This paper studies the Time-Critical Cooperative Delivery with Unknown Demands (TCDUD) problem, developed from the well-studied Capacitated Vehicle Routing Problem with Stochastic Demands (CVRPSD), that corresponds to emergency situations requiring 1) time-critical delivery; 2) unknown demands; and 3) cooperative delivery. We formulate the problem as a multi-agent sequential decision problem with a Collaborative Semi-Markov Decision Process (CSMDP) model in which timecritical delivery is urged by the reward function that takes both the amount and the time of fulfilled demands into account. Unlike traditional CVRPSD, we do not include a priori information about a customer's demand in problem definition, and require vehicles to visit a customer before its demand is revealed. A Transformer-based multi-agent reinforcement learning approach, namely MAODN, is devised to learn online policies that direct the vehicles to visit the customers, and perform timely delivery in a cooperative manner. MAODN utilizes a demand updater to accommodate online updates about customers' demands from the vehicles during delivery. Vehicles then make cooperative decisions via individual policy networks, leveraging the fleet state provided by the state aggregator. Experiment results suggest that our approach outperforms the baseline by at least 27.8% and demonstrates better robustness.

Resolving issues is a key activity in open-source software (OSS) development, but the ad hoc nature of issue handling on platforms like GitHub makes it challenging to understand the underlying processes of issue resolution, how well such processes are organized, and how to effectively improve issue resolution efficiency. In this work, we quantitatively examine the relationship between issues' organization levels, modeled as process uncertainties, and resolution efficiency measured by issue lifetime and event transition time. We propose an information-theoretical approach to assess the uncertainty lies in issue processing by calculating the entropy of Direct Follow Graphs (DFGs) that represent issue processes. Instead of performing analysis on a project's all issues as a whole, which may lead to over complex models, we find concise yet representative DFG-based models for a project's issues with an entropy-guided KMeans++ clustering algorithm. Findings from real-world OSS projects' issues suggest that higher process uncertainty of issues is associated with longer issue lifetime and extended event transition time. The result also indicates that enhancing issue process organization can potentially improve issue resolution efficiency.

This study addresses the Min-Max Multiple Traveling Salesmen Problem (m3-TSP), which aims to coordinate tours for multiple salesmen such that the length of the longest tour is minimized. Due to its NP-hard nature, exact solvers become impractical under the assumption that P ≠ NP. As a result, learning-based approaches have gained traction for their ability to rapidly generate high-quality approximate solutions. Among these, two-stage methods combine learning-based components with classical solvers, simplifying the learning objective. However, this decoupling often disrupts consistent optimization, potentially degrading solution quality. To address this issue, we propose a novel two-stage framework named Generate-and-Split (GaS), which integrates reinforcement learning (RL) with an optimal splitting algorithm in a joint training process. The splitting algorithm offers near-linear scalability with respect to the number of cities and guarantees optimal splitting in Euclidean space for any given path. To facilitate the joint optimization of the RL component with the algorithm, we adopt an LSTM-enhanced model architecture to address partial observability. Extensive experiments show that the proposed GaS framework significantly outperforms existing learning-based approaches in both solution quality and transferability.

Semi-supervised medical image segmentation (SS-MIS) shows promise in reducing reliance on scarce labeled medical data. However, SSMIS field confronts challenges such as distribution mismatches between labeled and unlabeled data, artificial perturbations causing training biases, and inadequate use of raw image information, especially low-frequency (LF) and high-frequency (HF) components. To address these challenges, we propose a Wavelet Transform based Bidirectional Copy-Paste SSMIS framework, named WT-BCP, which improves upon the Mean Teacher approach. Our method enhances unlabeled data understanding by copying random crops between labeled and unlabeled images and employs WT to extract LF and HF details. We propose a multi-input and multi-output model named XNet-Plus, to receive the fused information after WT. Moreover, consistency training among multiple outputs helps to mitigate learning biases introduced by artificial perturbations. During consistency training, the mixed images resulting from WT are fed into both models, with the student model’s output being supervised by pseudo-labels and ground-truth. Extensive experiments conducted on 2D and 3D datasets confirm the effectiveness of our model. Code: https://github.com/simzhangbest/WT-BCP.

