A Fall and Near-Fall Assessment and Evaluation System

Abstract

The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Ease of use and low power are considered in the design. The system was built and tested successfully. Different machine learning algorithms were applied to the stored data for fall and near-fall evaluation. Results indicate that the Naïve Bayes algorithm is the best choice, due to its fast model building and high accuracy in fall detection.

Assessment of geriatric syndromes and physical function in people living with HIV

“…53 Falls are commonly measured by self-report. With new technological developments in monitors and sensors, falls may be assessed by direct measurement in the future, 54 although the intermittent nature of falls could make measurement challenging. Self-report can be measured by simple recall such as "In the past month, have you had any fall including a slip or trip in which you lost your balance and landed on the floor or ground or lower level" 50 or can be measured by detailed daily fall diaries or calendars.…”

Wearable Inertial Sensors and Their Applications

“…Fall-detection algorithms detect different phases of a fall event: (1) motion prior to impact based on high velocity [46,47] and fast postural changes or free falls [48,49]; and (2) the impact itself based on high acceleration [50,51], a rapid change in acceleration and end posture [52,53], or reduced general activity after the impact [51]. In earlier studies, Kangas et al found that a waist-worn triaxial accelerometer with a simple algorithm was sufficient for fall detection [54,55].…”

Inertial Sensing-Based Pre-Impact Detection of Falls Involving Near-Fall Scenarios

“…However, near-falls (or recoverable imbalances) are common events for many older adults and are clinically relevant markers of falls worthy of further study [25]- [26]. For example, investigators have found that older adults who report multiple near falls (such as missteps or stumbles) are more likely to go on to fall [27].…”