Heart rate measurement using the built-in triaxial accelerometer from a commercial digital writing device

Wearable devices are on the rise. Smart watches and phones, fitness trackers or smart textiles now provide unprecedented access to our own personal data. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor the heart's and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heartrate when they are attached to the chest. They can also help filter out some noise in ECG signal from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen), with a standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is done with eight volunteers, writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685x10$^{-3}$ comparing the DigiPen's computed ${\Delta}$t (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates from the pen accurately correlate with the reference ECG signals

A Two-Level Approach to Characterizing Human Activities from Wearable Sensor Data

International audienceThe rapid emergence of new technologies in recent decades has opened up a world of opportunities for a better understanding of human mobility and behavior. It is now possible to recognize human movements, physical activity and the environments in which they take place. And this can be done with high precision, thanks to miniature sensors integrated into our everyday devices. In this paper, we explore different methodologies for recognizing and characterizing physical activities performed by people wearing new smart devices. Whether it's smartglasses, smartwatches or smartphones, we show that each of these specialized wearables has a role to play in interpreting and monitoring moments in a user's life. In particular, we propose an approach that splits the concept of physical activity into two sub-categories that we call micro-and macro-activities. Micro-and macro-activities are supposed to have functional relationship with each other and should therefore help to better understand activities on a larger scale. Then, for each of these levels, we show different methods of collecting, interpreting and evaluating data from different sensor sources. Based on a sensing system we have developed using smart devices, we build two data sets before analyzing how to recognize such activities. Finally, we show different interactions and combinations between these scales and demonstrate that they have the potential to lead to new classes of applications, involving authentication or user profiling

Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey

Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring. These systems enable individuals to increase their independence by allowing them to monitor their health from the home and by allowing more control over their well-being. In this study, we perform a comprehensive survey on this topic by reviewing a large number of literature in the area. We investigate these systems from various aspects, namely sensing technologies, communication technologies, intelligent and computing systems, and application areas. Specifically, we provide an overview of in-home health monitoring systems and identify their main components. We then present each component and discuss its role within in-home health monitoring systems. In addition, we provide an overview of the practical use of ubiquitous technologies in the home for health monitoring. Finally, we identify the main challenges and limitations based on the existing literature and provide eight recommendations for potential future research directions toward the development of in-home health monitoring systems. We conclude that despite extensive research on various components needed for the development of effective in-home health monitoring systems, the development of effective in-home health monitoring systems still requires further investigation.Comment: 35 pages, 5 figure

Design for energy-efficient and reliable fog-assisted healthcare IoT systems

Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease

Development of wireless prototype vehicle speed monitoring system.

Globally road accident is considered to be an important issue, which can be reduced by proper vehicle speed monitoring system. More recently, the advancement in wireless sensor technology shows a great promise in designing Intelligent Transportation System (ITS) due to its flexibility and cost-effectiveness for deployment. The aim of this research is to develop a prototype vehicle speed monitoring system using accelerometer-based wireless sensor. The basic concept of the system is based on the following methodology: developing an experimental system to generate random speed data, which can represent vehicle speed on the road and developing a software to monitor and manage the speed data wirelessly. A wireless sensor attached with a mechanical wheel measures the acceleration vibration of the system, which is equivalent to wheel speed and transmits the data wirelessly to a computer. A software (SpeedManage) has been developed using Java Socket programming codes which converts the vibration data to equivalent speed data and presents these in a Graphical User Interface (GUI). If the detected speed is greater than a set speed limit, the data will be automatically saved in a central database in the form of an electronic report for taking any further action. The functionality of the system has been simulated in a laboratory environment by setting different speed limits for monitoring single or multiple vehicle speed scenarios through appropriate algorithm and code development. The graphical user interface (GUI) of the software continuously presents the vehicle speeds with time and the overspeeding conditions are indicated. The speed details are also continuously updated on the left hand side of the GUI. The system is also capable of generating an automatic electronic report for a simulated speeding vehicle with vehicle number, speed details, time etc. Therefore, based on the performance of prototype system, it can be concluded that sensor-based vehicle speed monitoring system has great potential for monitoring vehicle speed wirelessly. SpeedManage software should help to effectively, automatically and intelligently monitor vehicle speed

