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

Biosignal and context monitoring: Distributed multimedia applications of body area networks in healthcare

We are investigating the use of Body Area Networks (BANs), wearable sensors and wireless communications for measuring, processing, transmission, interpretation and display of biosignals. The goal is to provide telemonitoring and teletreatment services for patients. The remote health professional can view a multimedia display which includes graphical and numerical representation of patients’ biosignals. Addition of feedback-control enables teletreatment services; teletreatment can be delivered to the patient via multiple modalities including tactile, text, auditory and visual. We describe the health BAN and a generic mobile health service platform and two context aware applications. The epilepsy application illustrates processing and interpretation of multi-source, multimedia BAN data. The chronic pain application illustrates multi-modal feedback and treatment, with patients able to view their own biosignals on their handheld device

Wearable Capacitive-based Wrist-worn Gesture Sensing System

Gesture control plays an increasingly significant role in modern human-machine interactions. This paper presents an innovative method of gesture recognition using flexible capacitive pressure sensor attached on user’s wrist towards computer vision and connecting senses on fingers. The method is based on the pressure variations around the wrist when the gesture changes. Flexible and ultrathin capacitive pressure sensors are deployed to capture the pressure variations. The embedding of sensors on a flexible substrate and obtain the relevant capacitance require a reliable approach based on a microcontroller to measure a small change of capacitive sensor. This paper is addressing these challenges, collect and process the measured capacitance values through a developed programming on LabVIEW to reconstruct the gesture on computer. Compared to the conventional approaches, the wrist-worn sensing method offerings a low-cost, lightweight and wearable prototype on the user’s body. The experimental result shows that the potentiality and benefits of this approach and confirms that accuracy and number of recognizable gestures can be improved by increasing number of sensor

Designing Auditory Feedback from Wearable Weightlifting Devices

While wearable devices for fitness have gained broad popularity, most are focused on tracking general activity types rather than correcting exercise forms, which is extremely important for weightlifters. We interviewed 7 frequent gym-goers about their opinions and expectations for feedback from wearable devices for weightlifting. We describe their desired feedback, and how their expectations and concerns could be balanced in future wearable fitness technologies

A review of contemporary techniques for measuring ergonomic wear comfort of protective and sport clothing

Protective and sport clothing is governed by protection requirements, performance, and comfort of the user. The comfort and impact performance of protective and sport clothing are typically subjectively measured, and this is a multifactorial and dynamic process. The aim of this review paper is to review the contemporary methodologies and approaches for measuring ergonomic wear comfort, including objective and subjective techniques. Special emphasis is given to the discussion of different methods, such as objective techniques, subjective techniques, and a combination of techniques, as well as a new biomechanical approach called modeling of skin. Literature indicates that there are four main techniques to measure wear comfort: subjective evaluation, objective measurements, a combination of subjective and objective techniques, and computer modeling of human–textile interaction. In objective measurement methods, the repeatability of results is excellent, and quantified results are obtained, but in some cases, such quantified results are quite different from the real perception of human comfort. Studies indicate that subjective analysis of comfort is less reliable than objective analysis because human subjects vary among themselves. Therefore, it can be concluded that a combination of objective and subjective measuring techniques could be the valid approach to model the comfort of textile materials

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn)

This paper presents the application of neural networks for analysing voltage stability of load buses in electric power system. Voltage stability margin (VSM) and load power margin (LPM) are used as the indicators for analysing voltage stability. The neural networks used in this research are divided into two types. The first type is using the neural network to predict the values of VSM and LPM. Multilayer perceptron back propagation (MLPBP) neural network and adaptive neuro-fuzzy inference system (ANFIS) will be used. The second type is to classify the values of VSM and LPM using the probabilistic neural network (PNN). The IEEE 30-bus system has been chosen as the reference electrical power system. All of the neural network-based models used in this research is developed using MATLAB