Resolving issue reports is essential to the development of opensource software, which helps developers collaborate, communicate, and fix bugs, thereby ensuring the quality and functionality of the software.While previous studies focus on the static factors that impact the issue resolution time, few of them investigate the issue resolution patterns.In this paper, we propose an approach based on discriminative sequential pattern mining to discover the patterns in issue resolution processes with different efficiency.We devise an issue resolution process modeling method, which adds events' time interval values to the activity sequences, thus providing a richer understanding of the issue resolution.And we find that there are several typical patterns in the issue resolution process with different lifetimes.To guide developers in prioritizing issues and allocating resources, we propose a prediction model and a recommendation approach based on the mined patterns.We extract features from patterns and construct random forest classifier models to predict the resolution speed of issues (fast, normal or slow).The accuracy of our models reaches 70% or higher in most repositories and mixed repositories' data, indicating a great performance.Finally, we recommend actions to users participating in ongoing issues at an early stage based on the mined patterns, thus improving the efficiency of issue resolution process.

Third-party libraries (TPLs) play a critical role in modern software development by providing reusable code that accelerates project development. However, the vast number of TPLs available makes selecting the appropriate library for a given task or finding replacements for deprecated libraries a challenging task. Existing methods are limited, relying only on miningbased approaches or feature-based solutions. In this study, we propose an innovative attributed multiplex learning approach that combines both textual and relational data across multiple layers to perform effective analogical library recommendation and retrieval. By representing libraries as nodes with attributes and modeling cross-library relationships as graph edges, our method constructs an attributed multiplex network for TPL representation embeddings. Our approach uses a unified, concise model to include different aspects of information. The proposed inductive model can also address cold-start issues. Moreover, our model is scalable and can adapt to a large number of libraries. To validate our approach, we conduct experiments including an ablation study within the NPM ecosystem. By using a ground-truth data set of 8,308 libraries, the results demonstrate a recommendation precision of 89.8 % at Hit@10. Additionally, we contribute a new data set extracted from deprecation messages containing 4,070 migration rules, enriching the relatively small existing data sets in the NPM ecosystem. In summary, our approach is efficient and promising for supporting real-world, large-scale TPL recommendation and retrieval.

In this work, we propose the EarlyPR framework that identifies and predicts potential pull-request (PR) contri-butions from an open source software (OSS) project's forks, which can potentially improve the efficiency of the fork-and-pull based development in OSS projects by supporting early warning of duplicated and rejected contributions, and detection of lost contributions. Unlike traditional, PR-based studies that rely on the descriptions and contents of PRs provided by their creators, which are only available after the PRs are created, EarlyPR makes predictions before the creation of PRs by mining the forks' commit history. EarlyPR's task is challenging because of the explosive number of commit subsets in a fork's commit history that may form PRs, and the absence of resulting, real PR-related information. To tackle the challenges, we adopt the state-of-the-art, Transformer-based architecture to extract rich statistical and content information from the forks and their commits to support the prediction of potential PR contributions. And to make the algorithms scalable, we devise a TemporalFilter to find candidate PRs by mimicking the real-world processes of picking subsets of commits from a fork's commit history when creating PRs. Experimental results on real-world OSS project data suggest that EarlyPR is effective in predicting PRs, which are essentially sets of commits selected from forks to compose these PRs. Experimental results obtained using real-world OSS projects' and their forks' data suggest that EarlyPR is effective by achieving a hitting rate of 0.790 and a missing rate of 0.367 by matching the predicted and real PRs under a stringent criterion of IoU > 0.5. We further demonstrate that we can forecast the merging of PRs based on EarlyPR's predictions with an accuracy of 70.8%. In summary, the proposed approach can potentially improve the efficiency of the fork-and-pull based OSS development by making accurate and early predictions of PR contributions from the distributed, and often independently, developed forks.

This work presents four novel entropy-based indices for measuring the community evolution of developer social networks (DSNs) in open source software (OSS) projects. The proposed indices offer a quantitative measure of community split, shrink, merge, and expand events. The indices have proven properties like monotonicity, and they have defined maximum and minimum values that signify meaningful scenarios. These indices can be combined to describe complex community evolution events such as emergence and extinction. Expanding upon these indices, this research proposes a novel machine learning approach, leveraging shapelet mining, to unearth representative patterns of community evolution. The results from real-world OSS projects show that these indices effectively capture various community evolution behaviors with a 94.1% accuracy compared to existing work. They also predict OSS team productivity with a 0.718 accuracy. With the shapelet mining and learning framework, the indices can identify patterns of community evolution and predict the survival of OSS projects with 93% accuracy 3 months before the projects’ last observed commits. The findings highlight the potential of these entropy-based indices for understanding OSS project status and predicting future trends, which are valuable for supporting future research on DSNs and OSS communities.