Handling Live Sensor Data on the Semantic Web

The increased linking of objects in the Internet of Things and the ubiquitous flood of data and information require new technologies in data processing and data storage in particular in the Internet and the Semantic Web. Because of human limitations in data collection and analysis, more and more automatic methods are used. Above all, these sensors or similar data producers are very accurate, fast and versatile and can also provide continuous monitoring even places that are hard to reach by people. The traditional information processing, however, has focused on the processing of documents or document-related information, but they have different requirements compared to sensor data. The main focus is static information of a certain scope in contrast to large quantities of live data that is only meaningful when combined with other data and background information. The paper evaluates the current status quo in the processing of sensor and sensor-related data with the help of the promising approaches of the Semantic Web and Linked Data movement. This includes the use of the existing sensor standards such as the Sensor Web Enablement (SWE) as well as the utilization of various ontologies. Based on a proposed abstract approach for the development of a semantic application, covering the process from data collection to presentation, important points, such as modeling, deploying and evaluating semantic sensor data, are discussed. Besides the related work on current and future developments on known diffculties of RDF/OWL, such as the handling of time, space and physical units, a sample application demonstrates the key points. In addition, techniques for the spread of information, such as polling, notifying or streaming are handled to provide examples of data stream management systems (DSMS) for processing real-time data. Finally, the overview points out remaining weaknesses and therefore enables the improvement of existing solutions in order to easily develop semantic sensor applications in the future

Validation of a cat activity monitor

Early detection of diseases and injuries in animals is crucial for their health and well-being. Early diagnosis can be assisted by objective registration of different types of physical activities or behaviour patterns. Monitoring specific parameters, such as changes in activity levels or habits, could serve as an indicator of underlying health issues. It can be challenging for pet owners to notice subtle changes in those characteristics at an early stage. It becomes more difficult in the case of parameters of a low frequency of occurrence, such as drinking and littering behaviours. Hydration status is extremely important in cats and changes in drinking and littering patterns could be early symptoms of potential disorders, in particular diabetes mellitus. There is a noticeable increase in owners’ awareness about the physical and mental health of their pets. With a growing demand for higher standards of tools to assess animals’ everyday habits, more technologies are being developed. Activity monitors utilizing accelerometers provide broad and continuous measures of physical activity, that enable remote and non-invasive monitoring of an individual’s actions. The aim of this study was to validate the registrations of an activity monitor. Specifically, the study aimed to assess the effectiveness of the activity monitor in detecting drinking and littering activities, which might suggest underlying health issues. To monitor these activities, this study used an activity monitor equipped with an accelerometer and attached to a collar. The validity and effectiveness of the activity monitor were established by comparing the measurements obtained from the activity collar to video recordings from the motion sensor camera. For forty-eight days, activity data on drinking and littering actions were collected from a single adult cat. Descriptive statistics were performed to summarize the main findings of the dataset to obtain key results. From the total of 5989 recordings registered by the motion sensor camera, 671 recordings containing actions of drinking and littering were selected for further analysis. Accordingly, 53 recordings were extracted from the activity monitor. This study found no correlation between the data obtained from the activity monitor and the video observations from the motion sensor camera. Further research is needed to investigate the reasons behind this lack of agreement and to improve methodologies for monitoring feline activities using activity monitors. Despite underwhelming findings, it should not rule out all potential applications in monitoring feline behaviors, managing health disorders, and promoting overall health remain promising

