A probabilistic approach to learn activities of daily living of a mobility aid device user

© 2014 IEEE. The problem of inferring human behaviour is naturally complex: people interact with the environment and each other in many different ways, and dealing with the often incomplete and uncertain sensed data by which the actions are perceived only compounds the difficulty of the problem. In this paper, we propose a framework whereby these elaborate behaviours can be naturally simplified by decomposing them into smaller activities, whose temporal dependencies can be more efficiently represented via probabilistic hierarchical learning models. In this regard, patterns of a number of activities typically carried out by users of an ambulatory aid device have been identified with the aid of a Hierarchical Hidden Markov Model (HHMM) framework. By decomposing the complex behaviours into multiple layers of abstraction the approach is shown capable of modelling and learning these tightly coupled human-machine interactions. The inference accuracy of the proposed model is proven to compare favourably against more traditional discriminative models, as well as other compatible generative strategies to provide a complete picture that highlights the benefits of the proposed approach, and opens the door to more intelligent assistance with a robotic mobility aid

A probabilistic model for assistive robotics devices to support activities of daily living

University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis explores probabilistic techniques to model interactions between humans and robotic devices. The work is motivated by the rapid increase in the ageing population and the role that assistive robotic devices can play in maintaining independence and quality of life as assistants and/or companions for these communities. While there are substantial social and ethical implications in this pursuit, it is advocated that robotic systems are bound to acquire more sophisticated assistive capabilities if they are to operate in unstructured, dynamic, human-centred environments, responsive to the needs of their human operators. Such cognitive assistive systems postulate advances along the complete processing pipeline, from sensing, to anticipating user actions and environmental changes, and to delivering natural supportive actuation. Within the boundaries of the human-robot interaction context, it can be expected that acute awareness of human intentions plays a key role in delivering practical assistive actions. This work is thereby focused on the human behaviours likely to result from merging sensed human-robot interactions and the learning gained from past experiences, proposing a framework that facilitates the path towards integrating tightly knit human-robot interaction models. Human behaviour is complex in nature and interactions with the environment and other objects occur in different and unpredictable ways. Moreover, observed sensory data is often incomplete and noisy. Inferring human intention is thus a challenging problem. This work defends the thesis that in many real-world scenarios these complex behaviours can be naturally simplified by decomposing them into smaller activities, so that their temporal dependencies can be learned more efficiently with the aid of probabilistic hierarchical models. To that end, a strategy is devised in the first part of the thesis to efficiently represent human Activities of Daily Living, or ADLs, by decomposing them into a flexible semantic structure of “Action Primitives” (APs), atomic actions which are proven able to encapsulate complex activities when combined within a temporal probabilistic framework at multiple levels of abstraction. A Hierarchical Hidden Markov Model (HHMM) is proposed as a powerful tool capable of modelling and learning these complex and uncertain human behaviours using knowledge gained from past interactions. The ADLs performed by humans consist of a variety of complex locomotion-related tasks, as well as activities that involve grasping and manipulation of objects used in everyday life. Two widely used devices that provide assistance to users with mobility impairments while carrying out their ADLs, a power walker and a robotic wheelchair, are instrumented and used to model patterns of navigational activities (i.e. visiting location of interest), as well as some additional platform-specific support activities (e.g. standing up using the support of assistive walker). Human indications while performing these activities are captured using low-level sensing fitted on the mobility devices (e.g. strain gauges, laser range finders). Grasping and manipulations related ADLs are modelled using data captured from a stream of video images, where data comprises of hand-object interactions and their motion in 3D space. The inference accuracy of the proposed framework in predicting APs and recognising long term user intentions is compared with traditional discriminative models (sequential Support Vector Machines (SVM)), other generative models (layered Dynamic Bayesian Networks (DBN)), and combinations thereof, to provide a complete picture that highlights the benefits of the proposed approach. Results from real data collected from a set of trials conducted by actor users demonstrate that all techniques are able to predict APs with good accuracies, yet successful inference of long term tasks is substantially reduced in the case of the layered DBN and SVM models. These findings validate the thesis’ proposal that the combination of decomposing tasks at multiple levels and exploiting their inherent temporal nature plays a critical role in predicting complex interactive tasks

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Probabilistic models versus discriminate classifiers for human activity recognition with an instrumented mobility-assistance aid

Detection of individuals' intentions and actions from a stream of human behaviour is an open and complex problem. There is however an intrinsic need to automatically recognise the activities performed by users of mobility assistive aids to better understand their behavioural patterns, with the ultimate objective of improving the utility of these devices. While discriminative algorithms such as Support Vector Machines (SVM) are well understood, generative probabilistic approaches to machine learning such as Dynamic Bayesian Networks (DBN) have only recently started gaining increasing interest within the robotics community. In this paper, a comprehensive evaluation of these techniques is carried out for human activity recognition in the context of their applicability to assistive robotics. Results show comparable recognition rates, offering valuable insights into the advantageous characteristics of DBN in relation to their dynamic and unsupervised nature for realistic human-robot interaction modelling

Acceptance of ambient assisted living (AAL) technologies among older Australians : a review of barriers in user experience

One of the great challenges facing Australian society is that of an ageing population. Amongst the issues involved in this drastic demographic change, the most significant aspect is the demand for older Australians to live independently at home. The development of Ambient Assisted Living (AAL) technologies aims to address this issue. The advancement of AAL applications have been done to support the users with their daily-life activities and health concerns by providing increased mobility, security, safety in emergencies, health-monitoring, improved lifestyle, and fall-detection through the use of sensors. However, the optimum uptake of these technologies among the end-users (the elderly Australians) still remains a big concern. Thus, there is an elevated need to understand the needs and preferences of the seniors in order to improve the acceptance of AAL applications. The aim of this study is to investigate the barriers and perceptions in the use of AAL applications amongst older Australians. Focus groups and quantitative surveys have been conducted to provide a detailed analysis of these impediments. The results show that there are different factors that restrict the use of these technologies along with the fact that elderly people have certain preferences when using them. An understanding of these factors has been gained and suggestions have been made to increase the acceptance of AAL devices. This work gives useful insights towards the design of AAL solutions according to user needs

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

Patients Monitoring System based on a Wireless Sensor Network Adaptive Platform

Guaranteeing ubiquity and appropriateness of health services provision to the users constitutes a priority issue for the Public Health Authorities. This paper presents an innovative Wireless Personal Area Network architecture that takes advantage of some of the features provided by Intelligent Environments -large number of devices, heterogeneous networks and mobility enhancement- in order to adapt and personalise ambient conditions to the user profile

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars