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

A Study on Human Fall Detection Systems: Daily Activity Classification and Sensing Techniques

Fall detection for elderly is a major topic as far as assistive technologies are concerned. This is due to the high demand for the products and technologies related to fall detection with the ageing population around the globe. This paper gives a review of previous works on human fall detection devices and a preliminary results from a developing depth sensor based device. The three main approaches used in fall detection devices such as wearable based devices, ambient based devices and vision based devices are identified along with the sensors employed.  The frameworks and algorithms applied in each of the approaches and their uniqueness is also illustrated. After studying the performance and the shortcoming of the available systems a future solution using depth sensor is also proposed with preliminary results

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

A protected discharge facility for the elderly: design and validation of a working proof-of-concept

With the increasing share of elderly population worldwide, the need for assistive technologies to support clinicians in monitoring their health conditions is becoming more and more relevant. As a quantitative tool, geriatricians recently proposed the notion of frail elderly, which rapidly became a key element of clinical practices for the estimation of well-being in aging population. The evaluation of frailty is commonly based on self-reported outcomes and occasional physicians evaluations, and may therefore contain biased results. Another important aspect in the elderly population is hospitalization as a risk factor for patient\u2019s well being and public costs. Hospitalization is the main cause of functional decline, especially in older adults. The reduction of hospitalization time may allow an improvement of elderly health conditions and a reduction of hospital costs. Furthermore, a gradual transition from a hospital environment to a home-like one, can contribute to the weaning of the patient from a condition of hospitalization to a condition of discharge to his home. The advent of new technologies allows for the design and implementation of smart environments to monitor elderly health status and activities, fulfilling all the requirements of health and safety of the patients. From these starting points, in this thesis I present data-driven methodologies to automatically evaluate one of the main aspects contributing to the frailty estimation, i.e., the motility of the subject. First I will describe a model of protected discharge facility, realized in collaboration and within the E.O. Ospedali Galliera (Genoa, Italy), where patients can be monitored by a system of sensors while physicians and nurses have the opportunity to monitor them remotely. This sensorised facility is being developed to assist elderly users after they have been dismissed from the hospital and before they are ready to go back home, with the perspective of coaching them towards a healthy lifestyle. The facility is equipped with a variety of sensors (vision, depth, ambient and wearable sensors and medical devices), but in my thesis I primarily focus on RGB-D sensors and present visual computing tools to automatically estimate motility features. I provide an extensive system assessment I carried out onthree different experimental sessions with help of young as well as healthy aging volunteers. The results I present are in agreement with the assessment manually performed by physicians, showing the potential capability of my approach to complement current protocols of evaluation

Advances in transfer learning methods based on computational intelligence

Traditional machine learning and data mining have made tremendous progress in many knowledge-based areas, such as clustering, classification, and regression. However, the primary assumption in all of these areas is that the training and testing data should be in the same domain and have the same distribution. This assumption is difficult to achieve in real-world applications due to the limited availability of labeled data. Associated data in different domains can be used to expand the availability of prior knowledge about future target data. In recent years, transfer learning has been used to address such cross-domain learning problems by using information from data in a related domain and transferring that data to the target task. The transfer learning methodology is utilized in this work with unsupervised and supervised learning methods. For unsupervised learning, a novel transfer-learning possibilistic c-means (TLPCM) algorithm is proposed to handle the PCM clustering problem in a domain that has insufficient data. Moreover, TLPCM overcomes the problem of differing numbers of clusters between the source and target domains. The proposed algorithm employs the historical cluster centers of the source data as a reference to guide the clustering of the target data. The experimental studies presented here were thoroughly evaluated, and they demonstrate the advantages of TLPCM in both synthetic and real-world transfer datasets. For supervised learning, a transfer learning (TL) technique is used to pre-train a CNN model on posture data and then fine-tune it on the sleep stage data. We used a ballistocardiography (BCG) bed sensor to collect both posture and sleep stage data to provide a non-invasive, in-home monitoring system that tracks changes in the subjects' health over time. The quality of sleep has a significant impact on health and life. This study adopts a hierarchical and none-hierarchical classification structure to develop an automatic sleep stage classification system using ballistocardiogram (BCG) signals. A leave-one-subject-out cross-validation (LOSO-CV) procedure is used for testing classification performance in most of the experiments. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Deep Neural Networks DNNs are complementary in their modeling capabilities, while CNNs have the advantage of reducing frequency variations, LSTMs are good at temporal modeling. Polysomnography (PSG) data from a sleep lab was used as the ground truth for sleep stages, with the emphasis on three sleep stages, specifically, awake, rapid eye movement (REM), and non-REM sleep (NREM). Moreover, a transfer learning approach is employed with supervised learning to address the cross-resident training problem to predict early illness. We validate our method by conducting a retrospective study on three residents from TigerPlace, a retirement community in Columbia, MO, where apartments are fitted with wireless networks of motion and bed sensors. Predicting the early signs of illness in older adults by using a continuous, unobtrusive nursing home monitoring system has been shown to increase the quality of life and decrease care costs. Illness prediction is based on sensor data and uses algorithms such as support vector machine (SVM) and k-nearest neighbors (kNN). One of the most significant challenges related to the development of prediction algorithms for sensor networks is the use of knowledge from previous residents to predict new ones' behaviors. Each day, the presence or absence of illness was manually evaluated using nursing visit reports from a homegrown electronic medical record (EMR) system. In this work, the transfer learning SVM approach outperformed three other methods, i.e., regular SVM, one-class SVM, and one-class kNN.Includes bibliographical references (pages 114-127)

