SaveMeNow.AI: A Machine Learning Based Wearable Device for Fall Detection in a Workplace

Anceschi, Emiliano; Bonifazi, Gianluca; Donato, Massimo Callisto De; Corradini, Enrico; Ursino, Domenico; Virgili, Luca

doi:10.1007/978-3-030-52067-0_22

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…The authors developed a neural network-based learning model that could study, track, and analyze the changes in WiFi channel state data based on normal behaviors and falls. Anceschi et al [28] proposed a machine learning-based wearable system for fall detection in a workplace environment. To develop and train the machine learning model, the authors merged four different datasets that consisted of diverse activities performed in a workplace.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A number of these methodologies have focused on activities in specific settings and cannot be seamlessly deployed in other settings consisting of different context parameters and environment variables. For instance, in [14,16], the presented systems are specific to hospital environments, the methodology presented in [21] is only applicable to a kitchen environment, and the approach in [28] is only applicable to a workplace environment. While such systems are important for safe and assisted living experiences in these local spatial contexts, their main drawback is the fact that these tools are dependent on the specific environmental settings for which they have been designed.…”

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confidence: 99%

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An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments

Thakur

Han

2021

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This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs). The framework consists of two novel functionalities. First, it can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment to detect any anomalies in user behavior that could constitute an emergency. These functionalities of this interdisciplinary framework were developed by integrating the latest advancements and technologies in human–computer interaction, machine learning, Internet of Things, pattern recognition, and ubiquitous computing. The framework was evaluated on a dataset of ADLs, and the performance accuracies of these two functionalities were found to be 76.71% and 83.87%, respectively. The presented and discussed results uphold the relevance and immense potential of this framework to contribute towards improving the quality of life and assisted living of the aging population in the future of Internet of Things (IoT)-based ubiquitous living environments, e.g., smart homes.

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Section: Literature Reviewmentioning

confidence: 99%

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confidence: 99%

An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments

Thakur

Han

2021

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“…It had to be written in the C language, with all the instructions needed for the initialization of the micro-controller and the configuration of the Machine Learning Core. In order to do this, STMicroelectronics provides two software tools (i.e., STM32CubeMX 5 and STM32CubeIDE 6 ) allowing users to develop C code for the microcontroller STM32L4R9. STM32CubeMX is a graphic tool to initialize the microcontroller peripherals, such as GPIO and USART, as well as its middlewares, like USB or TCP/IP protocols.…”

Section: Embedding the Defined Logics In Our Devicementioning

confidence: 99%

“…We would like to point out that a detailed description of a personal device for fall detection has been presented in an our recent paper [ 6 ]. This description is taken up and partly expanded in Section 4.1 of this paper.…”

Section: Introductionmentioning

confidence: 99%

A machine learning based sentient multimedia framework to increase safety at work

Bonifazi

Corradini

Ursino

et al. 2021

Multimed Tools Appl

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In the last few decades, we have witnessed an increasing focus on safety in the workplace. ICT has always played a leading role in this context. One ICT sector that is increasingly important in ensuring safety at work is the Internet of Things and, in particular, the new architectures referring to it, such as SIoT, MIoT and Sentient Multimedia Systems. All these architectures handle huge amounts of data to extract predictive and prescriptive information. For this purpose, they often make use of Machine Learning. In this paper, we propose a framework that uses both Sentient Multimedia Systems and Machine Learning to support safety in the workplace. After the general presentation of the framework, we describe its specialization to a particular case, i.e., fall detection. As for this application scenario, we describe a Machine Learning based wearable device for fall detection that we designed, built and tested. Moreover, we illustrate a safety coordination platform for monitoring the work environment, activating alarms in case of falls, and sending appropriate advices to help workers involved in falls.

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“…Physiological signals can be collected through wearable devices 24 hours a day, 7 days a week, producing big amounts of data, which are analysed through Artificial Intelligence (AI) algorithms more and more frequently, in order to provide useful information for the so-called decision-making processes [20], [21], thus supporting human choices in different fields, from Industry 4.0 [22], [23] to eHealth [24]. The purposes can be different: emotion classification [25], activity recognition [26], hypertension management [27], fall detection [28], smart living environments and well-being assessment [29], and so on. In order to be able to develop robust models, capable to provide reliable information, data quality is fundamental [30]; in this perspective, not only hardware and acquisition options (e.g.…”

Section: Introductionmentioning

confidence: 99%

The importance of physiological data variability in wearable devices for digital health applications

et al. 2022

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<p>This paper aims at characterizing the variability of physiological data collected through a wearable device (Empatica E4), given that both intra- and inter-subject variability play a pivotal role in digital health applications, where Artificial Intelligence (AI) techniques have become popular. Inter-beat intervals (IBIs), ElectroDermal Activity (EDA) and Skin Temperature (SKT) signals have been considered and variability has been evaluated in terms of general statistics (mean and standard deviation) and coefficient of variation. Results show that both intra- and inter-subject variability values are significant, especially when considering those parameters describing how the signals vary over time. Moreover, EDA seems to be the signal characterized by the highest variability, followed by IBIs, contrary to SKT that results more stable.<br />This variability could affect AI algorithms in classifying signals according to particular discriminants (e.g. emotions, daily activities, etc.), taking into account the dual role of variability: hindering a net distinction between classes, but also making algorithms more robust for deep learning purposes thanks to the consideration of a wide test population. Indeed, it is worthy to note that variability plays a fundamental role in the whole measurement chain, characterizing data reliability and impacting on the final results accuracy and consequently on decision-making processes.</p>

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SaveMeNow.AI: A Machine Learning Based Wearable Device for Fall Detection in a Workplace

Cited by 10 publications

References 31 publications

An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments

An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments

A machine learning based sentient multimedia framework to increase safety at work

The importance of physiological data variability in wearable devices for digital health applications

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