On open source software (OSS) platforms such as GitHub, forking and accepting pull-requests is an important approach for OSS projects to receive contributions, especially from external contributors who cannot directly commit into the source repositories. Having a large number of forks is often considered as an indicator of a project being popular. While extensive studies have been conducted to understand the reasons of forking, communications between forks, features and impacts of forks, there are few quantitative measures that can provide a simple yet informative way to gain insights about an OSS project's forks besides their count. Inspired by studies on biodiversity and OSS team diversity, in this paper, we propose an approach to measure the diversity of an OSS project's forks (i.e., its fork population). We devise a novel fork entropy metric based on Rao's quadratic entropy to measure such diversity according to the forks' modifications to project files. With properties including symmetry, continuity, and monotonicity, the proposed fork entropy metric is effective in quantifying the diversity of a project's fork population. To further examine the usefulness of the proposed metric, we conduct empirical studies with data retrieved from fifty projects on GitHub. We observe significant correlations between a project's fork entropy and different outcome variables including the project's external productivity measured by the number of external contributors' commits, acceptance rate of external contributors' pull-requests, and the number of reported bugs. We also observe significant interactions between fork entropy and other factors such as the number of forks. The results suggest that fork entropy effectively enriches our understanding of OSS projects' forks beyond the simple number of forks, and can potentially support further research and applications.

Studies on the OSS communities have shown that financial supports are critical to OSS developers and projects to maintain their progress and sustainability. However, there were few developers being paid directly for maintaining OSS projects in the past. The GitHub Sponsors program that brings financial supports to the general OSS developers in GitHub-the world's largest OSS platform may make a difference on this situation in the future. In this paper, we present a data set on GitHub Sponsors and conduct a data-driven study to analyze the participants of the program and the impact of sponsorships to developers' activities and their projects' outcomes and qualities. The results of our survey suggest that most developers state they will contribute more with sponsorships and provide some privilege for their sponsors. And through quantitative study, we find that developers make more contributions on GitHub after they got/offered sponsorships. Moreover, gaining sponsorship also has a weakly positive impact on developers' collaborators that did not get sponsorship. And not only developers, but their own or contributed projects also can be motivate by sponsorships. Our findings are useful to the community by understanding the impact of sponsorships on users' activities and projects' progress and sustainability, and helping the managers to improve the current financial support mechanism.

Existing studies in both psychology and software engineering have shown the importance of emotions in complex learning and programming tasks. For students who are learning to program, rich emotions are experienced which can provide valuable feedback to their teachers. To accurately model stu-dents' emotions, this paper adopts the well-recognized model of emotions during complex learning that involves four states: engaged, confused, frustrated, and bored. To perform continuous estimation of students' emotions in a non-intrusive manner, this paper proposes to track students' programming behavior and estimate their corresponding emotional states. Compare to the existing approaches on acquiring the students' emotional states with self-reports or bio-sensors, the proposed approach is more feasible in conducting real-world, and large-scale studies for not requiring extensive human interventions or additional devices. Evaluated using data collected from a real-world course project, the proposed approach is showed to be promising for achieving an estimation accuracy of 72.06 % for the above four emotional states. As an enabling technology, the proposed is potentially useful in supporting many applications and improve the quality of programming education in computer science.

As human beings take actions with a goal in mind, we could predict the following action of a person depending on his previous actions. Inspired by this, after collecting and analyzing more than 13,000 repositories with 441,290 Python source code files from the Internet, we find the actions expressed in code are in the developers’ high-level programming language statements.

Understanding the evolution of communities in developer social networks (DSNs) around open source software (OSS) projects can provide valuable insights about the socio-technical process of OSS development. Existing studies show the evolutionary behaviors of social communities can effectively be described using patterns including split, shrink, merge, expand, emerge, and extinct. However, existing pattern-based approaches are limited in supporting quantitative analysis, and are potentially problematic for using the patterns in a mutually exclusive manner when describing community evolution. In this work, we propose that different patterns can occur simultaneously between every pair of communities during the evolution, just in different degrees. Four entropy-based indices are devised to measure the degree of community split, shrink, merge, and expand, respectively, which can provide a comprehensive and quantitative measure of community evolution in DSNs. The indices have properties desirable to quantify community evolution including monotonicity, and bounded maximum and minimum values that correspond to meaningful cases. They can also be combined to describe more patterns such as community emerge and extinct. We conduct studies with real-world OSS projects to evaluate the validity of the proposed indices. The results suggest the proposed indices can effectively capture community evolution, and are consistent with existing approaches in detecting evolution patterns in DSNs with an accuracy of 94.1%. The results also show that the indices are useful in predicting OSS team productivity with an accuracy of 0.718. In summary, the proposed approach is among the first to quantify the degree of community evolution with respect to different patterns, which is promising in supporting future research and applications about DSNs and OSS development.

