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

Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach

Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth

A Review of Wearable Sensor Systems to Monitor Plantar Loading in the Assessment of Diabetic Foot Ulcers

Diabetes is highly prevalent throughout the world and imposes a high economic cost on countries at all income levels. Foot ulceration is one devastating consequence of diabetes, which can lead to amputation and mortality. Clinical assessment of diabetic foot ulcer (DFU) is currently subjective and limited, impeding effective diagnosis, treatment and prevention. Studies have shown that pressure and shear stress at the plantar surface of the foot plays an important role in the development of DFUs. Quantification of these could provide an improved means of assessment of the risk of developing DFUs. However, commercially-available sensing technology can only measure plantar pressures, neglecting shear stresses and thus limiting their clinical utility. Research into new sensor systems which can measure both plantar pressure and shear stresses are thus critical. Our aim in this paper is to provide the reader with an overview of recent advances in plantar pressure and stress sensing and offer insights into future needs in this critical area of healthcare. Firstly, we use current clinical understanding as the basis to define requirements for wearable sensor systems capable of assessing DFU. Secondly, we review the fundamental sensing technologies employed in this field and investigate the capabilities of the resultant wearable systems, including both commercial and research-grade equipment. Finally, we discuss research trends, ongoing challenges and future opportunities for improved sensing technologies to monitor plantar loading in the diabetic foot