Rf sensing and processing methods for noninvasive health monitoring

Vulnerable populations include groups of people with a higher risk of poor health as a result of the limitations due to illness or disability. The health issues of vulnerable populations include three categories: physical, psychological, and social. The people with physical issues include high-risk mothers and infants, older adults and others with chronic illnesses and people with disabilities. The psychological issues of vulnerable populations include chronic mental conditions, such as bipolar disorder, major depression, and hyperactivity disorder, as well as substance abuse and those who are suicidal. The social issues in vulnerable populations include those living in abusive families, the homeless, etc. This dissertation concentrates on methods for helping two groups of vulnerable populations, namely, frail older adults and psychiatric hospital patients, to monitor their activity level, respiration rate, sleeping quality, and restless time in bed. In the first part of our work, we investigate a contactless monitoring system for psychiatric patients in a naturalistic hospital setting that can track their motion in bed, estimate the breathing rate of patients during their peaceful sleeping periods, and can be used to estimate a patient's restless time and sleep quality. Specifically, the contactless monitoring system uses a Vayyar Radar system with a carrier frequency of 6.014 GHz to capture all reflections by the FMCW (frequency modulation continuous waveform) signal. The Vayyar Radar system has been installed in a Psychiatric Center to capture 12 nights with over 135 hours of data from 7 patients. A depth camera and a thermal camera have also been installed and are used as the ground truth. The goal is to classify in bed and out of bed classes, quantify restlessness in bed, and determine the breathing rate while patients are lying in bed. We have simulated the psychiatric hospital set-up in the lab, where a respiration belt is used for ground truth, and tested the system with body postures of patients observed in the psychiatric hospital. We estimated respiration rate with different sleep postures, with the aim of investigating a contactless monitoring system for psychiatric patients in the hospital that can estimate the breathing rate of patients during typical sleeping postures, and find the torso area when the patients use other postures, such as reading books in bed or reversing the body on the bed. In the second part of our work, we investigate two methods for learning the room structure via radio wave reflections for longitudinal health monitoring of older adults in a naturalistic home setting. The goal is to use these data as part of a monitoring system that can be easily installed in a home with minimal configuration, for the purpose of detecting very early signs of illness and functional decline. Two studies are conducted using RF (radio frequency) sensing. The first method learns the structure from the RF clutter patterns and uses the beat frequency of the maximum peak in each chirp to calculate the wall position. The second method learns the room structure from active movement patterns and uses the open space between the clusters of active movement patterns to estimate the possible wall locations. Comparing the two results from these methods provides a more robust wall location. In addition, a background filter is designed based on the wall position, and the activity level of people in different rooms is estimated using a fuzzy rule system applied to the RF motion data

Guest Editorial Cardiovascular Health Informatics: Risk Screening and Intervention

Despite enormous efforts to prevent cardiovascular disease (CVD) in the past, it remains the leading cause of death in most countries worldwide. Around two-thirds of these deaths are due to acute events, which frequently occur suddenly and are often fatal beforemedical care can be given. New strategies for screening and early intervening CVD, in addition to the conventional methods, are therefore needed in order to provide personalized and pervasive healthcare. In this special issue, selected emerging technologies in health informatics for screening and intervening CVDs are reported. These papers include reviews or original contributions on 1) new potential genetic biomarkers for screening CVD outcomes and high-throughput techniques for mining genomic data; 2) new imaging techniques for obtaining faster and higher resolution images of cardiovascular imaging biomarkers such as the cardiac chambers and atherosclerotic plaques in coronary arteries, as well as possible automatic segmentation, identification, or fusion algorithms; 3) new physiological biomarkers and novel wearable and home healthcare technologies for monitoring them in daily lives; 4) new personalized prediction models of plaque formation and progression or CVD outcomes; and 5) quantifiable indices and wearable systems to measure them for early intervention of CVD through lifestyle changes. It is hoped that the proposed technologies and systems covered in this special issue can result in improved CVD management and treatment at the point of need, offering a better quality of life to the patient

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

The aim of radio-frequency (RF) sensing for assisted living is to deliver automatic support and monitoring for older people in their homes, impaired patients living independently, individuals in need of continuous support, and people suffering from chronic diseases that require them to stay in care-homes or at hospitals. RF sensing technologies have the potential to improve the quality of living of elderly people or disabled individuals in need of timely assistance. This paper provides a comprehensive review on three of the most innovative RF sensing technologies for activities of daily living in healthcare sector (namely active radar, passive radar, and wireless channel information and RFID sensing) and presents some of the open challenges that need to be addressed