Bowel sounds are the sounds produced by intestinal peristalsis and can reflect intestinal activities. There is a long history of using bowel sounds as indicators for intestinal health in clinical practices, making bowel sound detection potentially useful as a key enabling technology for health care applications. Traditionally, bowel sounds are detected manually by medical staff. However, manual inspections are time consuming and prone to personal differences. In modern hospitals, advanced medical devices are used to detect the patients’ intestinal activities with rich sensing modalities. However, these devices are often invasive, expensive and large in size, making them only used in limited, critical scenarios. To facilitate bowel sound detection and further intestinal activity analysis in daily living, this paper presents a wearable system to capture and recognize users’ bowel sounds and other types of body sounds without restricting their daily activities. We propose to use dual-channel, stethoscope augmented microphones to capture users’ body sounds. With the captured audio stream, we extract both time- and frequency-domain features to represent signals in each frame. Beyond traditional audio features such as energy and MFCC, we further extract dual-channel features to augment the information obtained from a frame. We evaluate the system’s performance with four subjects in real-world settings, the results suggest our system is effective for achieving a detection accuracy of 85.7%.

Recovering missing values in time series is critical when performing time series analysis. And the blackouts issue studied in this paper, described as losing all the data during a certain period, is among the most urgent issues due to its devastating impact on service quality, and is challenging because of the absence of coevolving data sequences for reference. As a result, many existing approaches that rely on data from other coevolving sequences for missing value recovery are infeasible in handling blackouts. To address the issue, this work proposes a novel Hankel matrix factorization approach, HKMF-T, to recover missing values during blackouts for tagged time series, where a tagged time series consists of a data sequence and a corresponding tag sequence. Motivated by real-world observations, HKMF-T decomposes the data sequence into two components: 1) an internal, slowly-varying smooth trend, and 2) external impacts indicated by the tag sequence. By transforming a partially observed data sequence into a corresponding Hankel matrix, we learn the above two components and estimate the missing values under a unified framework of Hankel matrix factorization. Extensive experiments are conducted to evaluate the practical performance of HKMF-T on real-world data sets. And the results suggest HKMF-T outperforms the baseline approaches for blackouts with long durations.

The quality of software products is closely related to software developers. A good psychological state can improve the quality of software products to a certain extent. More and more researchers are beginning to study how to effectively identify the psychological state of developers to help their work. However, many of these works have a significant intrusion into developers and are not suitable for long-term deployment in real enterprises. In this paper, we present a tool for IDE-based programming activity data collection for software developers. A conceptual model is presented for raw data collection and hierarchical concept encapsulation. We implement this tool in the form of an IDE plug-in for popular IDEs including IntelliJ IDEA and Android Studio, which can collect and process developers' in-IDE activity data in a non-intrusive and privacy-preserving manner. To demonstrate its usefulness, we deploy the proposed tool in a real enterprise environment. Based on this tool and the data collected in the real enterprise environment, we build applications including programming activity statistics and flow state recognition. The experiment results suggest the proposed approach can effectively capture the detailed programming activities and perform flow state recognition. The recognition accuracy is 80.14%.

Flow is a holistic description of people's optimal experiences during creative activities that can be characterized as being totally concentrated on, and actively involved in the task, enjoying the process of creation, and achieving a balance between one's skill and the task's challenge. Understanding software developers' flow states has attracted an increasing attention in both research and practice because of the strong link between being in flow and achieving good performance. In this paper, we study the problem of tracking and recognizing developers' flow states by tracing their computer interactions including activities of using the keyboard, mouse, IDE functions, and switching application windows. Compared to the traditional approaches that rely on self-reports or wearable sensors, a major advantage of the proposed approach is being non-invasive for not requiring any additional efforts from the developers after the training phase is completed, which is important because the developers' flow states can easily be interrupted by external interferences. Based on the captured interaction traces, we represent the developers' activities with extensive features, and propose to address the flow state recognition problem using machine learning technologies. And a hierarchical recognition model is built following the multi-dimensional construct of the flow concept, which is interpretable and effective. We develop a prototype system and conduct a 17-day field study in a medium-sized IT company in China to collect real-world data. The results show that our approach is effective by achieving the highest recognition accuracy of 92.6%, and efficient for performing real-time recognition.

