In recent years, machine learning (ML) and deep learning (DL) have emerged as key technologies for balancing energy efficiency and thermal comfort in Internet of Things (IoT) based Smart Buildings (SBs). Among the various components of SBs, the heating, ventilation, and air conditioning (HVAC) system plays a critical role, as it significantly influences both energy consumption and occupant comfort. In this context, accurately predicting indoor temperatures is essential for optimizing HVAC operations, resulting in enhanced energy efficiency, improved comfort, and lower energy costs. To address this challenge, this paper proposes Advanced Temperature Management of Smart Building (ATM-SB), a hybrid DL approach that combines Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks to predict indoor temperature at 15, 30, and 60 minute intervals using multivariate sensor data. Using the ATM-SB approach, a prototype was developed at AEI S.r.l., Italy. It was trained and validated on a real-world dataset collected from a smart office building and laboratory across both summer and winter seasons, and further tested on four public datasets from diverse environments. ATM-SB achieves competitive performance and demonstrates robust generalization across diverse real world and public datasets, including comparisons with LSTM, GRU, CNN-LSTM, and gradient boosting, achieving an MAE as low as 0.0098 and an R 2 of up to 0.98 on real data. Statistical validation through 5-fold cross-validation and significance testing confirms the robustness and generalization of the model. By enabling accurate forecasting across diverse scenarios, ATM-SB provides an effective solution for intelligent HVAC control in next-generation SBs.

The Internet of Medical Things (IoMT) has emerged as a transformative technology platform in the healthcare sector, enabling real-time monitoring and intelligent decision-making through connected devices. However, prioritizing and offloading the massive volume of computational tasks generated by IoMT devices while minimizing latency and energy consumption poses significant challenges. Existing approaches often overlook dynamic real-time factors such as task urgency and data freshness, as well as the integration of local task processing via Device-to-Device (D2D) communication with offloading to Mobile Edge Computing (MEC) servers. In this paper, we develop a priority- and Age of Information (AoI)-Aware task offloading framework for latency and energy optimization in healthcare IoT systems, namely PRALEIT, exploiting Mixed Integer Linear Programming (MILP) problem. The developed PRALEIT system introduced probabilistic classification of IoMT tasks based on vital signs and AoI value by leveraging a Bayesian classifier. The experimental results depict that the PRALEIT system significantly reduces task execution delay and energy consumption compared to state-of-the-art models, ensuring reliable and sustainable healthcare services.

Visually Impaired People (VIP) face various challenges in their daily life, especially while they navigate in outdoor environments. Portable and user-friendly devices would facilitate their independent living. In this work, we designed a wearable camera-based system to help VIP to detect and navigate on blind sidewalks. We proposed an adaptive image mask selection method to automatically segment the blind sidewalk; a walk deviation method is used to help the VIP navigating on such tactile paving. We implemented the algorithm on a Raspberry Pi to make the system standalone and cost-effective. The performance are evaluated in real-life scenarios and our results prove better recognition accuracy and computation efficiency. Experiment results showed that for each video frame our algorithm’s average execution time is as low as 49.5 ms, while for the image mask renewal step is 50.4 ms.

Chronic stress significantly undermines cognitive function and well-being, yet its reliable detection remains elusive due to inter-individual psychobehavioral heterogeneity. To the best of our knowledge, this study is the first to introduce a novel multimodal framework synergizing psychometric traits (conscientiousness, neuroticism) and 64-channel electroencephalography (EEG) for precision-driven chronic stress classification. We collected psychophysiological data from 21 subjects, including Big Five personality inventories, Perceived Stress Scale (PSS) scores, and EEG recordings. Statistical analysis revealed a strong inverse correlation between conscientiousness and perceived stress (r = -0.59), while neuroticism showed a weaker positive association (r = 0.12). Power spectral density (PSD) features from artifact-corrected EEG were fused with trait metrics to train SVM and KNN classifiers. The proposed framework achieved state-of-the-art accuracy (SVM: 94.7%; KNN: 89.4%), with neuroticism emerging as a critical predictor alongside beta/gamma-band spectral markers—a novel finding underscoring its role in stress pathophysiology. This work pioneers the integration of personality-aware analytics with neurophysiological biomarkers for stress phenotyping, establishing a new paradigm for personalized mental healthcare. By demonstrating the feasibility of trait-guided machine learning, our contributions advance scalable, individualized interventions, addressing a critical gap in precision psychiatry. These results lay the groundwork for adaptive digital health systems that leverage multimodal data to mitigate chronic stress and enhance quality of life.

