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

Performance testing of a low power consumption wireless sensor communication system integrated with an energy harvesting power source

This paper presents the performance testing results of a wireless sensor communication system with low power consumption integrated with a vibration energy harvesting power source. The experiments focus on the system’s capability to perform continuous monitoring and to wirelessly transmit the data acquired from the sensors to a user base station, completely battery-free. Energy harvesting technologies together with system design optimisation for power consumption minimisation ensure the system’s energy autonomous capability demonstrated in this paper by presenting the promising testing results achieved following its integration with Structural Health Monitoring (SHM) and Body Area Network (BAN) applications

Bioengineered Textiles and Nonwovens – the convergence of bio-miniaturisation and electroactive conductive polymers for assistive healthcare, portable power and design-led wearable technology

Today, there is an opportunity to bring together creative design activities to exploit the responsive and adaptive ‘smart’ materials that are a result of rapid development in electro, photo active polymers or OFEDs (organic thin film electronic devices), bio-responsive hydrogels, integrated into MEMS/NEMS devices and systems respectively. Some of these integrated systems are summarised in this paper, highlighting their use to create enhanced functionality in textiles, fabrics and non-woven large area thin films. By understanding the characteristics and properties of OFEDs and bio polymers and how they can be transformed into implementable physical forms, innovative products and services can be developed, with wide implications. The paper outlines some of these opportunities and applications, in particular, an ambient living platform, dealing with human centred needs, of people at work, people at home and people at play. The innovative design affords the accelerated development of intelligent materials (interactive, responsive and adaptive) for a new product & service design landscape, encompassing assistive healthcare (smart bandages and digital theranostics), ambient living, renewable energy (organic PV and solar textiles), interactive consumer products, interactive personal & beauty care (e-Scent) and a more intelligent built environment

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure

Heart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge

Wearable Platform for Automatic Recognition of Parkinson Disease by Muscular Implication Monitoring

The need for diagnostic tools for the characterization of progressive movement disorders - as the Parkinson Disease (PD) - aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results

The future design direction of smart clothing development

Literature indicates that Smart Clothing applications, the next generation of clothing and electronic products, have been struggling to enter the mass market because the consumers’ latent needs have not been recognised. Moreover, the design direction of Smart Clothes remains unclear and unfocused. Nevertheless, a clear design direction is necessary for all product development. Therefore, this research aims to identify the design directions of the emerging Smart Clothes industry by conducting a questionnaire survey and focus groups with its major design contributors. The results reveal that the current strategy of embedding a wide range of electronic functions in a garment is not suitable. This is primarily because it does not match the users’ requirements, purchasing criteria and lifestyle. The results highlight the respondents’ preference for personal healthcare and sportswear applications that suit their lifestyle, are aesthetically attractive, and provide a practical function