Monitoring of urban transportation networks using wireless sensor networks

Dissertação de mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015With today’s technologies, constant growth of people in major cities and the permanent concern with our natural resources, such as energy, it is inevitable that we look for ways to improve our urban transportation networks. One way of improving transportation networks is to make an efficient vehicle routing. In this thesis vehicle routing with backup provisioning, using wireless sensor technologies, is proposed. We start by collecting the entry and exit of people inside urban transportations, which will give us a view of fleet load over time, using wireless sensor technologies. For this purpose a monitoring software was developed. Such data gathering and monitoring tool will allow data analysis in real time and, according to information extracted, to propose solutions for service improvements. In this thesis the possibility of using such data to plan vehicle routes with backup provisioning is discussed. That is, a variant of the open vehicle routing problem is proposed, called vehicle routing with backup provisioning, where the possibility of reacting to overloading/overcrowding of vehicles in certain stops is considered. After mathematically formalizing the problem a heuristic algorithm to plan routes is proposed. Results show that vehicle routing with backup provisioning can be a way of providing sustainable urban mobility with efficient use of resources, while increasing quality of service perceived by users. We expect this tool to be useful for the improvement of Urban Transportation Networks

Embedding a Grid of Load Cells into a Dining Table for Automatic Monitoring and Detection of Eating Events

This dissertation describes a “smart dining table” that can detect and measure consumption events. This work is motivated by the growing problem of obesity, which is a global problem and an epidemic in the United States and Europe. Chapter 1 gives a background on the economic burden of obesity and its comorbidities. For the assessment of obesity, we briefly describe the classic dietary assessment tools and discuss their drawback and the necessity of using more objective, accurate, low-cost, and in-situ automatic dietary assessment tools. We explain in short various technologies used for automatic dietary assessment such as acoustic-, motion-, or image-based systems. This is followed by a literature review of prior works related to the detection of weights and locations of objects sitting on a table surface. Finally, we state the novelty of this work. In chapter 2, we describe the construction of a table that uses an embedded grid of load cells to sense the weights and positions of objects. The main challenge is aligning the tops of adjacent load cells to within a few micrometer tolerance, which we accomplish using a novel inversion process during construction. Experimental tests found that object weights distributed across 4 to 16 load cells could be measured with 99.97±0.1% accuracy. Testing the surface for flatness at 58 points showed that we achieved approximately 4.2±0.5 um deviation among adjacent 2x2 grid of tiles. Through empirical measurements we determined that the table has a 40.2 signal-to-noise ratio when detecting the smallest expected intake amount (0.5 g) from a normal meal (approximate total weight is 560 g), indicating that a tiny amount of intake can be detected well above the noise level of the sensors. In chapter 3, we describe a pilot experiment that tests the capability of the table to monitor eating. Eleven human subjects were video recorded for ground truth while eating a meal on the table using a plate, bowl, and cup. To detect consumption events, we describe an algorithm that analyzes the grid of weight measurements in the format of an image. The algorithm segments the image into multiple objects, tracks them over time, and uses a set of rules to detect and measure individual bites of food and drinks of liquid. On average, each meal consisted of 62 consumption events. Event detection accuracy was very high, with an F1-score per subject of 0.91 to 1.0, and an F1 score per container of 0.97 for the plate and bowl, and 0.99 for the cup. The experiment demonstrates that our device is capable of detecting and measuring individual consumption events during a meal. Chapter 4 compares the capability of our new tool to monitor eating against previous works that have also monitored table surfaces. We completed a literature search and identified the three state-of-the-art methods to be used for comparison. The main limitation of all previous methods is that they used only one load cell for monitoring, so only the total surface weight can be analyzed. To simulate their operations, the weights of our grid of load cells were summed up to use the 2D data as 1D. Data were prepared according to the requirements of each method. Four metrics were used to evaluate the comparison: precision, recall, accuracy, and F1-score. Our method scored the highest in recall, accuracy, and F1-score; compared to all other methods, our method scored 13-21% higher for recall, 8-28% higher for accuracy, and 10-18% higher for F1-score. For precision, our method scored 97% that is just 1% lower than the highest precision, which was 98%. In summary, this dissertation describes novel hardware, a pilot experiment, and a comparison against current state-of-the-art tools. We also believe our methods could be used to build a similar surface for other applications besides monitoring consumption