Manual annotation (MA) of sensor data for Human Activity Recognition (HAR) is labor-intensive, error-prone, and limits scalability. This paper proposes a multi-sensor methodology to automatically generate training labels (aka. soft labels) for HAR systems without human intervention. The approach integrates data from inertial measurement units glued to objects of daily life with the Received Signal Strength Indicator (RSSI) information derived from BLE beacon anchors for estimating both the performed activity and the subject’s location. We validate the quality of the generated soft labels against video-based MA ground truth. Experimental results show that a deep learning model for HAR trained on a Wi-Fi Channel State Information (CSI) dataset annotated with soft labels achieves comparable results with respect to the same model trained on the corresponding manually-annotated dataset.

Indoor and outdoor localization systems are now essential tools for delivering Internet of Things (IoT)-based services across a range of fields, including smart buildings, industrial automation, and healthcare. These systems primarily aim to determine the position of users or objects within a given environment. Due to the abundance of electronic devices in these settings, localization techniques that utilize device interactions, such as fingerprinting, offer a cost-effective and accurate solution. However, the fingerprinting approach involves a time-intensive mapping phase, and training and executing the associated localization algorithm can impose demanding time, energy, and computational resource requirements. In this context, our paper introduces a practical system that eliminates the need for time-consuming environment mapping by leveraging radio signal emitters within the environment. Our approach achieved localization accuracy with an error range between 1.5 $m^2$ (best) - 6.5 $m^2$ (worst). Additionally, we collect human activity data from accelerometer, gyroscope, and magnetometer sensors to develop pattern recognition models for everyday activities.

Falls can have significant and far-reaching effects on various groups, particularly the elderly, workers, and the general population. These effects can impact both physical and psychological well-being, leading to long-term health problems, reduced productivity, and a decreased quality of life. Numerous fall detection systems have been developed to prompt first aid in the event of a fall and reduce its impact on people's lives. However, detecting a fall after it has occurred is insufficient to mitigate its consequences, such as trauma. These effects can be further minimized by activating safety systems (e.g., wearable airbags) during the fall itself—specifically in the pre-impact phase—to reduce the severity of the impact when hitting the ground. Achieving this, however, requires recognizing the fall early enough to provide the necessary time for the safety system to become fully operational before impact. To address this challenge, this paper introduces a novel lightweight convolutional neural network (CNN) designed to detect pre-impact falls. The proposed model overcomes the limitations of current solutions regarding deployability on resource-constrained embedded devices, specifically for controlling the inflation of an airbag jacket. We extensively tested and compared our model, deployed on an STM32F722 microcontroller, against state-of-the-art approaches using two different datasets.

Using WiFi's Channel State Information for human activity recognition—referred to as WiFi sensing—has attracted considerable attention. But despite this interest and many publications over a decade, WiFi sensing has not yet found its way into practice because of a lack of robustness of the inference results. In this paper, we quantitatively show that even “microchanges” in the environment can significantly impact WiFi signals, and potentially alter the ML inference results. We therefore argue that new training and inference techniques might be necessary for mainstream adoption of WiFi sensing.

According to data from the US Bureau of Labor Statistics, the number of job postings for software engineers has steadily increased over the past few years and is expected to grow by 22% from 2019 to 2029. This paper presents the pedagogical experience within the new Immersive Software Engineering (ISE) program concerning mathematical foundations and data analytics topics. These topics were designed to cover essential mathematical concepts such as calculus, linear algebra, probability, and statistics and their integration within data analytic tools and techniques such as time-series forecasting, data cleaning, data visualization, and introduction to pattern recognition. In addition, hands-on projects and real-world applications were incorporated throughout the course to provide students with practical experience in these areas. We reflect on the first delivery of the ISE course, which provided students with a new innovative blended learning environment, and how it will be further developed towards Open Educational Resources (OER) components and refined to respond to the rapidly evolving needs of the software engineering industry.

In the context of smart buildings and smart cities, the design of low-cost and privacy-aware solutions for recognizing the presence of humans and their activities is becoming of great interest. Existing solutions exploiting wearables and video-based systems have several drawbacks, such as high cost, low usability, poor portability, and privacy-related issues. Consequently, more ubiquitous and accessible solutions, such as WiFi sensing, became the focus of attention. However, at the current state-of-the-art, WiFi sensing is subject to low accuracy and poor generalization, primarily affected by environmental factors, such as humidity and temperature variations, and furniture position changes. Such is-sues are partially solved at the cost of complex data preprocessing pipelines. In this paper, we present a highly accurate, resource-efficient deep learning-based occupancy detection solution, which is resilient to variations in humidity and temperature. The approach is tested on an extensive benchmark, where people are free to move and the furniture layout does change. In addition, based on a consolidated algorithm of explainable AI, we quantify the importance of the WiFi signal w.r.t. humidity and temperature for the proposed approach. Notably, humidity and temperature can indeed be predicted based on WiFi signals; this promotes the expressivity of the WiFi signal and at the same time the need for a non-linear model to properly deal with it.

Obesity is a real health emergency with significant consequences for individuals and society, reducing expectations and quality of life or even the death for 2.8 million people each year. Being a chronic condition with limited pharmacological options, weight loss therapies are mainly based on dietary and cognitive-behavioral interventions delivered at specialist public and/or private centers. In order to promote and facilitate weight loss, this paper proposes a virtual coaching system to motivate and guide the patients during the therapy and to support the clinicians in monitoring its effectiveness. The system has three components: a) the clinician's web application, which is used to monitor and modify the therapy for each patient; b) a mobile application for patients, used to log nutrition intakes and physical activities, and to consult motivational documents/videos; and c) a cloud server, which collects data and implements smart monitoring features. The platform is adopted in a clinical trial involving 120 patients with obesity (Body Mass Index 2> 30) that has recently started.