Task failures in decentralized Internet of Things (IoT)-edge computing environments not only lead to inefficiencies, increased latency, and resource wastage but can also introduce system instability and cause application malfunctions. These failures may arise due to network disruptions, resource constraints, or inefficient task scheduling, ultimately affecting the overall reliability and performance of IoT-edge systems. This study presents a novel Long Short-Term Memory (LSTM)-based Federated Learning (FL) framework for proactive task failure prediction, ensuring adaptive scheduling and efficient resource utilization. Unlike existing conventional methods, our approach personalizes failure prediction per device, addressing heterogeneous execution characteristics while preserving data privacy. By integrating LSTM with FL, we improve the failure detection accuracy and reduce unnecessary task executions. We first trained all models using Federated Learning (FL) and then conducted a comparative analysis of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and LSTM. Our findings show that LSTM achieves the highest accuracy and F1 score, while CNN excels in recall and energy efficiency. These insights validate the effectiveness of our FL-based failure prediction framework and highlight the advantages of model personalization for dynamic decentralized IoT-edge environments.

Vehicular Edge Computing (VEC) is a key enabler of real-time intelligence in next-generation transportation systems. However, conventional Federated Learning (FL) in VEC typically depends on static edge-server aggregation, resulting in high communication overhead, increased latency, and poor responsiveness under dynamic mobility. To overcome these challenges, we propose Proximity-Aware Federated Learning (PA-FL), a decentralized framework that integrates vehicle-to-vehicle (V2V) collaboration and edge-assisted synchronization to enhance learning efficiency, scalability, and robustness. PA-FL introduces three core innovations: (i) Collaborative Local Aggregation, where vehicles perform proximity-based model fusion before forwarding updates to the edge, reducing uplink traffic and accelerating convergence; (ii) Adaptive Neighbor Selection, which dynamically filters peers based on spatiotemporal proximity and link stability to ensure context-relevant learning; and (iii) Context-Aware Synchronization, which adjusts aggregation frequency based on vehicular density and mobility to improve energy efficiency and learning consistency. Extensive experiments demonstrate that PA-FL achieves an average accuracy of 87.08% ± 0.49, surpassing state-of-the-art FL baselines by over 13% in accuracy and 11% in F1 score. It reduces task failure rates across all proximity ranges and lowers per-round energy consumption to 0.038 J, achieving a 6× improvement in communication efficiency. Delay per communication round is also reduced to 0.85 seconds, supporting real-time responsiveness. These results validate PA-FL as a resilient and scalable framework for symbiotic FL where vehicles collaboratively learn from local context while contributing to global intelligence in AI-integrated, 6G-enabled vehicular edge environments.

This paper introduces a novel Compressive Sensing (CS)-based cryptosystem tailored for the secure and efficient transmission of Electrocardiogram (ECG) signals in Internet of Medical Things (IoMT) environments. It leverages the inherent sparsity of ECG signals in the wavelet domain and ensures both data privacy and integrity during transmission through a simple but effective additional encryption stage. We have evaluated the performance of four distinct sensing matrices and three reconstruction algorithms across multiple wavelet families. Through extensive simulations using the MIT-BIH Arrhythmia Database we demonstrate that the Low-Density Parity-Check matrix combined with the L1 optimization algorithm achieves the highest Quality Score, with Compression Ratio up 50%. The proposed approach also shows an excellent ability in preserving important pathological features in presence of abnormal beats. The proposed CS encoder has been hardware implemented on low-resource microcontroller and FPGA devices. When realized on a Xilinx Artix 7 XC7A12 T FPGA, such a prototype allows real-time operations to be sustained running at 1 MHz clock frequency and dissipating only 0.8nJ per sample.

An important challenge in edge computing service management is maintaining good quality of service and low latency for end-users. Edge services must be hosted near the end user, necessitating sophisticated management of virtualized resources in the edge infrastructure. In the context of edge computing, users' location and mobility patterns are essential components of resource allocation and service transfer. As a result, it is of the utmost importance to assess the effectiveness of the proposed management solutions in a city environment with realistic mobility. The purpose of this study is to investigate the simulation of edge computing and to present an integrated solution environment that makes use of two validated simulators for the modeling of urban mobility and edge computing services.

