A cane-based low cost sensor to implement attention mechanisms in telecare robots

Telepresence robots have been recently used for Comprehensive Geriatric Assessment (CGA). Since the robot can not track a person continuously, there are several strategies to decide when to check them, from cyclic checks to simple requests from users and/or caregivers. In order to adapt to the user needs and condition, it is preferable to perform CGA as soon as regularities appear. However, this requires detection of potential issues in users to offer immediate service. In this work we propose a new low cost force sensor system to detect user’s condition and attract attention of CGA robots, so they can perform a full examination on a need basis. The main advantages of this system are: i) it can be attached to any standard commercial cane; ii) its power consumption is very reduced; and iii) it provides continuous information as long as the user walks. It has been tested with several elderly volunteers in care facilities. Results have proven that the sensor readings are indeed correlated with the users’ condition.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

DEVELOPMENT OF A TELEREHABILITATION SOLUTION FOR REMOTE MONITORING AND CARE

Ph.DDOCTOR OF PHILOSOPH

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area

IMUs: validation, gait analysis and system’s implementation

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a prevalent problem in actual society. The number of falls has been increasing greatly in the last fifteen years. Some falls result in injuries and the cost associated with their treatment is high. However, this is a complex problem that requires several steps in order to be tackled. Namely, it is crucial to develop strategies that recognize the mode of locomotion, indicating the state of the subject in various situations, namely normal gait, step before fall (pre-fall) and fall situation. Thus, this thesis aims to develop a strategy capable of identifying these situations based on a wearable system that collects information and analyses the human gait. The strategy consists, essentially, in the construction and use of Associative Skill Memories (ASMs) as tools for recognizing the locomotion modes. Consequently, at an early stage, the capabilities of the ASMs for the different modes of locomotion were studied. Then, a classifier was developed based on a set of ASMs. Posteriorly, a neural network classifier based on deep learning was used to classify, in a similar way, the same modes of locomotion. Deep learning is a technique actually widely used in data classification. These classifiers were implemented and compared, providing for a tool with a good accuracy in recognizing the modes of locomotion. In order to implement this strategy, it was previously necessary to carry out extremely important support work. An inertial measurement units’ (IMUs) system was chosen due to its extreme potential to monitor outpatient activities in the home environment. This system, which combines inertial and magnetic sensors and is able to perform the monitoring of gait parameters in real time, was validated and calibrated. Posteriorly, this system was used to collect data from healthy subjects that mimicked Fs. Results have shown that the accuracy of the classifiers was quite acceptable, and the neural networks based classifier presented the best results with 92.71% of accuracy. As future work, it is proposed to apply these strategies in real time in order to avoid the occurrence of falls.As quedas são um problema predominante na sociedade atual. O número de quedas tem aumentado bastante nos últimos quinze anos. Algumas quedas resultam em lesões e o custo associado ao seu tratamento é alto. No entanto, trata-se de um problema complexo que requer várias etapas a serem abordadas. Ou seja, é crucial desenvolver estratégias que reconheçam o modo de locomoção, indicando o estado do sujeito em várias situações, nomeadamente, marcha normal, passo antes da queda (pré-queda) e situação de queda. Assim, esta tese tem como objetivo desenvolver uma estratégia capaz de identificar essas situações com base num sistema wearable que colete informações e analise a marcha humana. A estratégia consiste, essencialmente, na construção e utilização de Associative Skill Memories (ASMs) como ferramenta para reconhecimento dos modos de locomoção. Consequentemente, numa fase inicial, foram estudadas as capacidades das ASMs para os diferentes modos de locomoção. Depois, foi desenvolvido um classificador baseado em ASMs. Posteriormente, um classificador de redes neuronais baseado em deep learning foi utilizado para classificar, de forma semelhante, os mesmos modos de locomoção. Deep learning é uma técnica bastante utilizada em classificação de dados. Estes classificadores foram implementados e comparados, fornecendo a uma ferramenta com uma boa precisão no reconhecimento dos modos de locomoção. Para implementar esta estratégia, era necessário realizar previamente um trabalho de suporte extremamente importante. Um sistema de unidades de medição inercial (IMUs), foi escolhido devido ao seu potencial extremo para monitorizar as atividades ambulatórias no ambiente domiciliar. Este sistema que combina sensores inerciais e magnéticos e é capaz de efetuar a monitorização de parâmetros da marcha em tempo real, foi validado e calibrado. Posteriormente, este Sistema foi usado para adquirir dados da marcha de indivíduos saudáveis que imitiram quedas. Os resultados mostraram que a precisão dos classificadores foi bastante aceitável e o classificador baseado em redes neuronais apresentou os melhores resultados com 92.71% de precisão. Como trabalho futuro, propõe-se a aplicação destas estratégias em tempo real de forma a evitar a ocorrência de quedas

