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Multi-modality physiological signal-based emotion recognition has attracted increasing attention as its capacity to capture human affective states comprehensively. Due to multi-modality heterogeneity and cross-subject divergence, practical applications ...

Federated learning (FL) enables collaborative learning across multiple biomedical data silos with multimodal foundation models while preserving privacy. Due to the heterogeneity in data processing and collection methodologies across diverse medical ...

Federated multi-task learning (FMTL) has emerged as a promising framework for learning multiple tasks simultaneously with client-aware personalized models. While the majority of studies have focused on dealing with the non-independent and ...

Model fusion is becoming a crucial component in the context of model-as-a-service scenarios, enabling the delivery of high-quality model services to local users. However, this approach introduces privacy risks and imposes certain limitations on its ...

Physiological signal based ubiquitous computing has garnered significant attention. However, the heterogeneity among multimodal physiological signals poses a critical challenge to practical applications. To traverse this heterogeneity gap, recent studies ...

Human activity recognition requires the efforts to build a generalizable model using the training datasets with the hope to achieve good performance in test datasets. However, in real applications, the training and testing datasets may have totally ...

As an enabling technology for edge computing scenarios, indoor localization has a broad prospect in a variety of location-based applications, such as tracking, navigating, and monitoring in indoor environments. In order to improve the location ...$$$$

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental disorders affecting children. Because the etiology of ADHD is complex and its symptoms are not specific, there is a lack of feasible quantitative diagnostic methods. ...

Recently, the problem of indoor localization based on WLAN signals is attracting increasing attention due to the development of mobile devices and the widespread construction of networks. However, no definitive solution for achieving a low-cost and ...

People with autism are at risk for social isolation due to differences in their perception and engagement with the social world. In this work, we aim to address one specific concern related to socialization the understanding, awareness, and use of ...

Activities of Daily Living (ADL) recognition through wearable devices is an emerging research field. While, for many applications, recognition methods are faced with simultaneously dynamic changes in feature dimension, activity class and data ...

In this paper, we describe the results of a controlled experiment measuring everyday movement activity through a novel recognition prototype named AIR. AIR measures distance from the feet using infrared (IR) sensors. We tested this approach for ...

People with autism are at risk for social isolation due to the differences they experience in how they perceive and engage with the social world. In this work, we aim to address one specific concern related to socialization---the understanding, ...

Wi-Fi-based indoor localization with high capability and feasibility needs to implement lifelong online learning mechanism. However, the characteristic of Wi-Fi is wide variability, which lies in not only the fluctuation of signal strength value, but ...

Along with the proliferation of mobile devices and wireless signal coverage, indoor localization based on Wi-Fi gets great popularity. Fingerprint based method is the mainstream approach for Wi-Fi indoor localization, for it can achieve high ...

Benefit from widely used Bluetooth sensor, user surrounding contexts can be availably recognized leveraging Bluetooth data. Most existing studies seldom deal with newly extended surrounding contexts which results in degrading the recognition performance,...

Indoor localization based on Wi-Fi is crucial for many practical applications. However, considered the highly dynamic indoor environment, Wi-Fi indoor localization system cannot maintain the high performance for longtime. To address this challenge, we ...

User-centric ambient social contexts can be effectively captured by dynamic bluetooth data. However, conventional approaches for training classifiers struggle with social contexts that are incrementally constructed and continuously discovered in ...

There are two longstanding issues: the fluctuation of wireless signal and the unstability of Access Point (AP), which greatly affect the performance of Wi-Fi based indoor localization. Most existing fingerprint based Wi-Fi localization methods adopt ...

For handling data and training model, existing machine learning methods do not take timeliness problem into consideration. Timeliness here means the data distribution or the data trend changes with time passing by. Based on timeliness management scheme, ...