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

Using Type-2 Fuzzy Models to Detect Fall Incidents and Abnormal Gaits Among Elderly

— June 2012, 11% of the overall population in Taiwan was over the age of 65. This ratio is higher than the average figure for the United Nations (8%) . Critical issues concerning elderly in healthcare include fall detection, loneliness prevention and retard of obliviousness. In this study we design type-2 fuzzy models that utilize smart phone tri-axial accelerometer signals to detect fall incidents and identify abnormal gaits among elderly. Once a fall incident is detected an alarm is sent to notify the medical staff for taking any necessary treatment. When the proposed system is used as a pedometer, all the tri-axial accelerometer signals are used to identify the gaits during walking. Based on the proposed type-2 fuzzy models, the walking gaits can be identified as normal, left-tilted, and right-tilted. Experimental results from type-2 fuzzy models reveal that the accuracy rates in identifying normal walking and fall over are 92.3% and 100%, respectively, exceeding what are obtained using type-1 fuzzy models

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

Gait Balance Detection and Analysis Using Smart-phone Application

This poster describes the components of a gait analysis framework in a smart-phone App. Gait detection is a major concern problem in the field of biomedical Engineering. This present work deals with gait detection using the help of the following devices: a 3-axial accelerometer sensor, an Arduino uno kit a GPS-GSM module, and a buzzer. The sensor analyzes the data stream coming from a (Microelectromechanical systems) MEMS tri-axial accelerometer to infer fall occurrences and to evaluate the gait quality. With the signals obtained, a new automated approach is developed for classifying (diagnosing) locomotive patients using features that may be extracted from their gait signal. For the analysis of different patterns, we use MATLAB. Arduino uno kit is an open source electronics prototyping platform based on flexible and easy-to-use hardware and software modules. The accelerometer helps in tracking human motion and it plays a vital role in gait detection. On the smart-phone side, the application is made of four major components: Background Service, Classification Engine, Notification System and Graphical User Interface. This is a research work in-progress where we are trying to also analyze and process the gait signals from PhysioNet’s Gait in Neurodegenerative disorder public database that includes various kinds of diseases such as Parkinson’s disease, Huntington’s disease, Amyotrophic Lateral Sclerosis and Healthy control. With the signals obtained, in this study, we try to develop a new automated approach for classifying (diagnosing) locomotive patients using features that may be extracted from their gait signal

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

Trialling a Personal Falls Monitoring System using Smart Phone

Copyright © 2015, Australian Computer Society, Inc. This paper appeared at the 8th Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2015), Sydney, Australia, January 2015. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 164, Anthony Maeder and Jim Warren, Ed. Reproduction for academic, not-for-profit purposes permitted provided this text is included.This paper describes a personal falls monitoring project using smart phone based tri-axial accelerometry, for surveillance of elderly people with falls risk living independently at home. The project relied on collaboration of three parties to achieve its clinical, research and technology aims. The results of data collection during the six month trial period are presented and analysed here. These results indicate a very high rate of false positives (94.7%) which would need to be addressed in future development of the system