Privacy Leakage of Physical Activity Levels in Wireless Embedded Wearable Systems

International audienceWith the ubiquity of sensing technologies in our personal spaces, the protection of our privacy and the confidentiality of sensitive data becomes a major concern. In this paper, we focus on wearable embedded systems that communicate data periodically over the wireless medium. In this context, we demonstrate that private information about the physical activity levels of the wearer can leak to an eavesdropper through the physical layer. Indeed, we show that the physical activity levels strongly correlate with changes in the wireless channel that can be captured by measuring the signal strength of the eavesdropped frames. We practically validate this correlation in several scenarios in a real residential environment, using data collected by our prototype wearable accelerometer-based sensor. Lastly, we propose a privacy enhancement algorithm that mitigates the leakage of this private information

Generalised and Versatile Connected Health Solution on the Zynq SoC

This chapter presents a generalized and versatile connected health solution for patient monitoring. It consists of a mobile system that can be used at home, an ambulance and a hospital. The system uses the Shimmer sensor device to collect three axes (x, y and z) accelerometer data as well as electrocardiogram signals. The accelerometer data is used to implement a fall detection system using the k-Nearest Neighbors classifier. The classification algorithm is implemented on various platform including a PC and the Zynq system on chip platform where both programmable logic and processing system of the Zynq are explored. In addition, the electrocardiogram signals are used to extract vital information, the signals are also encrypted using the Advanced Encryption Standard and sent wirelessly using Wi-Fi for further processing. Implementation results have shown that the best overall accuracy reaches 90% for the fall detection while meeting real-time performances when implemented on the Zynq and while using only 48% of Look-up Tables and 22% of Flip-Flops available on chip

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results.Universidad de Malaga Plan Propio-Universidad de MalagaSpanish GovernmentEuropean Commission RTI2018-098371-B-I0

Review of current study methods for VRU safety : Appendix 4 –Systematic literature review: Naturalistic driving studies

With the aim of assessing the extent and nature of naturalistic studies involving vulnerable road users, a systematic literature review was carried out. The purpose of this review was to identify studies based on naturalistic data from VRUs (pedestrians, cyclists, moped riders and motorcyclists) to provide an overview of how data was collected and how data has been used. In the literature review, special attention is given to the use of naturalistic studies as a tool for road safety evaluations to gain knowledge on methodological issues for the design of a naturalistic study involving VRUs within the InDeV project. The review covered the following types of studies: •Studies collecting naturalistic data from vulnerable road users (pedestrians, cyclists, moped riders, motorcyclists). •Studies collecting accidents or safety-critical situations via smartphones from vulnerable road users and motorized vehicles. •Studies collecting falls that have not occurred on roads via smartphones. Four databases were used in the search for publications: ScienceDirect, Transport Research International Documentation (TRID), IEEE Xplore and PubMed. In addition to these four databases, six databases were screened to check if they contained references to publications not already included in the review. These databases were: Web of Science, Scopus, Google Scholar, Springerlink, Taylor & Francis and Engineering Village.The findings revealed that naturalistic studies of vulnerable road users have mainly been carried out by collecting data from cyclists and pedestrians and to a smaller degree of motorcyclists. To collect data, most studies used the built-in sensors of smartphones, although equipped bicycles or motorcycles were used in some studies. Other types of portable equipment was used to a lesser degree, particularly for cycling studies. The naturalistic studies were carried out with various purposes: mode classification, travel surveys, measuring the distance and number of trips travelled and conducting traffic counts. Naturalistic data was also used for assessment of the safety based on accidents, safety-critical events or other safety-related aspect such as speed behaviour, head turning and obstacle detection. Only few studies detect incidents automatically based on indicators collected via special equipment such as accelerometers, gyroscopes, GPS receivers, switches, etc. for assessing the safety by identifying accidents or safety-critical events. Instead, they rely on self-reporting or manual review of video footage. Despite this, the review indicates that there is a large potential of detecting accidents from naturalistic data. A large number of studies focused on the detection of falls among elderly people. Using smartphone sensors, the movements of the participants were monitored continuously. Most studies used acceleration as indicator of falls. In some cases, the acceleration was supplemented by rotation measurements to indicate that a fall had occurred. Most studies of using kinematic triggers for detection of falls, accidents and safety-critical events were primarily used for demonstration of prototypes of detection algorithms. Few studies have been tested on real accidents or falls. Instead, simulated falls were used both in studies of vulnerable road users and for studies of falls among elderly people

Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using singleand multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.Ministerio Español de Ciencia e Innovación under project number PID2020-115570GB-C22 (DemocratAI::UGR)Cátedra de Empresa Tecnología para las Personas (UGR-Fujitsu

Elderly Fall Detection Systems: A Literature Survey

Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial

Power-efficient collaborative fall detection using a wearable device and a robot

With increasing life expectancy, the technological sub-field of elderly care is expected to rise in importance. This growing population has put a strain on current elderly care facilities and personnel. This research work is focused on designing and implementing a collaborative elderly care system (CECAS) using a companion robot and a wearable device that will enable the monitoring and companionship needed in assisted living communities and private homes. This system is meant to improve the automation in elderly care which will lead to a reduction in costs and an increase in availability. Two elderly care applications are used to demonstrate the capability of the proposed system, which includes: fall detection and response, and conversational assistance. In the fall detection and response application, a two-step method is proposed, which consists of a motion data based preliminary detection on the wearable device and a video-based final detection on the companion robot. To further reduce the operating costs and improve the system, parameters such as the image size and the neural network size are optimized through extensive experimental study. In the conversational assistance application, the robot is capable to conversate with the user to determine if external assistance is needed and acts as an extra measure of fall detection. The results of the experiments showcase that both the wearable device and the companion robot can be optimized for power-efficiency, speed, and retain fall detection performance