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

Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling

In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods

A 'one-size-fits-most' walking recognition method for smartphones, smartwatches, and wearable accelerometers

The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using 'activity counts,' a measure which overlooks specific types of physical activities. We proposed a walking recognition method for sub-second tri-axial accelerometer data, in which activity classification is based on the inherent features of walking: intensity, periodicity, and duration. We validated our method against 20 publicly available, annotated datasets on walking activity data collected at various body locations (thigh, waist, chest, arm, wrist). We demonstrated that our method can estimate walking periods with high sensitivity and specificity: average sensitivity ranged between 0.92 and 0.97 across various body locations, and average specificity for common daily activities was typically above 0.95. We also assessed the method's algorithmic fairness to demographic and anthropometric variables and measurement contexts (body location, environment). Finally, we have released our method as open-source software in MATLAB and Python.Comment: 39 pages, 4 figures (incl. 1 supplementary), and 5 tables (incl. 2 supplementary

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