The current surge in AI trends has catalyzed a strong inclination among organizations to transition towards AI-driven solutions. However, a significant challenge arises from the prevalent monolithic nature of existing applications, which often impedes scalability and limits the potential for enhancement through agent-based interventions. This paper aims to investigate strategies for transitioning from monolithic applications to microservices-based architectures and explore the utilization of agents for control within microservices environments. Subsequently, drawing from existing literature and our own insights, we endeavor to formulate a comprehensive strategy for transforming original monolithic applications into intelligently controlled microservices-based systems. We conclude with an IoT use case in order to illustrate the application of this strategy and highlight the advantages that can be achieved.

The healthcare industry faces challenges due to rising treatment costs, an aging population, and limited medical resources. Remote monitoring technology offers a promising solution to these issues. This article introduces an innovative adaptive method that deploys an ultrawideband (UWB) radar-based Internet of Medical Things (IoMT) system to remotely monitor elderly individuals’ vital signs and fall events during their daily routines. The system employs edge computing for prioritizing critical tasks and a combined cloud infrastructure for further processing and storage. This approach enables monitoring and telehealth services for elderly individuals. A case study demonstrates the system’s effectiveness in accurately recognizing high-risk conditions and abnormal activities, such as sleep apnea and falls. The experimental results show that the proposed system achieved high accuracy levels, with a mean absolute error (MAE) ± standard deviation of absolute error (SDAE) of 1.23± 1.16 bpm for heart rate (HR) detection and 0.22 ± 0.27 bpm for respiratory rate (RR) detection. Moreover, the system demonstrated a recognition accuracy of 90.60% for three types of falls (i.e., stand, bow, squat to fall), one daily activity, and No Activity Background. These findings indicate that the radar sensor provides a high degree of accuracy suitable for various remote monitoring applications, thus enhancing the safety and well-being of elderly individuals in their homes.

Indoor location and navigation technologies are crucial for healthcare, security and other location-based services. Wi-Fi and inertial sensors have become mainstream indoor localization technologies for wearable device platforms due to simple deployment and low cost. This study proposes an extended Kalman filtering (EKF)-based multimodal sensor fusion algorithm for indoor localization, combining Wi-Fi fingerprint and inertial measurement unit (IMU) data to provide accurate and continuous pedestrian localization. The main contributions of this work are threefold. Firstly, a Wi-Fi fingerprint data augmentation method based on Access Point (AP) location sorting is proposed and a regression network model with a convolutional denoising autoencoder for WiFi-based indoor localization (CDAELoc) is designed to improve the robustness. Secondly, a dual-branch deep inertial odometry (DbDIO) network model for IMU-based indoor localization is introduced, consisting of two branches with various convolutional kernel sizes to extract features at different scales. Finally, an EKF-based Wi-Fi and Inertial Odometry (WIO-EKF) fusion localization system is presented, utilizing the predicted results from the proposed CDAELoc and DbDIO models as the system observations and mitigating the initial heading error of DbDIO. The proposed models are applied to the UJIIndoorLoc, RoNIN public datasets and self-collected dataset. Experimental results prove that the proposed CDAELoc model outperforms other Wi-Fi localization models, reducing the average positioning error by 12.5%. The proposed DbDIO model achieves higher accuracy and requires fewer model parameters than any other deep inertial odometry model. Finally, the average positioning error of WIO-EKF is lower than those of CDAELoc and DbDIO by 34% and 42%.

The Metaverse is an emerging concept that aims to integrate the physical and virtual worlds, creating a shared 3D virtual world where users can interact and immerse in new experiences. With the rise of Metaverse-driven Cyber-Physical-Social Systems (CPSSs), integrating drones as a critical technology in the Metaverse has become increasingly important. CPSSs have become proliferating and integral to our daily lives. This paper proposes a conceptual framework for Metaverse-driven drone edge intelligence, which integrates drone-enabled sensing, communication, and computation to enable real-time decision-making in CPSSs. We present a detailed analysis of the challenges and opportunities for integrating drones in the Metaverse and discuss the potential impact of our framework on various application domains. Our work contributes to advancing the Metaverse and CPSSs by providing a novel approach for empowering real-time decision-making and enabling new user experiences through integrating drones and the Metaverse. The proposed framework has the potential to revolutionize the way we approach data-driven decision-making in various industries and applications, including precision agriculture, transportation, emergency response, smart cities, healthcare, manufacturing, and energy.