Recovering missing values in time series is critical when performing time series analysis. And the blackouts issue studied in this paper, described as losing all the data during a certain period, is among the most urgent and challenging issues. While the existing approaches for missing value recovery in time series could not handle this issue properly, in this work, we proposes a Hankel matrix factorization-based approach for tagged time series called HKMF-T, following the idea of decomposing a data sequence into the smooth trend and the external impact components. By transforming the data sequence into its Hankel matrix form, HKMF-T models the smooth trend implied by high-order temporal correlations as the product of two low-rank matrices, and learns the external impacts indicated by a corresponding tag sequence. Through extensive experiments conducted on three real-world data sets, HKMF-T shows its effectiveness by outperforming all baseline methods for blackouts with durations longer than nine sampling intervals.

This paper presents the CARMUS framework for continuous activity recognition with missing values on smartphones. Besides the power and resource constraints discussed in existing work, our framework is proposed to further tackle the critical issue of missing values during data collection. We demonstrate the issue's impact on continuous recognition through a motivating example, and specify two challenges-blackouts and resource constraints-with respect to smartphone-based sensing and processing platforms. To address the challenges, CARMUS provides a novel framework which involves a light-weight admission control unit and a data imputation unit intuited by the daily repeated pattern and temporal smoothness of human activity data. Based on extensive experiments conducted on a real-world data set with 37% of the data missing, we show that the CARMUS framework is effective for achieving an 85.5% recognition accuracy by adopting the state-of-the-art imputation algorithms.

Understanding the relevance between the Free/Libra Open Source Software projects is important for developers to perform code and design reuse, discover and develop new features, keep their projects up-to-date, and etc. However, it is challenging to perform relevance ratings between the FLOSS projects mainly because: 1) beyond simple code similarity, there are complex aspects considered when measuring the relevance; and 2) the prohibitive large amount of FLOSS projects available. To address the problem, in this paper, we propose a method to measure and further predict the relevance ratings between FLOSS projects. Our method uses topic features extracted by the LDA topic model to describe the characteristics of a project. By using the topic features, multiple aspects of FLOSS projects such as the application domain, technology used, and programming language are extracted and further used to measure and predict their relevance ratings. Based on the topic features, our method uses matrix factorization to leverage the partially known relevance ratings between the projects to learn the mapping between different topic features to the relevance ratings. Finally, our method combines the topic modeling and matrix factorization technologies to predict the relevance ratings between software projects without human intervention, which is scalable to a large amount of projects. We evaluate the performance of the proposed method by applying our topic extraction and relevance modeling methods using 300 projects from GitHub. The result of topic extraction experiment shows that, for topic modeling, our LDA-based approach achieves the highest hit rate of 98.3% and the highest average accuracy of 29.8%. And the relevance modeling experiment shows that our relevance modeling approach achieves the minimum average predict error of 0.093, suggesting the effectiveness of applying the proposed method on real-world data sets.

In this paper, we study the problem of pedestrian relative positioning with respect to their walking direction. Existing approaches are mainly based on trajectory information or device proximity detection, and they highly rely on infrastructure or specialized device support. Importantly, most work does not provide relative position information with respect to people's walking direction. To address the above issues, we propose a direction-aware, audio-based solution that only uses daily wearable devices. Based on the fact that pedestrian's arms often swing back and forth during walking, we develop the wrist-body model that formally models the distance change between a user's wrist and his/her walking mate's body when walking together. Based on this model, we design our system by attaching the audio sources to a user's wrists and an audio receiver to the other user's body. We develop key indicators that characterize the received audio signal's Doppler shift induced by arm swing motions and the differences in signal strength. We further propose methods such as cycle segmentation and aggregation to deal with several real-world challenges. The performance of our approach is studied through extensive experiments. Evaluation conducted using real-world data suggests the prototype system achieves 85.9% positioning accuracy, demonstrating its effectiveness.

