Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach

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 Review of Intelligent Sensor-Based Systems for Pressure Ulcer Prevention

Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and software approaches have been proposed to ameliorate some aspects of pressure ulcer prevention and monitoring. In this article, we focus on reviewing solutions that use sensor-based data, possibly in combination with other intrinsic or extrinsic information, processed by some form of intelligent algorithm, to provide healthcare professionals with knowledge that improves the decision-making process when dealing with a patient at risk of developing pressure ulcers. We used a systematic approach to select 21 studies that were thoroughly reviewed and summarized, considering which sensors and algorithms were used, the most relevant data features, the recommendations provided, and the results obtained after deployment. This review allowed us not only to describe the state of the art regarding the previous items, but also to identify the three main stages where intelligent algorithms can bring meaningful improvement to pressure ulcer prevention and mitigation. Finally, as a result of this review and following discussion, we drew guidelines for a general architecture of an intelligent pressure ulcer prevention system.info:eu-repo/semantics/publishedVersio

Estimation of posture and prediction of the elderly getting out of bed using a body pressure sensor

We propose an IoT support system for estimating the posture of the care recipient on the bed from the body pressure of the care recipient measured by a sheet-type body pressure sensor, and detecting the posture related to leaving the bed in real time. In addition, we propose a method that predicts getting out of the bed before the care recipient takes a posture related to getting out of the bed by considering the state transition. Intervention experiment showed that using body pressure features as an explanatory variable and applying machine learning, 16 types of postures on the bed of care recipients with an F value of 0.7 or more could be identified. From the experiment without intervention, by applying the hidden Markov model, we calculated the transition probability to each hidden state when the care recipient getting out of the bed and the transition probability to each hidden state when the care recipient not getting out of the bed. As a result, there was a difference of about 0.1 in the transition probability of the state related to raising upper body

Body-worn accelerometer-based health assessment algorithms for independent living older adults

The mainstream smart wearable products used for activity trackers have experienced significant growth recently. Among the older population, collecting long periods of activity data in a real-life setting is challenging even with wearable devices. Studies have found inconsistent and lower accuracies when older adults use these smart devices [1], [2],[2],[3]. As a person ages, many have lower daily levels of activity and their dynamic functional patterns, such as gaits and sit-to-stand transitional movements vary throughout the day. This thesis explores wearable health-tracking applications by evaluating daytime and nighttime pattern metrics calculated from continuous accelerometer signals. These signals were collected externally from the upper trunk of the body in an independent-living environment of 30 elderly volunteers. Our gold standard to validate the metrics from the accelerometer signals were similar metrics calculated from an in-home sensor network [4]. This thesis first developed an algorithm to count steps and another algorithm to detect stand-to-sit and sit-to-stand (STS) to demonstrate the importance of considering differences in daily functional health patterns when creating algorithms. Next, this thesis validates that accelerometer data can show similar motion density results as motion sensor data. And thirdly, this thesis proposes an updated vacancy algorithm using a new motion sensor system that detects when no one is in the living space, compared against the current algorithm.Includes bibliographical references (pages 108-111)

Vision-Based 3D Human Motion Analysis for Fall Detection and Bed-Exiting

Fall is one of the most dangerous and costly accidents that threaten health of elderly people, and a large portion of falls occurs when a patient is trying to exit a bed. This thesis proposes two vision-based approaches for general fall detection and bed-exiting detection for elderly people, respectively. The Kinect sensor is chosen as the major monitoring device. The first approach exploits the Kinect sensor with its Windows SDK to detect fall activities. The recorded spatial coordinates of the human body joints from Kinect\u27s 3D skeletal view are processed to extract posture features. Then the principle component analysis and k-means clustering algorithms are applied for dimensionality reduction, vector quantization and feature translation. HMMs are well known for their application in temporal pattern recognition, thus they are chosen for this project to classify human motion which is a temporal sequence of postures. HMMs are trained by the labelled extracted features to model and discriminate four fall motion classes and three non-fall classes. The second approach utilizes segmented motion history image (MHI) sequences to extract space-temporal features of a moving human body. Eight Hu image moments are calculated to translate the space-temporal features of each frame into vectors to describe video frames. The k-means clustering and HMM modelling are utilized for vector quantization and classification between bed-exiting activities and rolling-on-bed activities. In addition, likelihood probability curves are generated along the time line of all MHIs, endeavoring to predict a bed-exiting activity. Detailed descriptions of the experiments and result evaluation are documented in this thesis. The experimental results using human subjects verifies the feasibility and effectiveness of the proposed approaches for general fall detection and bed-exiting prediction