Postural monitoring in wheelchair users is a topic of growing interest. The detection of changes in the sitting patterns of these patients may serve to detect changes in their functional status and be able to adapt rehabilitation early. For this reason, this paper presents a methodology for the detection of specific postural anomalies that, unlike previous works, adopts unsupervised learning. The proposed methodology involves data dimensionality reduction using Principal Component Analysis, and the application of K-means clustering to group different normal posture states. The anomalies are detected using a threshold approach, where data points that fall outside a certain threshold are considered as anomalies. The results show that the methodology is effective in identifying anomalies with a high degree of accuracy (around 90%).

Abstract According to the World Health Organization and other authorities, falls are one of the main causes of accidental injuries among the elderly population. Therefore, it is essential to detect and predict the fall activities of older persons in indoor environments such as homes, nursing, senior residential centers, and care facilities. Due to non-contact and signal confidentiality characteristics, radar equipment is widely used in indoor care, detection, and rescue. This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities. The experimental results show that the method is able to distinguish three types of fall activities (i.e., stand to fall, bow to fall, and squat to fall) and obtain a high recognition accuracy up to 95.7%.

The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. It takes a lot of effort to learn people’s food preferences and make recommendations based on their affects and nutrition. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social affect has some significant benefits over the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person’s affect is detected using electroencephalography (EEG) signals. EEG allows to capture the brain signals and analyze them to anticipate affective state toward a food. In this study, we have used a 14-channel wireless Emotiv Epoc+ to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person’s energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment parameters.

The development of activity recognition based on multi-modal data makes it possible to reduce human intervention in the process of monitoring. This paper proposes an efficient and cost-effective multi-modal sensing framework for activity monitoring, it can automatically identify human activities based on multi-modal data, and provide help to patients with moderate disabilities. The multi-modal sensing framework for activity monitoring relies on parallel processing of videos and inertial data. A new supervised adaptive multi-modal fusion method (AMFM) is used to process multi-modal human activity data. Spatio-temporal graph convolution network with adaptive loss function (ALSTGCN) is proposed to extract skeleton sequence features, and long short-term memory fully convolutional network (LSTM-FCN) module with adaptive loss function is adapted to extract inertial data features. An adaptive learning method is proposed at the decision level to learn the contribution of the two modalities to the classification results. The effectiveness of the algorithm is demonstrated on two public multi-modal datasets (UTD-MHAD and C-MHAD) and a new multi-modal dataset H-MHAD collected from our laboratory. The results show that the performance of the AMFM approach on three datasets is better than the performance of the video or the inertial-based single-modality model. The class-balanced cross-entropy loss function further improves the model performance based on the H-MHAD dataset. The accuracy of action recognition is 91.18%, and the recall rate of falling activity is 100%. The results illustrate that using multiple heterogeneous sensors to realize automatic process monitoring is a feasible alternative to the manual response.

Recent years have witnessed an increasing popularity of wireless body area network (WBAN), with which continuous collection of physiological signals can be conveniently performed for healthcare monitoring. Energy consumption is a critical issue because it directly affects the duration of the equipped sensors. In this article, we propose a low-cost and confidential electrocardiogram (ECG) acquisition approach for WBAN. The compressed sensing (CS) is employed for low-cost signal acquisition, and its cryptographic features are exploited for promoting the framework's confidentiality. In particular, the RIPless measurement matrix is used to give CS the resistance against plaintext attack, while the first-order Σ∆ quantizer is employed to embed the cryptographic diffusion feature into the whole system. Two chaotic systems are employed for generating the required secret elements for the acquisition and encryption. Experiment results well demonstrate the signal reconstruction and security performance of the proposed framework.

The recent widespread pandemic of COVID-19 has put tremendous pressure on the healthcare system. The deployment of telehealth technology is crucial in solving this problem when patients are mildly ill and need to self-isolate at home or in a specific location. This paper proposes using a single radar sensor to continuously contact-less monitor the patients' vital signals in their daily lives. We use edge computing to handle high-priory tasks and combined cloud infrastructure for further process and storage to provide monitoring and telehealth services. A case study is presented to show how the approach can continuously monitor and recognize high-risk diseases and abnormal activity (e.g., sleep apnea). While an accident occurs, the system could provide fast and accurate emergency services. The work has been compared with a good standard. And the experimental results show that the proposed approach for heart rate (HR) and respiratory rate (RR) detection achieved a Mean Absolute Error (MAE) ± Standard Deviation of Absolute Error (SDAE) of 0.09±1.43 bpm and 0.23±3.23 bpm, respectively. This indicates the radar sensor can provide a high recognition accuracy to meet the requirements for a range of cardiopulmonary function monitoring. This kind of telemedicine service facilitates monitoring the self-isolated subjects to detect and recognize human physical and physiological activities.

