Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Wearable Sensor Gait Analysis for Fall Detection Using Deep Learning Methods

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide range of gait variations in older. Choosing the appropriate sensor and placing it in the most suitable location are essential components of a robust real-time fall detection system. This dissertation implements various detection models to analyze and mitigate injuries due to falls in the senior community. It presents different methods for detecting falls in real-time using deep learning networks. Several sliding window segmentation techniques are developed and compared in the first study. As a next step, various methods are implemented and applied to prevent sampling imbalances caused by the real-world collection of fall data. A study is also conducted to determine whether accelerometers and gyroscopes can distinguish between falls and near-falls. According to the literature survey, machine learning algorithms produce varying degrees of accuracy when applied to various datasets. The algorithm’s performance depends on several factors, including the type and location of the sensors, the fall pattern, the dataset’s characteristics, and the methods used for preprocessing and sliding window segmentation. Other challenges associated with fall detection include the need for centralized datasets for comparing the results of different algorithms. This dissertation compares the performance of varying fall detection methods using deep learning algorithms across multiple data sets. Furthermore, deep learning has been explored in the second application of the ECG-based virtual pathology stethoscope detection system. A novel real-time virtual pathology stethoscope (VPS) detection method has been developed. Several deep-learning methods are evaluated for classifying the location of the stethoscope by taking advantage of subtle differences in the ECG signals. This study would significantly extend the simulation capabilities of standard patients by allowing medical students and trainees to perform realistic cardiac auscultation and hear cardiac auscultation in a clinical environment

Detection of postural transitions using machine learning

The purpose of this project is to study the nature of human activity recognition and prepare a dataset from volunteers doing various activities which can be used for constructing the various parts of a machine learning model which is used to identify each volunteers posture transitions accurately. This report presents the problem definition, equipment used, previous work in this area of human activity recognition and the resolution of the problem along with results. Also this report sheds light on the process and the steps taken to undertake this endeavour of human activity recognition such as building of a dataset, pre-processing the data by applying filters and various windowing length techniques, splitting the data into training and testing data, performance of feature selection and feature extraction and finally selecting the model for training and testing which provides maximum accuracy and least misclassification rates. The tools used for this project includes a laptop equipped with MATLAB and EXCEL and MEDIA PLAYER CLASSIC respectively which have been used for data processing, model training and feature selection and Labelling respectively. The data has been collected using an Inertial Measurement Unit contains 3 tri-axial Accelerometers, 1 Gyroscope, 1 Magnetometer and 1 Pressure sensor. For this project only the Accelerometers, Gyroscope and the Pressure sensor is used. The sensor is made by the members of the lab named ‘The Technical Research Centre for Dependency Care and Autonomous Living (CETpD) at the UPC-ETSEIB campus. The results obtained have been satisfactory, and the objectives set have been fulfilled. There is room for possible improvements through expanding the scope of the project such as detection of chronic disorders or providing posture based statistics to the end user or even just achieving a higher rate of sensitivity of transitions of posture by using better features and increasing the dataset size by increasing the number of volunteers.Incomin

Estimating Epipolar Geometry With The Use of a Camera Mounted Orientation Sensor

Context: Image processing and computer vision are rapidly becoming more and more commonplace, and the amount of information about a scene, such as 3D geometry, that can be obtained from an image, or multiple images of the scene is steadily increasing due to increasing resolutions and availability of imaging sensors, and an active research community. In parallel, advances in hardware design and manufacturing are allowing for devices such as gyroscopes, accelerometers and magnetometers and GPS receivers to be included alongside imaging devices at a consumer level. Aims: This work aims to investigate the use of orientation sensors in the field of computer vision as sources of data to aid with image processing and the determination of a scene’s geometry, in particular, the epipolar geometry of a pair of images - and devises a hybrid methodology from two sets of previous works in order to exploit the information available from orientation sensors alongside data gathered from image processing techniques. Method: A readily available consumer-level orientation sensor was used alongside a digital camera to capture images of a set of scenes and record the orientation of the camera. The fundamental matrix of these pairs of images was calculated using a variety of techniques - both incorporating data from the orientation sensor and excluding its use Results: Some methodologies could not produce an acceptable result for the Fundamental Matrix on certain image pairs, however, a method described in the literature that used an orientation sensor always produced a result - however in cases where the hybrid or purely computer vision methods also produced a result - this was found to be the least accurate. Conclusion: Results from this work show that the use of an orientation sensor to capture information alongside an imaging device can be used to improve both the accuracy and reliability of calculations of the scene’s geometry - however noise from the orientation sensor can limit this accuracy and further research would be needed to determine the magnitude of this problem and methods of mitigation

Movement-based Group Awareness with Wireless Sensor Networks

We propose a method through which dynamic sensor nodes determine that they move together, by communicating and correlating their movement information. We describe two possible solutions, one using inexpensive tilt switches, and another one using low-cost MEMS accelerometers. We implement a fast, incremental correlation algorithm, with an execution time of 6ms, which can run on resource constrained devices. The tests with the implementation on real sensor nodes show that the method is reliable and distinguishes between joint and separate movements. In addition, we analyze the scalability from four different perspectives: communication, energy, memory and execution speed. The solution using tilt switches proves to be simpler, cheaper and more energy efficient, while the accelerometer-based solution is more reliable, more robust to sensor alignment problems and, potentially, more accurate by using extended features, such as speed and distance

A Study and Estimation a Lost Person Behavior in Crowded Areas Using Accelerometer Data from Smartphones

As smartphones become more popular, applications are being developed with new and innovative ways to solve problems in the day-to-day lives of users. One area of smartphone technology that has been developed in recent years is human activity recognition (HAR). This technology uses various sensors that are built into the smartphone to sense a person\u27s activity in real time. Applications that incorporate HAR can be used to track a person\u27s movements and are very useful in areas such as health care. We use this type of motion sensing technology, specifically, using data collected from the accelerometer sensor. The purpose of this study is to study and estimate the person who may become lost in a crowded area. The application is capable of estimating the movements of people in a crowded area, and whether or not the person is lost in a crowded area based on his/her movements as detected by the smartphone. This will be a great benefit to anyone interested in crowd management strategies. In this paper, we review related literature and research that has given us the basis for our own research. We also detail research on lost person behavior. We looked at the typical movements a person will likely make when he/she is lost and used these movements to indicate lost person behavior. We then evaluate and describe the creation of the application, all of its components, and the testing process