference phases. Instead, in our proposed framework, only the target sensing modality (IMU) is required at inference time. To evaluate our method, we collected both IMU and bio-impedance sensing data through an experiment involving ten subjects performing six types of upper-body and four kinds of lower-body exercises over five days. The bio-impedance-alone classification model achieved an average Macro F1 score of 75.49% and 71.57% for upper-body and lower-body fitness activities, respectively, which was lower than that of the IMU-alone model (83.10% and 78.61%). However, with our proposed method, significant performance improvement (2.66% for upper-body activities and 3.2% for lower-body activities) was achieved by the IMU-only classification model. This improvement leverages the contrastive representation learning framework, where the information from bio-impedance sensing guides the training procedure of the IMU-only model. The results highlight the potential of contrastive representation learning as a valuable tool for advancing fitness activity recognition, with bio-impedance playing a pivotal role in augmenting the capabilities of IMU-based systems.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102047"},"PeriodicalIF":3.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0