Over the last decade, Human Activity Recognition (HAR) has become a vibrant research field in various applications scenarios, ranging from sports, healthcare and well-being to smart cities, smart homes, and industry, mainly due to the widespread availability of devices as smartphones, smartwatches, and wearables. A key ingredient for sophisticated HAR systems is represented by the availability of high-quality datasets. These are generally gathered by dedicated Body Area Networks (BANs), and further elaborated through machine learning and deep learning algorithms. Thus, the BAN design plays a central role in such a context, where the main challenges are related to easiness of use, costs and energy constraints of their components. In this context, our paper presents a highly configurable HAR system, based on a low-cost and easy-to-use BAN. The system includes a CNN-based algorithm validated over a dataset, collected through the proposed BAN, on 12 persons performing 7 different human activities.

Identity recognition is increasingly used to control access to sensitive data, restricted areas in industrial, healthcare, and defense settings, as well as in consumer electronics. To this end, existing approaches are typically based on collecting and analyzing biometric data and imply severe privacy con-cerns. Particularly when cameras are involved, users might even reject or dismiss an identity recognition system. Furthermore, iris or fingerprint scanners, cameras, microphones, etc., imply installation and maintenance costs and require the user's active participation in the recognition procedure. This paper proposes a non-intrusive identity recognition system based on analyzing WiFi's Channel State Information (CSI). We show that CSI data attenuated by a person's body and typical movements allows for a reliable identification - even in a sitting posture. We further propose a lightweight deep learning algorithm trained using CSI data, which we implemented and evaluated on an embedded platform (i.e., a Raspberry Pi 4B). Our results obtained using real-world experiments suggest a high accuracy in recognizing people's identity, with a specificity of 98% and a sensitivity of 99%, while requiring a low training effort and negligible cost.

This article presents a low-cost system for distance estimation, occupancy counting, and presence detection based on Bluetooth Low Energy radio signal variation patterns that mitigates the limitation of existing approaches related to economic cost, privacy concerns, computational requirements, and lack of ubiquitousness. To explore the approach effectiveness, exhaustive tests have been carried out on four different datasets by exploiting several pattern recognition models.

Some wearable solutions exploiting on-body acceleration sensors have been proposed to recognize Freezing of Gait (FoG) in people affected by Parkinson Disease (PD). Once a FoG event is detected, these systems generate a sequence of rhythmic stimuli to allow the patient restarting the gait. While these solutions are effective in detecting FoG events, they are unable to predict FoG to prevent its occurrence. This paper fills in the gap by presenting a machine learning-based approach that classifies accelerometer data from PD patients, recognizing a pre-FOG phase to further anticipate FoG occurrence in advance. Gait was monitored by three tri-axial accelerometer sensors worn on the back, hip and ankle. Gait features were then extracted from the accelerometer's raw data through data windowing and non-linear dimensionality reduction. A k-nearest neighbor algorithm (k-NN) was used to classify gait in three classes of events: pre-FoG, no-FoG and FoG. The accuracy of the proposed solution was compared to state-of-the-art approaches. Our study showed that: (i) we achieved performances overcoming the state-of-the-art approaches in terms of FoG detection, (ii) we were able, for the very first time in the literature, to predict FoG by identifying the pre-FoG events with an average sensitivity and specificity of, respectively, 94.1% and 97.1%, and (iii) our algorithm can be executed on resource-constrained devices. Future applications include the implementation on a mobile device, and the administration of rhythmic stimuli by a wearable device to help the patient overcome the FoG.

People affected by the Parkinson's disease are often subject to episodes of Freezing of Gait (FoG) near specific areas within their environment. In order to prevent such episodes, this paper presents a low-cost indoor localization system specifically designed to identify these critical areas. The final aim is to exploit the output of this system within a wearable device, to generate a rhythmic stimuli able to prevent the FoG when the person enters a risky area. The proposed localization system is based on a classification engine, which uses a fingerprinting phase for the initial training. It is then dynamically adjusted by exploiting a probabilistic graph model of the environment.

We present the new methodology ONTO-PLC to deliver software programs on system-on-chip or single-board computers used to control industrial plants, as substitutes for programmable logic control technologies. The methodology is ontology-driven based on the abstract description of the plant at a level in which the plant itself is viewed as a set of instruments, each instrument being a set of machineries coordinated in functional terms by a control system, formed by sensors and actuators, under the control of an abstract model of behavior delivered by means of an extended finite state machine.

In the last decade, several localization systems have been proposed, some of them achieving an accuracy in the order of decimeters. Nonetheless, a broad range of these methods may reveal unattractive for low-cost and resource-bounded mobile application scenarios. Indeed, significant constraints stem from costs and availability of the required technological infrastructure for the target environment. In addition, resources necessary to train and run the localization algorithm may pose stringent requirements in terms of time, power and computational. In the aforementioned context, this paper presents an effective radio-based positioning algorithm able to localize the user by exploiting a graph-based representation of the environment. The graph is created by analyzing the received signal level and the movement directions of a mobile device with respect to a set of real and virtual landmarks. The comparison with a commercial localization system proves the effectiveness and accuracy of the proposed approach.

This paper presents an approach to automatically generate transactors that implement TLM protocols for RTL IPs, such that the RTL IPs can be abstracted towards corresponding TLM models and easily integrated inside a TLM virtual prototype. The obtained transactor is self-adaptive, since it allows plugging the target IP in the virtual prototype independently from the protocol implemented by the corresponding TLM initiator. The transactor is automatically created from the set of PSL assertions that describe the temporal behaviour of the communication protocol of the original RTL IP.