The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high-risk pregnancies based on IoT sensors, feature extraction based on data analytics, and an intelligent diagnostic aid system based on a 1-D convolutional neural network (CNN) classifier. The fetal heart rate and a group of maternal clinical indicators, such as the uterine tonus activity, blood pressure, heart rate, temperature, and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different formats and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal, and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.

The ever-growing aging of the population has emphasized the importance of in-home AAL (Ambient Assisted Living) services for monitoring and improving its well-being and health, especially in the context of care facilities (retirement villages, clinics, senior neighborhood, etc). The paper proposes a novel simulation-driven platform named E-ALPHA (Edge-based Assisted Living Platform for Home cAre) which supports both Edge and Cloud Computing paradigm to develop innovative AAL services in scenarios of different scales. E-ALPHA flexibly combines Edge, Cloud or Edge/Cloud deployments, supports different communication protocols, and fosters the interoperability with other IoT platforms. Moreover, the simulation-based design helps in preliminary assessing (i) the expected performance of the service to be deployed according to the infrastructural characteristics of each specific small, medium and large scenario; and (ii) the most appropriate applications/platform configuration for a real deployment (kind and number of involved devices, Edge- or Cloud-based deployment, required connectivity type, etc). In this direction, two different use cases modeled according to realistic input (coming from past experience involving real testbed) are shown in order to demonstrate the potentials of the proposed simulation-driven AAL platform.

Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and the huge amount of data coming from Internet of Things (IoT) devices toward the Internet. In the next future, Edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus, the paper proposes BodyEdge, a novel architecture well suited for human-centric applications, in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing edge gateway supporting multiradio and multitechnology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public cloud platforms to guarantee a high flexibility, robustness, and adaptive service level. The advantages of the designed software platform have been evaluated in terms of reduced transmitted data and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which BodyEdge is a valid and inexpensive solution for healthcare application scenarios.

The integration of existing and future smart cyberphysical systems within a fully realized Internet of Things (IoT) cannot dismiss the requirement of interoperability. The absence of standards for the IoT along with its intrinsic complexity demand for proper methodologies in order to fully support the development of heterogeneous, but interoperable, IoT systems as well as their integration. However, at the state-of-the-art, no methodologies for IoT systems integration are available. To fill this gap, in this paper the INTER-METH engineering methodology is presented. Developed in the context of the European H2020 project named INTER-IoT, INTER-METH supports the integration process of heterogeneous IoT platforms from the analysis to the maintenance phase. Its main features as well as its abstract and instantiated process schema are described; in particular, in this paper the focus is on the analysis phase that is fundamental for driving the integration process.

This paper proposes a method for the detection of emotion-relevant activities performed when seated and its corresponding system based on wrist-worn inertial sensors combined with a pressure detection smart cushion. In particular, aiming at providing an additional source of information in traditional emotion recognition systems, we focus on shame-, fear-, and joy-related activities. Experiments are conducted and the results of performance evaluation show the proposed method achieves high recognition accuracy with a set obtained by fusing time-and frequency-domain features extracted from the different available sensors.

Originated from Wireless Sensor Networks (WSNs), Body Sensor Networks (BSNs) have been applied to numerous domains. However, after an in-depth analysis of the state-of-the-art, several factors have been found to limit the development of applications based on BSNs. In this paper we introduce the concept of Open BSNs for improving the development of BSNs. Specifically, open BSNs can improve key aspects such as energy efficiency, system interoperability, system usability and scalability, and privacy support. Application scenarios and future research challenges of Open BSNs are also presented.

