B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows

Human Activity Recognition (HAR), based on machine and deep learning algorithms is considered one of the most promising technologies to monitor professional and daily life activities for different categories of people (e.g., athletes, elderly, kids, employers) in order to provide a variety of services related, for example to well-being, empowering of technical performances, prevention of risky situation, and educational purposes. However, the analysis of the effectiveness and the efficiency of HAR methodologies suffers from the lack of a standard workflow, which might represent the baseline for the estimation of the quality of the developed pattern recognition models. This makes the comparison among different approaches a challenging task. In addition, researchers can make mistakes that, when not detected, definitely affect the achieved results. To mitigate such issues, this paper proposes an open-source automatic and highly configurable framework, named B-HAR, for the definition, standardization, and development of a baseline framework in order to evaluate and compare HAR methodologies. It implements the most popular data processing methods for data preparation and the most commonly used machine and deep learning pattern recognition models.Comment: 9 Pages, 3 Figures, 3 Tables, Link to B-HAR Library: https://github.com/B-HAR-HumanActivityRecognition/B-HA

SHPIA 2.0: An Easily Scalable, Low-Cost, Multi-purpose Smart Home Platform for Intelligent Applications

Sensors, electronic devices, and smart systems have invaded the market and our daily lives. As a result, their utility in smart home contexts to improve the quality of life, especially for the elderly and people with special needs, is getting stronger and stronger. Therefore, many systems based on smart applications and intelligent devices have been developed, for example, to monitor people’s environmental contexts, help in daily-life activities, and analyze their health status. However, most existing solutions have drawbacks related to accessibility and usability. They tend to be expensive and lack generality and interoperability. These solutions are not easily scalable and are typically designed for specific constrained scenarios. This paper tackles such drawbacks by presenting SHPIA 2.0, an easily scalable, low-cost, multi-purpose smart home platform for intelligent applications. It leverages low-cost Bluetooth Low Energy (BLE) devices featuring both BLE connected and BLE broadcast modes, to transform common objects of daily life into smart objects. Moreover, SHPIA 2.0 allows the col- lection and automatic labeling of different data types to provide indoor monitoring and assistance. Specifically, SHPIA 2.0 is designed to be adaptable to different home-based application scenarios, including human activity recognition, coaching systems, and occupancy detection and counting. The SHPIA platform is open source and freely available to the scientific community, fostering collaboration and innovation

Towards Posture and Gait Evaluation through Wearable-Based Biofeedback Technologies

In medicine and sport science, postural evaluation is an essential part of gait and posture correction. There are various instruments for quantifying the postural system’s efficiency and deter- mining postural stability which are considered state-of-the-art. However, such systems present many limitations related to accessibility, economic cost, size, intrusiveness, usability, and time-consuming set-up. To mitigate these limitations, this project aims to verify how wearable devices can be assem- bled and employed to provide feedback to human subjects for gait and posture improvement, which could be applied for sports performance or motor impairment rehabilitation (from neurodegenerative diseases, aging, or injuries). The project is divided into three parts: the first part provides experimen- tal protocols for studying action anticipation and related processes involved in controlling posture and gait based on state-of-the-art instrumentation. The second part provides a biofeedback strategy for these measures concerning the design of a low-cost wearable system. Finally, the third provides al- gorithmic processing of the biofeedback to customize the feedback based on performance conditions, including individual variability. Here, we provide a detailed experimental design that distinguishes significant postural indicators through a conjunct architecture that integrates state-of-the-art postural and gait control instrumentation and a data collection and analysis framework based on low-cost devices and freely accessible machine learning techniques. Preliminary results on 12 subjects showed that the proposed methodology accurately recognized the phases of the defined motor tasks (i.e., rotate, in position, APAs, drop, and recover) with overall F1-scores of 89.6% and 92.4%, respectively, concerning subject-independent and subject-dependent testing setups

Metals in Sediment Cores from Nine Coastal Lagoons in Central Vietnam

Problem statement: After being dramatically hit by war events, Vietnam is presently experiencing a huge economical and social development. However, very few data, relative to pollution levels and trends, are available for the correct management of critical areas such as coastal lagoons, where many economical activities are linked to high value environmental features. Approach: A set of sediment cores from nine coastal lagoons of central Vietnam (Lang Co, Truong Giang, An Khe, Nuoc Man, Nuoc Ngot, Thi Nai, O Loan, Thuy Trieu and Dam Nai) were sampled in 2008 and analyzed to assess metal and (Al, Cd, Cr, Cu, Fe, Hg, Li, Mn, Ni, Pb, V, U and Zn) and As levels and historical trends. Results: Concentrations are generally low, with the exception of As, which often exceeds ERL guidelines and Ni that does the same at O Loan. In some cases, concentrations-depth profiles account for recent increasing trends but surficial values are still low when compared to both international guidelines and polluted sediments all around the world. Sediment grain size seems to affect the depth distribution of a number of metals and when normalized to the content of silt and clay, values are particularly high at Dam Nai and Thi Nai, due to the very coarse composition of surficial sediments. Conclusion: Metal concentrations in lagoon sediments derive from the composition of rocks and soils in the watersheds. However, recent increasing trends need for further monitoring

