Multimodal Analytics for Healthcare

The ailing healthcare system demands effective autonomous solutions to improve service and provide individualize care, while reducing the burden on the scarce healthcare workforce. Most of these solutions require a multidisciplinary approach that combines healthcare with computational abilities. The work presented in this thesis introduces a multimodal multiview network along with methods and solutions that leverage inexpensive visual sensors and computers to monitor healthcare. One of the most prominent outcomes of this work includes enabling the medical analysis of ICU conditions such as sleep disorders, decubitus ulcerations, and hospital acquired infections, which are preventable and negatively affect patients' health population. The problems tackled include patient pose classification, pose motion analysis and summarization, role representation and identification, and activity and event logging in natural hospital settings. These problems are addressed via a non-intrusive non-disruptive multimodal multiview sensor network (Medical Internet-of-Things). The multimodal data is combined with coupled-optimization to estimate source weights and accurately classify patient poses. Pose patterns such as pose transitions are represented using deep convolutional features and pose duration is modelled via segments. The proposed techniques serve to differentiate between poses and pseudo-poses (transitory poses) and create effective motion summaries. The role representation is tackled using novel appearance and semantic interaction maps to assign generic labels to individuals (doctor, nurse, visitor, etc) without using identifiable information (e.g., facetracking or badges), which is prohibited in healthcare applications. Finally, activity and event analysis is tackled using a new contextual aspect frames where aspect bases and weights are learned and then used to reconstruct activities. The objective of this thesis is to enable the development, evaluation, and optimization of individualized therapies, standards-of-care, room infrastructural designs, and clinical workflows and procedures

Under the Cover Infant Pose Estimation using Multimodal Data

Infant pose monitoring during sleep has multiple applications in both healthcare and home settings. In a healthcare setting, pose detection can be used for region of interest detection and movement detection for noncontact based monitoring systems. In a home setting, pose detection can be used to detect sleep positions which has shown to have a strong influence on multiple health factors. However, pose monitoring during sleep is challenging due to heavy occlusions from blanket coverings and low lighting. To address this, we present a novel dataset, Simultaneously-collected multimodal Mannequin Lying pose (SMaL) dataset, for under the cover infant pose estimation. We collect depth and pressure imagery of an infant mannequin in different poses under various cover conditions. We successfully infer full body pose under the cover by training state-of-art pose estimation methods and leveraging existing multimodal adult pose datasets for transfer learning. We demonstrate a hierarchical pretraining strategy for transformer-based models to significantly improve performance on our dataset. Our best performing model was able to detect joints under the cover within 25mm 86% of the time with an overall mean error of 16.9mm. Data, code and models publicly available at https://github.com/DanielKyr/SMa

Modeling Humans at Rest with Applications to Robot Assistance

Humans spend a large part of their lives resting. Machine perception of this class of body poses would be beneficial to numerous applications, but it is complicated by line-of-sight occlusion from bedding. Pressure sensing mats are a promising alternative, but data is challenging to collect at scale. To overcome this, we use modern physics engines to simulate bodies resting on a soft bed with a pressure sensing mat. This method can efficiently generate data at scale for training deep neural networks. We present a deep model trained on this data that infers 3D human pose and body shape from a pressure image, and show that it transfers well to real world data. We also present a model that infers pose, shape and contact pressure from a depth image facing the person in bed, and it does so in the presence of blankets. This model similarly benefits from synthetic data, which is created by simulating blankets on the bodies in bed. We evaluate this model on real world data and compare it to an existing method that requires RGB, depth, thermal and pressure imagery in the input. Our model only requires an input depth image, yet it is 12% more accurate. Our methods are relevant to applications in healthcare, including patient acuity monitoring and pressure injury prevention. We demonstrate this work in the context of robotic caregiving assistance, by using it to control a robot to move to locations on a person’s body in bed.Ph.D

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

Human fall detection methodologies : from machine learning using acted data to fall modelling using myoskeletal simulation