Mobility impaired individuals need the wheelchair to support their independent life, so monitor activities performed on the wheelchair can provide significant insights on their general health status. Activity recognition related to healthy people is a well established research area; however, only few works addressed this problem for wheelchair users. This paper proposes a novel approach based on dynamic Bayesian networks to recognize physical activities performed on a wheelchair. We equipped the wheelchair seat with a pressure detection unit and attached two inertial measurement units on the user's wrists. We focus on common basic activities and specifically, to experimentally evaluate our method, we defined four dynamic activities (moving forward, moving backward, moving left-circle, moving right-circle) and two static activities (left-right swing, forward-backward swing). Data is collected using a smart wheelchair system we developed in previous research. Firstly, we generate the posture sequence from the pressure signals and detect the raw acceleration data from inertial measurement units; then, we fuse the posture sequence and inertial features to detect the postural-based activities. Results shows that our proposed method can achieve an overall classification accuracy of 91.88%.

In recent years, the elderly population is increasing enormously, from 9% in 1994 to 12% in 2014, and is expected to reach 21% by 2050. Elderly live often alone today and even conducting an independent daily life, some of them move with the aid of walkers or using wheelchairs. Monitoring elderly activity in mobility has become a major priority to provide them an effective care service. This paper focuses on an enhancement of a smart wheelchair based on pressure sensors to monitor users sitting on the wheelchair. If the wheelchair user assumes a dangerous posture, the system triggers audio/visual alarms to avoid critical consequences such as wheelchair overturn. The paper discusses the hardware design of the system, then analyzes and compares posture recognition methods that have been applied on pressure data we collected. The experiments demonstrate the effectiveness of the proposed method and 99.5% posture recognition accuracy has been observed.

Cardiac Defense Response (CDR) is a basic psycho-physiological response related to startle reflex and preceding negative emotional states including fear. In the health-care context, the definition of methods to automatically identify the CDR is a relevant issue, because frequent CDR activations (not associated to proper danger stimuli) can pose the subject to health risk and eventually develop into severe psychophysical disorders. Therefore, providing tools for automatic identification of this defense mechanism can significantly help psychologists and caregivers in understanding the patient's mental and health status as well as patients themselves to self-regulate and self-control against excessive defense and stress responses. This work discusses and compares different methods and specifically proposes a novel algorithm designed to detect the CDR by analyzing the electrocardiogram (ECG) signal. It is based on the extraction of specific features from a signal, directly generated from the ECG, which are compared against an ad-hoc computed reference CDR template. The proposed method has been tested on real ECG traces, a number of them containing full activations of the CDR pattern, and compared against other techniques, discussed in the paper, reaching an improvement of 10 percent in sensitivity, 18 percent in specificity, and 24 percent in precision with respect to the best performance of the other related methods.

This paper describes a novel integrated system for detecting, monitoring, and securely recording human physical activities using wearable sensors, a personal mobile device, and a Cloud-computing infrastructure supported by the BodyCloud [1] platform. Body Sensor Networks (BSNs), empowered by wireless non-invasive wearable physiological sensors, have been widely accepted as one of the key enabling technologies for the revolution of personal-health services. In addition, the integration of BSN applications with Cloud-computing technologies can effectively supports the diffusion of such services in our daily life. Many of these personal-health systems - regardless of their final aim - are based, use or are supported by contextual information on user's physical activity (body posture, movement or action) being performed. This work, hence, aims at providing a basic physical activity service that is capable of supporting personal, mobile-Health applications with real-time activity recognition and labeling both on the personal mobile device and on the Cloud.

Wireless body sensor networks (BSNs) possess enormous potential for changing people's daily lives. They can enhance many human-centered application domains such as m-Health, sport and wellness, and human-centered applications that involve physical/virtual social interactions. However, there are still challenging issues that limit their wide diffusion in real life: primarily, the programming complexity of these systems, due to the lack of high-level software abstractions, and the hardware constraints of wearable devices. In contrast with low-level programming and general-purpose middleware, domain-specific frameworks are an emerging programming paradigm designed to fulfill the lack of suitable BSN programming support with proper abstraction layers. This paper analyzes the most important requirements for an effective BSN-specific software framework, enabling efficient signal-processing applications. Specifically, we present signal processing in node environment (SPINE), an open-source programming framework, designed to support rapid and flexible prototyping and management of BSN applications. We describe how SPINE efficiently addresses the identified requirements while providing performance analysis on the most common hardware/software sensor platforms. We also report a few high-impact BSN applications that have been entirely implemented using SPINE to demonstrate practical examples of its effectiveness and flexibility. This development experience has notably led to the definition of a SPINE-based design methodology for BSN applications. Finally, lessons learned from the development of such applications and from feedback received by the SPINE community are discussed.