A freely available system for human activity recognition based on a low-cost body area network

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

Leveraging mmWave for Contactless Breath Rate Estimation of Moving Subjects

The breath rate (BR) measurement is fundamental for comprehensive human health monitoring in many scenarios. Accurately estimating BR, particularly in moving subjects, poses several challenges, including the necessity for direct physical contact with the individual or constraints on the individual's movements and mobility. In this paper, a methodology is proposed that employs frequency-modulated continuous wave (FMCW) radar, operating at a frequency of 77 GHz, to estimate the BR of subjects moving freely within an environment. The proposed methodology features a pre-processing pipeline designed to refine the radar's captured signals. The processed signals are used to accurately track the subject, and estimate their corresponding BR. Preliminary outcomes of our investigation indicate that the BR estimates generated by our system exhibit a mean absolute error (MAE) of 2.48 breaths per minute, as averaged across four subjects when compared with the ground truth

Real-Time Multi-Person Identification and Tracking via HPE and IMU Data Fusion

In the context of smart environments, crafting re- mote monitoring systems that are efficient, cost-effective, user- friendly, and respectful of privacy is crucial for many scenar- ios. Recognizing and tracing individuals via markerless motion capture systems in multi-person settings poses challenges due to obstructions, varying light conditions, and intricate interactions among subjects. Nevertheless, methods based on data gathered by Inertial Measurement Units (IMUs) located in wearables grapple with other issues, including the precision of the sensors and their optimal placement on the body. We then argue that more accurate results can be achieved by mixing human pose estimation (HPE) techniques with information collected by wearables. Thus, this paper introduces a real-time platform to track and identify per- sons by fusing HPE and IMU data. It exploits a matching model that consists of two synergistic components: the first employs a geometric approach, correlating orientation, acceleration, and velocity readings from the input sources, while the second utilizes a Convolutional Neural Network (CNN) to yield a correlation coefficient for each HPE and IMU data pair. The proposed platform achieves promising results in tracking and identification, with an accuracy rate of 96.9%

ICT-Based Solutions for Alzheimer’s Disease Care: A Systematic Review

In recent years, there has been a growing recognition of the significant challenges posed by Alzheimer’s Disease (AD) and the need for innovative solutions to improve the quality of life for affected individuals. As AD prevalence continues to rise, technological advancements offer promising opportunities to address the multifaceted needs of patients and caregivers. This survey paper thoroughly investigates technological innovations in AD care, offering valuable insights into cutting-edge approaches that have the potential to positively impact the lives of affected individuals. By providing a holistic view of available assistive solutions, we review 2459 papers and selected 46 relevant studies published between 2015 and 2023, specifically focusing on healthcare technologies and solutions, utilizing sensing methods. The former will include Telemedicine, E-health, Smart Environment, Internet of Things (IoT), Ambient Assisted Living (AAL), Internet of Medical Things (IoMT), and Personalized Assistive Solutions (PAS), while the latter encompasses Wearable/Environmental, Radio/Audio, Video/Image, and Digital Platforms. Our comparative assessment of recent survey papers reveals the unique contribution of this study, as it comprehensively examines the intersection of multiple parameters. By summarizing insights from these studies, we identify gaps and recommend future directions for advancements in AD care

Combining 3D Human Pose Estimation and IMU Sensors for Human Identification and Tracking in Multi-Person Environments

Human pose estimation (HPE) based on deep neural networks (DNN) aims to predict the poses of human body in videos without needing markers. One of the main limitations in its applicability is consistently identifying and tracking the keypoints of an individual in multi-person scenarios. Despite various solutions based on image analysis being attempted, challenges such as model accuracy, occlusions, or individuals exiting the camera’s field of view often result in the loss of the association between humans and their keypoints across video frames. In this article, we propose a human identification and tracking methodology in multi-person environments based on data fusion between HPE software and wearable IMU sensors. We demonstrate how to align the data generated by these two sensor categories (camera-based HPE and IMUs) and assess the alignment between each skeleton of keypoints and IMU pair using a scoring system. Additionally, we illustrate how to combine different metrics, such as orientation, acceleration, and velocity, to address alignment problems caused by inaccuracies in sensor data

Enhancing Freezing of Gait Detection in Parkinson’s Through Fine-Tuned Deep Learning Models

Freezing of Gait (FoG) is a common and disabling symptom in Parkinson's Disease (PD), characterized by a sudden and temporary inability to initiate or continue walking. FoG arises from various factors such as environmental triggers, or physiological status of people with Parkinson's. Traditional methods for preventing or alleviating FoG have limitations, prompting exploration into new technologies, such as the combination of sensing technologies and Deep Learning (DL) and Machine Learning (ML) algorithms. However, recognizing FoG with sensors and ML/DL poses challenges, such as the generalizability of the FoG recognition models over different individuals. Moreover, current approaches often require extensive time and effort to personalize the FoG recognition models. To mitigate these challenges, we propose a system that reduces the workload for creating personalized models through a fine-tuning approach. Our methodology has undergone rigorous testing in a subject-independent setup on a self-collected dataset of 22 subjects. Through the fine-tuning phase, we observed a remarkable average increase of up to 20.9 % in F1-score performance compared to the training and testing approach without fine-tuning