1,720,985 research outputs found
Fostering Human Activity Recognition Workflows: An Open-Source Baseline Framework
No full textThe application of machine and deep learning algorithms in Human Activity Recognition (HAR) has shown great potential for monitoring various professional and daily life activities, benefiting different research areas such as healthcare, well-being and industrial automation. HAR can enable the development of various services and applications to empower technical performance and enable risk prevention in working places, to support education and training, and, more in general, to monitor the biopsychosocial status of people. However, we still lack a baseline framework for easily implementing the data processing pipeline that must be designed to setup and configure HAR workflows. This makes challenging to estimate the effectiveness, efficiency, and the overall quality of HAR solutions, thus hindering the comparison among different approaches. This also increases the likelihood that researchers introduce errors, which negatively affect the accuracy of the obtained results. To fill in the gap, this paper introduces B-HAR, an open-source framework to automatically implement baseline HAR workflows
A cosimulation methodology for HW/SW validation and performance estimation
No full textCosimulation strategies allow us to simulate and verify HW/SW embedded systems before the real platform is available. In this field, there is a large variety of approaches that rely on different communication mechanisms to implement an efficient interface between the SW and the HW simulators. However, the literature lacks a comprehensive methodology which addresses the need for integrating and synchronizing heterogeneous simulators, like, for example, the SystemC simulation kernel for HW modules and an instruction set simulator for SW applications, without being intrusive for the HW and SW descriptions involved in the simulation. In this context, this article presents, compares, and integrates in a system-level framework two different co-simulation strategies for modeling, analyzing, and validating the performance of a HW/SW embedded system. Moreover, for both of them, a mechanism is proposed to provide an accurate time synchronization of the HW/SW communication. The first strategy is intended to provide an early cosimulation environment where HW/SW interaction can be validated without involving the operating system. The communication is implemented between a single SW task and a SystemC description of an HW module by exploiting the features of the remote debugging interface of a debugger (the GNU GDB), and by modifying the SystemC simulation kernel. On the other hand, the second strategy is intended to be used in further development steps, when the operating system is introduced to validate the cosimulation between HW modules and multitasking SW applications. In this approach, the communication is implemented via interrupts by using the features offered by the operating system. Experimental results are reported on two different case studies to analyze and compare the effectiveness of both the approache
Dynamic and formal verification of embedded systems: A comparative survey
No full textEmbedded Systems, by their nature, constitute a meeting point for communities with extremely different background. In particular, the high demands for quality and reliability for embedded systems have led to complementary quality assurance efforts: hardware engineers have developed techniques for dynamic verification in terms of co-simulation, which, in particular, addresses the different nature of hardware and software components. Thus these techniques are tailored for the transactional level, which comprises dedicated models for the hardware and the software parts. On the other hand, there is a bulk of work on formal verification techniques, which typically address higher levels of abstraction. These techniques are exhaustive in the sense that they cover all the infinite possible paths of their models, however at the price of neglecting many of the low-level aspects treated by co-simulation. It is the goal of this paper to increase the mutual understanding between these communities and to animate research at this exciting borderline
Estimating Indoor Occupancy Through Low-Cost BLE Devices
Full text linkDetecting the presence of persons and estimating their quantity in an indoor environment has grown in importance recently. For example, the information if a room is unoccupied can be used for automatically switching off the light, air conditioning, and ventilation, thereby saving significant amounts of energy in public buildings. Most existing solutions rely on dedicated hardware installations, which involve presence sensors, video cameras, and carbon dioxide sensors. Unfortunately, such approaches are costly, subject to privacy concerns, have high computational requirements, and lack ubiquitousness. The work presented in this article addresses these limitations by proposing a low-cost occupancy detection system. Our approach builds upon detecting variations in Bluetooth Low Energy (BLE) signals related to the presence of humans. The effectiveness of this approach is evaluated by performing comprehensive tests on five different datasets. We apply several pattern recognition models and compare our methodology with systems building upon IEEE 802.11 (WiFi). On average, in multifarious environments, we can correctly classify the occupancy with an accuracy of 97.97%. When estimating the number of people in a room, on average, the estimated number of subjects differs from the actual one by 0.32 persons. We conclude that our system's performance is comparable to that of existing ones based on WiFi, while significantly reducing cost and installation effort. Hence, our approach makes occupancy detection practical for real-world deployments
Non-invasive monitoring of Alzheimer's patients through WiFi channel state information
No full textThe design of noninvasive systems for monitoring people's activities is becoming of central interest in recent years. Such systems are essential for those affected by diseases that modify their cognitive status and are not collaborative in using wearable or interactive systems (e.g., mobile apps to communicate). This is particularly true regarding neurodegenerative diseases that involve memory loss, cognitive decline, communication difficulties, behavioral changes, loss of independence, and physical complications. In response to the need of healthcare structures and caregivers to monitor this category of people during their in-home daily life, this paper proposes a nonintrusive system capable of detecting whether or not a person is in his/her room and if he/she is lying on the bed. Checking these conditions is of utmost importance, in particular, during the night to support the monitoring activity of caregivers and social-health operators taking care of people with Dementia and Alzheimer's disease. The proposed system exploits WiFi's Channel State Information (CSI) gathered by common access points installed in the room. CSI data are then used to train a Convolutional Neural Network (CNN) and a fine-tuning technique is applied to increase the generalization capabilities of the CNN model on new environments. In our experimental analysis, we trained the CNN model by collecting CSI data in four different rooms, from two subjects performing three distinct activities. Promising results have been achieved (accuracy > 97.5%) in recognizing the target activities
An indoor localization system to detect areas causing the freezing of gait in Parkinsonians
No full textTowards the automatic data annotation for human activity recognition based on wearables and BLE beacons
No full textIn machine learning, the data annotation process is an essential, but error-prone and time-consuming manual activity, which associates metadata to the samples of a dataset. In the context of Human Activity Recognition (HAR) this generally reflects in a human watching the video recordings of the activities carried out by the target user to assign a label to each video frame. The label can refer, for example, to the nature of the performed activity, or to the time series collected through sensors worn by the user or present in the environment. This paper deals with the automation of the data annotation process in the HAR context by presenting a methodology that (i) maps Bluetooth Low Energy (BLE) beacons distributed in the environment to the locations where a human typically performs activities like eating, cooking, working, and resting, and (ii) associates the data collected by sensors embedded in the smartwatch worn by the user (i.e., acceleration, angular velocity, and magnetometer) to the nearest BLE beacon. In this way, data gathered through the smartwatch are automatically annotated with the human activity associated to the nearest beacon
A low-cost BLE-based distance estimation, occupancy detection and counting system
No full textThis 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
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