In this paper we propose reference architectures and SPINE-based middleware for Collaborative Body Sensor Networks (CBSNs) that can enable new smart wearable systems in the context of physical pervasive computing environments. CBSNs are wireless BSNs that are able to cooperate to support a common goal. Cooperation is therefore based on interaction among the CBSNs and distributed computation across the interacting CBSNs. In particular, interaction can be activated when CBSNs are in proximity and based on service-specific protocols that allow for service management between the involved CBSNs. Specifically, the paper presents C-SPINE, an enhancement of the SPINE middleware for CBSN applications. Finally, a collaborative emotion detection system, integrating heart rate sensing with handshake detection, is developed through C-SPINE and experimentally analyzed.

Wireless sensor networks (WSNs) are emerging as powerful platforms for distributed embedded computing supporting a variety of high-impact applications. However, programming WSN applications is a complex task that requires suitable paradigms and technologies capable of supporting the specific characteristics of such networks which uniquely integrate distributed sensing, computation and communication. Mobile agents are a distributed computing paradigm based on code mobility that has already demonstrated high effectiveness and efficiency in IP-based highly dynamic distributed environments. Due to their intrinsic characteristics, mobile agents may provide more benefits in the context of WSNs than in conventional distributed environments. In this paper we present the design, implementation and experimentation of MAPS (Mobile Agent Platform for Sun SPOT), an innovative Java-based framework for wireless sensor networks based on Sun SPOT technology which enables agent-oriented programming of WSN applications. The MAPS architecture is based on components that interact through events. Each component offers a minimal set of services to mobile agents that are modeled as multi-plane state machines driven by ECA rules. In particular, the offered services include message transmission, agent creation, agent cloning, agent migration, timer handling and easy access to the sensor node resources (sensors, actuators, input switches, flash memory and battery). Agent programming with MAPS is presented through both a simple example related to mobile agent-based monitoring of a sensor node and a more complex case study for real-time human activity monitoring based on wireless body sensor networks. Moreover, a performance evaluation of MAPS carried out by computing micro-benchmarks, related to agent communication, creation and migration, is illustrated.

Abstract Wireless body sensor networks (WBSNs) enable a broad range of applications for continuous and real‐time health monitoring and medical assistance. Programming WBSN applications is a complex task especially due to the limitation of resources of typical hardware platforms and to the lack of suitable software abstractions. In this paper, SPINE (signal processing in‐node environment), a domain‐specific framework for rapid prototyping of WBSN applications, which is lightweight and flexible enough to be easily customized to fit particular application‐specific needs, is presented. The architecture of SPINE has two main components: one implemented on the node coordinating the WBSN and one on the nodes with sensors. The former is based on a Java application, which allows to configure and manage the network and implements the classification functions that are too heavy to be implemented on the sensor nodes. The latter supports sensing, computing and data transmission operations through a set of libraries, protocols and utility functions that are currently implemented for TinyOS platforms. SPINE allows evaluating different architectural choices and deciding how to distribute signal processing and classification functions over the nodes of the network. Finally, this paper describes an activity monitoring application and presents the benefits of using the SPINE framework. Copyright © 2010 John Wiley & Sons, Ltd.

Employment of BSN-based technologies in real world scenarios requires a flexible infrastructure at both hardware and software level. In this paper, we emphasize how the use of SPINE (Signal Processing In-Node Environment), a software framework for BSN, supports the development of heterogeneous health-care applications based on reusable subsystems. One of the main goal of SPINE is to provide a flexible architecture that can support variety of practical applications without the need for costly redeployment of the code running on sensor nodes. We also present a SPINE sensor node emulator that supports the first phase of the algorithm design, when the actual hardware devices may not be available. This approach can guide the choice of the required hardware (e.g. the sensors) to meet the application requirements based on the results obtained in the emulated environment. Such tool can simplify the research collaboration during the specification stage of a project, due to availability of a common (virtual) architecture.

SPINE (signal processing in node environment) is a framework for the development of body sensor network (BSN) applications. It provides developers of signal processing algorithms with APIs and libraries of protocols, utilities and data processing functions. Hence, it offers application developers new abstractions that improve interoperability and allow to reduce development time. This paper presents the architecture and the capabilities of the SPINE framework, and shows its use in the development of a real-time activity monitoring system prototype.