Elderly care is one of the many applications supported by real-time activity recognition systems. Traditional approaches use cameras, body sensor networks, or radio patterns from various sources for activity recognition. However, these approaches are limited due to ease-of-use, coverage, or privacy preserving issues. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system aims at providing an easy-to-use solution with high detection coverage. Our system uses passive tags which are maintenance-free and can be embedded into the clothes to reduce the wearing and maintenance efforts. A small RFID reader is also worn on the user's body to extend the detection coverage as the user moves. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address the issues of false negative of tag readings and tag/antenna calibration, and design a fast online recognition system. Antenna and tag selection is done automatically to explore the minimum number of devices required to achieve target accuracy. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6 percent with a latency of 5 seconds. Additionally, we show that the system only requires two antennas and four tagged body parts to achieve a high recognition accuracy of 85 percent.

Smoking behavior detection has attracted much research interest for its significant impact on smokers' physical and mental health. Existing research has shown the potential of using wearable devices for fine-grained smoking puff and session detection by detecting a smoker's content of breathing, lighter usage, breathing, and gesture patterns. However, the existing systems are complex, and they are usually vulnerable to confounding activities and diversity of smoking behavior. To address these limitations, this paper proposes the design and implementation of a simple and compact smart neckband device for smoking detection. The device is equipped with both passive and active acoustic sensors to detect smoking sessions and puffs. We propose a hierarchical processing framework in which the lower-layer detects the sub-movements, i.e., lighter usage, hand-to-mouth gesture and deep breathing, from perceived audio data; and the higher-layer, based on the lower-layerąŕs detection results, detects smoking puffs and sessions using temporal sequence analysis techniques. Real-world experiments suggest our system can accurately detect smoking puffs and sessions with F1 score of respectively 93.59% and 92.96% in complex environments with the presence of confounding activities and diverse ways of smoking.

Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing due to its potential in many applications, such as assistive living and healthcare. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved or concurrent) manner in real life. Little work has been done in addressing complex issues in such a situation. The existing models of interleaved and concurrent activities are typically learning-based. Such models lack of flexibility in real life because activities can be interleaved and performed concurrently in many different ways. In this paper, we propose a novel pattern mining approach to recognize sequential, interleaved, and concurrent activities in a unified framework. We exploit Emerging Pattern-a discriminative pattern that describes significant changes between classes of data-to identify sensor features for classifying activities. Different from existing learning-based approaches which require different training data sets for building activity models, our activity models are built upon the sequential activity trace only and can be applied to recognize both simple and complex activities. We conduct our empirical studies by collecting real-world traces, evaluating the performance of our algorithm, and comparing our algorithm with static and temporal models. Our results demonstrate that, with a time slice of 15 seconds, we achieve an accuracy of 90.96 percent for sequential activity, 88.1 percent for interleaved activity, and 82.53 percent for concurrent activity.

The advances of wireless networking and sensor technology open up an interesting opportunity to infer human activities in a smart home environment. Existing work in this paradigm focuses mainly on recognizing activities of single user. In this work, we focus on the fundamental problem of recognizing activities of multiple users using a wireless body sensor network, and propose a scalable pattern mining approach to recognize both single- and multiuser activities in a unified framework. We exploit Emerging Pattern-a discriminative knowledge pattern which describes significant changes among activity classes of data-for building activity models and design a scalable, noise-resistant, Emerging Pattern-based Multiuser Activity Recognizer (epMAR) to recognize both single- and multiuser activities. We develop a multimodal, wireless body sensor network for collecting real-world traces in a smart home environment, and conduct comprehensive empirical studies to evaluate our system. Results show that epMAR outperforms existing schemes in terms of accuracy, scalability, and robustness.

With the rapid advances of wearable sensors and wireless networks. There is a growing research interest in building Body Area Sensor Network (BSN) systems to support applications such as human activity recognition and daily health monitoring. The main challenges of building a BSN system include: First, the wearable sensor nodes are highly constrained in resources; Second, the sensor nodes are prong to failures; Third, the applications requires the sensors to work with high sampling rate which results in the heavy load on each sensor node. Beside the technical challenges, the usability of the system is also a critical issue when deployed into the the real-world environment.

Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a real time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast, lightweight template matching algorithm to detect gestures at the sensor node level, and then use a discriminative pattern based real-time algorithm to recognize high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average precision of 94.9%, an average recall of 82.5%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network communication cost by 60.2%.

Understanding and recognizing human activities from sensor readings is an important task in pervasive computing. Existing work on activity recognition mainly focuses on recognizing activities for a single user in a smart home environment. However, in real life, there are often multiple inhabitants l

Understanding and recognizing human activities from sensor readings is an important task in pervasive computing. In this paper, we investigate the fundamental problem of recognizing activities for multiple users from sensor readings in a home environment, and propose a novel pattern mining approach