Fall prevention intervention technologies: A conceptual framework and survey of the state of the art

In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge.The Royal Society, grant Ref: RG13082

Ambulatory Monitoring Using Passive RFID Technology

Human activity recognition using wearable sensors is a growing field of study in pervasive computing that forms the basis for ubiquitous applications in areas like health care, manufacturing, human computer interaction and sports. A new generation of passive (batteryless) sensors such as sensor enabled RFID (Radio Frequency Identification) tags are creating new prospects for wearable sensor based applications. As passive sensors are lightweight and small, they can be used for unobtrusive monitoring. Furthermore, these sensors are maintenance free as they require no battery. However, recognising activities from passive sensor enabled RFID tags is challenging due to the sparse and noisy nature of the data streams from these sensors because they need to harvest adequate energy for successful operation. Therefore, within this thesis, we propose methods to recognise activities in real time using passive RFID technology by alleviating the adverse effects of sparsity and noise. We mainly consider ambulatory monitoring to facilitate mitigating falls in hospitals and older care settings as our application context. Specifically, three aspects are considered: i) data acquisition from sensor enabled RFID tags; ii) monitoring ambulatory movements using passive sensor enabled RFID tags to recognise activities leading to falls; and iii) detecting falls using a dense deployment of passive RFID tags. A generic middleware architecture and a generic tag ID format to embed sensor data and uniquely identify tag capabilities are proposed to acquire sensor data from passive sensor enabled RFID tags. The characteristics of this middleware are established using experiments with RFID readers and an example application scenario. In the context of ambulatory monitoring using passive sensor enabled RFID tags, first, an algorithm to facilitate the online interpolation of sparse accelerometer data from passive sensor enabled RFID tags is proposed followed by an investigation of features for activity recognition. Secondly, two data stream segmentation methods are proposed that can segment the data stream on possible activity boundaries to mitigate the adverse effects posed by data stream sparsity on segmentation. Thirdly, an algorithm to model the sequential nature considering previous sensor observations for a given time and their class labels to classify a sparse data stream in real time is proposed. Finally, a classification algorithm based on structured prediction is proposed to both segment and classify the sensor data stream simultaneously. The proposed methods are evaluated using four datasets that have been collected from a passive sensor enabled RFID tag with an accelerometer and successful monitoring of ambulatory movements is demonstrated to be possible by employing innovative data stream processing methods, based on machine learning. In order to detect falls, particularly long lie situation, using a dense deployment of passive RFID tags embedded in a carpet, an efficient and scalable machine learning based algorithm is proposed. This algorithm relies only on binary tag observation information. First, it identifies possible fall locations using heuristics and then the falls are identified using machine learning from features extracted considering possible fall locations alone. From an evaluation, it is demonstrated that the proposed algorithm could successfully identify falls in real time.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

Activity Analysis from Smart Bed Strain Gauge Data

Užití automatizace a dat je nedílnou součástí v současném průmyslu, včetně toho zdravotnického. Vzrůstající popularita umělé inteligence umožnila vytváření pomocných nástrojů, které zvyšují kvalitu diagnóz a péče o pacienty. Jedno z využívaných „chytrých“ zařízení je nemocniční lůžko poskytující data o pacientovi pro vyhodnocování různých statistik. Tato práce se zaměřuje na vizualizaci a detekci poloh v reálném čase použitím čtyř tenzometrů vestavěných v konstrukci postele. Pro tyto účely byl vytvořen vlastní software na extrakci a zpracování dat. Experiment pro detekci poloh s modelem algoritmu SVM ukázal uspokojivé výsledky i při učení klasifikačního modelu pouze na jednom subjektu. Model byl schopen v reálném čase rozpoznat polohy čtyř cizích subjektů různých vah a konstitucí. Experimenty byly uskutečněny v laboratoři v CIIRCu a jejich průběh byl zaznamenán na video přiložené k práci.Use of automation and data appears in most industries and branches including healthcare. The rising popularity of AI paved the way for creation of tools that help improving the quality of diagnosis and care. One of these “smart” gadgets is a hospital bed that provides data for evaluation of the patient's statistics. This thesis focuses on real-time visualization and posture detection using four strain gauges built within the bed's construction. For this purpose, it was necessary to implement a respective data processing software for data extraction. A conducted experiment with the SVM-trained model showed that despite being trained on only one subject, the model was able to sufficiently detect postures of four foreign subjects of different weights and constructions. All the experiments were held and recorded in the laboratory of CIIRC and corresponding demonstrative video can be found on CD attached to this thesis