Intelligent technologies for the aging brain: opportunities and challenges

Intelligent computing is rapidly reshaping healthcare. In light of the global burden of population aging and neurological disorders, dementia and elderly care are among the healthcare sectors that are most likely to benefit from this technological revolution. Trends in artificial intelligence, robotics, ubiquitous computing, neurotechnology and other branches of biomedical engineering are progressively enabling novel opportunities for technology-enhanced care. These Intelligent Assistive Technologies (IATs) open the prospects of supporting older adults with neurocognitive disabilities, maintain their independence, reduce the burden on caregivers and delay the need for long-term care (1, 2). While technology develops fast, yet little knowledge is available to patients and health professionals about the current availability, applicability, and capability of existing IATs. This thesis proposes a state-of-the-art analysis of IATs in dementia and elderly care. Our findings indicate that advances in intelligent technology are resulting in a rapidly expanding number and variety of assistive solutions for older adults and people with neurocognitive disabilities. However, our analysis identifies a number of challenges that negatively affect the optimal deployment and uptake of IATs among target users and care institutions. These include design issues, sub-optimal approaches to product development, translational barriers between lab and clinics, lack of adequate validation and implementation, as well as data security and cyber-risk weaknesses. Additionally, in virtue of their technological novelty, intelligent technologies raise a number of Ethical, Legal and Social Implications (ELSI). Therefore, a significant portion of this thesis is devoted to providing an early ethical Technology Assessment (eTA) of intelligent technology, hence contributing to preparing the terrain for its safe and ethically responsible adoption. This assessment is primarily focused on intelligent technologies at the human-machine interface, as these applications enable an unprecedented exposure of the intimate dimension of individuals to the digital infosphere. Issues of privacy, integrity, equality, and dual-use were addressed at the level of stakeholder analysis, normative ethics and human-rights law. Finally, this thesis is aimed at providing evidence-based recommendations for guiding participatory and responsible development in intelligent technology, and delineating governance strategies that maximize the clinical benefits of IATs for the aging world, while minimizing unintended risks

Are Behaviour Change Approaches Incorporated Within Digital Technology-Based Physical Rehabilitation Interventions Following Stroke? A Scoping Review

Background: Digital health technologies (DHTs) are increasingly used in physical stroke rehabilitation to support individuals to successfully engage with the frequent, intensive, and lengthy activities required to optimise recovery. Despite this, little is known about behaviour change within these interventions. Objective: This scoping review aimed to identify if, and how behaviour change approaches (i.e., theories, models and frameworks, and techniques to influence behaviour) are incorporated within physical stroke rehabilitation interventions that include a DHT. Methods: Databases (Embase, Medline, PyscINFO, CINAHL, Cochrane Library and AMED) were searched using keywords relating to behaviour change, DHT, physical rehabilitation and stroke. Results were independently screened by 2 reviewers. Sources were included if they reported a completed primary research study in which a behaviour change approach could be identified within a physical stroke rehabilitation intervention that included a DHT. Data including the study design, DHT utilised, and behaviour change approaches were charted. Specific behaviour change techniques were coded to the behaviour change technique taxonomy version 1 (BCTTv1). Results: From a total of 1973 identified sources, 103 studies (5%) were included for data charting. The most common reason for exclusion at full text screening, was the absence of an explicit approach to behaviour change (165/245, 67%). Almost half of the included studies (45/103, 44%) were described as pilot or feasibility studies. Virtual reality (VR) was the most frequently identified DHT type (58/103, 56%) and almost two-thirds of studies focused on upper limb rehabilitation (65/103, 63%). Only a limited number of studies (18/103, 17%) included a theory, model, or framework for behaviour change. The most frequently used BCTTv1 clusters were feedback and monitoring (88/103, 85%), reward and threat (56, 54%), goals and planning (33, 32%), and shaping knowledge (33, 32%). Relationships between feedback and monitoring, and reward and threat were identified using a relationship map with prominent use of both these clusters in interventions which included VR. Conclusions: Despite an assumption that DHTs can promote engagement in rehabilitation, this scoping review demonstrates that very few studies of physical stroke rehabilitation which include a DHT, overtly utilised any form of behaviour change approach. From those studies that did consider behaviour change, most did not report robust underpinning theory. Future development and research need to explicitly articulate how including DHTs within an intervention may support the behaviour change required for optimal engagement in physical rehabilitation following stroke, as well as establish their effectiveness. This understanding is likely to support the realisation of the transformative potential of DHTs in stroke rehabilitation