Engineering 4.0 to Improve the Safety of Plant Operators in a Metalworking Company of International Importance: The Ansaldo Energia Case

The paper describes how a multidisciplinary team has developed, on behalf of Ansaldo Energia Spa, a methodology based on the technologies made available by Industry 4.0; a methodology that allows rescue teams to quickly intervene in the event of man-down in isolated areas of the plant where the unfortunate person would risk being found with significant delay and consequent problems for his physical well-being. To achieve this result, an appropriate hardware and software device has been developed by a highly specialized supplier, under the direction of the team. Such a device makes it possible to alert automatically rescue teams in real-time, at the occurrence of the event, and geo-locate, with extreme precision, the man on the ground. The methodology, once devised, has been standardized in a series of sequential and generalized steps, in order to make it applicable to any type of company or construction site, or workshop in which the event of the man-down may occur. The methodology is configured as a real toolkit for the protection of operators from damage, even extreme, that can derive from prolonged waits of the rescue teams, each time that operators incur negative events for their safety, whether they are exogenous (illnesses with fainting, heart attacks, epileptic attacks, strokes...) and endogenous (accidents in the workplace)

The Emerging Wearable Solutions in mHealth

The marriage of wearable sensors and smartphones have fashioned a foundation for mobile health technologies that enable healthcare to be unimpeded by geographical boundaries. Sweeping efforts are under way to develop a wide variety of smartphone-linked wearable biometric sensors and systems. This chapter reviews recent progress in the field of wearable technologies with a focus on key solutions for fall detection and prevention, Parkinson’s disease assessment and cardiac disease, blood pressure and blood glucose management. In particular, the smartphone-based systems, without any external wearables, are summarized and discussed

Analysis of Android Device-Based Solutions for Fall Detection

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.Ministerio de Economía y Competitividad TEC2013-42711-

SoC as IoT sensor network hub

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Personalized fall detection monitoring system based on learning from the user movements

Personalized fall detection system is shown to provide added and more benefits compare to the current fall detection system. The personalized model can also be applied to anything where one class of data is hard to gather. The results show that adapting to the user needs, improve the overall accuracy of the system. Future work includes detection of the smartphone on the user so that the user can place the system anywhere on the body and make sure it detects. Even though the accuracy is not 100% the proof of concept of personalization can be used to achieve greater accuracy. The concept of personalization used in this paper can also be extended to other research in the medical field or where data is hard to come by for a particular class. More research into the feature extraction and feature selection module should be investigated. For the feature selection module, more research into selecting features based on one class data

Smartphone - Smartwatch Based Automatic Car Crash Detection & Emergency Notification Application

Η πτυχιακή αυτή αποτελεί μια λεπτομερή ανάλυση του τρόπου ανάπτυξης μιας εφαρμογής σχεδιασμένης να υποστηριχθεί από iPhone και Apple Watch συσκευές, που έχει σαν πρωταρχικό της μέλλημα να σώσει ανθρώπινες ζωές από τροχαία ατυχήματα. Πιο συγκεριμένα, στο κύριο κομμάτι της εφαρμογής, στοιχεία που συλλέγονται συνεχώς από τους εξής αισθητήρες της συσκευής: GPS, Ταχύμετρο, Γυροσκόπειο και Μικρόφωνο αναλύονται με σκοπό τον εντοπισμό κάποιας ασυνίθιστης μεταβολής κάποιου εξογενούς παράγοντα και την ενημέρωση ,κατόπιν, των επαφών έκακτης ανάγκης. Επιπροσθέτως, οι πληροφορίες όλων των ατυχημάτων καταγράφονται σε μια διαδικτυακή βάση δεδομένων για εκτενέστερη μελέτη των συνθηκών.This thesis is a complete documentation of the development of an application running on both iPhone and Apple Watch devices dedicated to save human lives from road accidents. More specifically, the main part of the application analyses data collected by device’s GPS, gyroscope and microphone so as to detect any unusual incident and finally notifies user’s emergency contacts whenever it is needed. Additionally, it stores all the details about all accidents in an online database for further inspection

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare

Analysis of a hybrid Android system for fall detection

Android personal devices have become an interesting and cost-effective technology to deploy wearable Fall Detection Systems. In contrast with other smartphone-based solutions, this paper describes a fall detection architecture that integrates two-Bluetooth enabled devices: a smartwatch and a smartphone. The evaluation of the system under different fall recognition algorithms and mobility patterns indicates that the simultaneous operation of the two devices as fall detectors clearly improves the specificity of the system when compared to the cases where just one device is employed as a fall detector. The performed analysis also encompasses the study of the battery consumption and the performance of the system under constant monitoring in everyday life conditions.Universidad de Malaga, Campus de Excelencia Internacional Andalucia Tech. This work was supported by European FEDER funds and the Spanish Ministry of Economy and Competitiveness [grant TEC2013-42711-R] (URL: http://www.mineco.gob.es/portal/site/mineco/