Human Fall Detection is a research area with interest from many disciplines and aims to perform for many assisted-living monitoring applications to promptly identify life-threatening situations. A fall occurs when a person is unable to maintain balance due to a variety of issues; physical; mental or environmental. The accurate detection of the fall is crucial as a missed detection can be fatal. Variability of human physiological characteristics is currently unstudied as to the impact on a fall detector's performance as young adults and elderly are expected to fall differently. Another important issue is the scene occlusions. In the use of visual sensors, an occluded fall is treated as a missed detection as the whereabouts of the person is unknown when occluded. Finally, current studies are based on acted fall datasets on which algorithms are trained. These dataset are unrepresentative of real fall events and illustrate the events without occlusions or other scene in uences. Several fall detection algorithms were developed during the study aiming to achieve accuracy in detection falls while fall-like actions such as lying down remain undetected. Human fall datasets were used for training and testing purposes of A machine learning algorithm using data from depth cameras which captured the fall events from different views. A new pathway was introduced tackling the issues of availability issues of data-driven machine learning approaches which was achieved with the use of simulation data. The use of myoskeletal simulation was then selected as a closer representation of the human body in terms of structure and behaviour. With the use of a simulation model, a personalised estimation of the fall event can be achieved as it is parametrised on a physical characteristic such as the height of the falling person. Alternative technologies such as accelerometers have been used for fall detection to prove the validity of this approach on other modalities. A study regarding the impact of occlusions for fall detection which is one of the issues not properly investigated in current work is proposed and examined. Synthetic occlusions were added to existing depth data from publicly available datasets. The research methodologies were evaluated using the most representative depth video and accelerometer data from existing datasets, as well as YouTube videos of real-fall events. The machine learning methodologies achieved good results on similar body variability datasets. A discussion regarding the proof of concept of the simulation-based approach for fall modelling is mentioned given the comparative results against existing methodologies which achieves better than any existing work evaluated against known datasets. The simulation approach is also evaluated against occluded fall and non-fall event data, proving the further robustness of the approach. This platform can be expanded to analyse any type of fall, or body posture (e.g. elderly), without the use of humans to performs fall events

Characterizing Unstructured Motor Behaviors in the Epilepsy Monitoring Unit

Key advancements in recording hardware, data computation, clinical care, and cognitive science continue to drive new possibilities in how humans and machines can interact directly through thought. Neural data analyses with these advancements has progressed neuroscience research in functional brain mapping and brain-computer interfaces (BCIs). Much of our knowledge about BCIs is informed by data collected through carefully controlled experiments. Constraining BCI experiments with structured paradigms allows researchers to collect a high number of consistent data in a short amount of time, while also controlling for external confounds. Very little is currently known about how well these task-based relationships extend to daily life, in part because collecting data outside of the lab is challenging. To further understand natural brain activity, we must study more complex behaviors in more environmentally relevant settings. The results of this dissertation address three general challenges to studying neural correlates to unstructured behaviors. First, we continuously monitored unstructured human movements in the epilepsy monitoring unit using a video sensor synchronized to clinical intracortical electrodes. Second, we annotated unstructured behaviors from these video using both manual and computer vision methods. Finally, analyzed neural features with respect to unstructured human movements, and evaluated the performance of features identified in previous task-based studies. The preliminary nature of this work means that a majority of our demonstrations are whether the continuous paradigm can be leveraged, how one might go about leveraging it, and evaluations that tie our results back to earlier task-based studies. Our advances here motivate future works that focus more intently on what types of behaviors and neural signal features to explore

Towards Object-Centric Scene Understanding

Visual perception for autonomous agents continues to attract community attention due to the disruptive technologies and the wide applicability of such solutions. Autonomous Driving (AD), a major application in this domain, promises to revolutionize our approach to mobility while bringing critical advantages in limiting accident fatalities. Fueled by recent advances in Deep Learning (DL), more computer vision tasks are being addressed using a learning paradigm. Deep Neural Networks (DNNs) succeeded consistently in pushing performances to unprecedented levels and demonstrating the ability of such approaches to generalize to an increasing number of difficult problems, such as 3D vision tasks. In this thesis, we address two main challenges arising from the current approaches. Namely, the computational complexity of multi-task pipelines, and the increasing need for manual annotations. On the one hand, AD systems need to perceive the surrounding environment on different levels of detail and, subsequently, take timely actions. This multitasking further limits the time available for each perception task. On the other hand, the need for universal generalization of such systems to massively diverse situations requires the use of large-scale datasets covering long-tailed cases. Such requirement renders the use of traditional supervised approaches, despite the data readily available in the AD domain, unsustainable in terms of annotation costs, especially for 3D tasks. Driven by the AD environment nature and the complexity dominated (unlike indoor scenes) by the presence of other scene elements (mainly cars and pedestrians) we focus on the above-mentioned challenges in object-centric tasks. We, then, situate our contributions appropriately in fast-paced literature, while supporting our claims with extensive experimental analysis leveraging up-to-date state-of-the-art results and community-adopted benchmarks

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio